* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Success. Updated the following files:
A research-profile/personal-context-note.m / completed / call_BERSFeCRuEhQSUHHq59WhYk

Draft:
I’m going to save a compact distilled note in the workspace next to the CV materials, so it’s easy for me to reuse later and easy to migrate into the native/module setup.Done


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- Success. Updated the following files:
A research-profile/personal-context-note.m / completed / call_BERSFeCRuEhQSUHHq59WhYk

Draft:
I’m going to save a compact distilled note in the workspace next to the CV materials, so it’s easy for me to reuse later and easy to migrate into the native/module setup.Done — I saved it here:

- `/home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md`

I kept it compact and structured so it’s actually useful later:
- durable profile facts
- strategic context
- career situation snapshot
- current energy/emotional state
- trigger links/associations
- practical interpretation notes

So it’s not just a raw dump, and it should be easy to notice, reuse, or split into native modules later.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 685.1s
Time: model 682.0s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 1.5k o 103 cache 238.6k/99% est $0.0052; p≈2.1ktok/8.4kch
Ctx: direct ACP session 237.4k; active 2.9k; cache 234.5k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


I’m going to save a compact distilled note in the workspace next to the CV materials, so it’s easy for me to reuse later and easy to migrate into the native/module setup.Done — I saved it here:

- `/home/matrix-lite/workspace/persistent-chat/research-profile/personal-context-note.md`

I kept it compact and structured so it’s actually useful later:
- durable profile facts
- strategic context
- career situation snapshot
- current energy/emotional state
- trigger links/associations
- practical interpretation notes

So it’s not just a raw dump, and it should be easy to notice, reuse, or split into native modules later.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 686.1s
Time: model 682.0s, overhead 4.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 1.5k o 103 cache 238.6k/99% est $0.0052; p≈2.1ktok/8.4kch
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


Nice thank you. Sorry I'm just too tired and also sick to think of a better place.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.9s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.0ktok/8.2kch
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 72


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
No


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
No need to apologize — that was exactly the right time to use a simple good-enough place. Rest matters more than perfect information architecture right now.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 7.3s
Time: model 4.5s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.3k o 34 cache 238.6k/99% est $0.0087; p≈2.0ktok/8.2kch
Ctx: direct ACP session 240.2k; active 1.6k; cache 238.6k/99%; p≈2.1ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


No need to apologize — that was exactly the right time to use a simple good-enough place. Rest matters more than perfect information architecture right now.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 8.2s
Time: model 4.5s, overhead 3.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.3k o 34 cache 238.6k/99% est $0.0087; p≈2.0ktok/8.2kch
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


I wonder if I should bother with laundry if I'm flying out tomorrow. I could use a few more clean things but too lazy to get up now


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.7s
Time: overhead 1.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈1.9ktok/7.4kch
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 73


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
My


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
My lazy-but-practical answer: **only do it if 1 small load clearly improves the trip**.

Quick test:
- Do you already have enough for the travel day + first 2–3 days?
  - **yes** → skip it
  - **no** → do a minimal emergency load now

So not:
- “optimize all laundry”

but:
- underwear / socks / maybe 1–2 shirts level only

If you’re tired and sick, full laundry is probably not worth it unless tomorrow-you will really suffer for skipping it.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 9.5s
Time: model 6.8s, overhead 1.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 1.9k o 119 cache 241.7k/99% est $0.0066; p≈1.9ktok/7.4kch
Ctx: direct ACP session 241.9k; active 3.3k; cache 238.6k/99%; p≈2.0ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 74


My lazy-but-practical answer: **only do it if 1 small load clearly improves the trip**.

Quick test:
- Do you already have enough for the travel day + first 2–3 days?
  - **yes** → skip it
  - **no** → do a minimal emergency load now

So not:
- “optimize all laundry”

but:
- underwear / socks / maybe 1–2 shirts level only

If you’re tired and sick, full laundry is probably not worth it unless tomorrow-you will really suffer for skipping it.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 10.2s
Time: model 6.8s, overhead 3.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 1.9k o 119 cache 241.7k/99% est $0.0066; p≈1.9ktok/7.4kch
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 22.6%; usable 23.1%; observed turns 71; actual turns 74


Nah, just the jeans and shorts could be fresher, but I can just clean them with a sponge or something


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 74


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 74


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 74


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.0s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.0ktok/7.9kch
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 74


📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Waiting for first model/tool update…


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, then I’d absolutely skip laundry.

If it’s just:
- jeans
- shorts
- “could be fresher”

then sponge / spot clean / air them out is the correct tired-person strategy. Save the full laundry energy for when it actually matters.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 11.9s
Time: model 9.0s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.3k o 58 cache 243.2k/99% est $0.0065; p≈2.0ktok/7.9kch
Ctx: direct ACP session 243.7k; active 2.0k; cache 241.7k/99%; p≈1.9ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 75


Yeah, then I’d absolutely skip laundry.

If it’s just:
- jeans
- shorts
- “could be fresher”

then sponge / spot clean / air them out is the correct tired-person strategy. Save the full laundry energy for when it actually matters.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 12.9s
Time: model 9.0s, overhead 3.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 2.3k o 58 cache 243.2k/99% est $0.0065; p≈2.0ktok/7.9kch
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.2%; usable 23.7%; observed turns 74; actual turns 75


Yeah thanks I'll do just that


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 75


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 75


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 75


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.6s
Time: overhead 1.6s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈1.9ktok/7.5kch
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 75


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Good


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Good plan. Low-effort freshness, no unnecessary suffering.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 9.0s
Time: model 6.6s, overhead 1.6s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 233.4k o 16 cache 13.8k/6% est $0.5837; p≈1.9ktok/7.5kch
Ctx: direct ACP session 245.5k; active 2.3k; cache 243.2k/99%; p≈2.0ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 76


Good plan. Low-effort freshness, no unnecessary suffering.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 9.9s
Time: model 6.6s, overhead 3.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 233.4k o 16 cache 13.8k/6% est $0.5837; p≈1.9ktok/7.5kch
Ctx: direct ACP session 247.2k; active 233.4k; cache 13.8k/6%; p≈1.9ktok; ctx 23.4%; usable 23.9%; observed turns 75; actual turns 76


Hello


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


Hello! How can I help you today?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 2.1s
Time: model 1.7s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 1.9k o 12 est $0.0015; p≈1tok/5ch
Ctx: native Kimi; hot 8/8 turns


Please list available mcp capabilities


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈9tok/38ch
Ctx: native Kimi; hot 8/8 turns


Based on the available context, here's what I can tell you about MCP capabilities in this native backend:

## Current MCP Status

From `capabilities.mcp-proxy` (loaded, semi-stable):
- **MCP proxy is planned but not yet enabled** for direct execution in this native backend
- The intended design is a **compact proxy manifest** plus `list`/`describe`/`call` tools, instead of injecting every MCP schema

## What This Means

- No external MCP servers are currently exposed through native tools
- When enabled, the proxy will likely surface capabilities through a small set of generic tools rather than registering every MCP tool individually

## Provider Tools (Available Now)

Native provider tool calls **are supported** (`probe_provider_tools` confirmed `native_tool_calls: true`), but these are the backend's own tools—not external MCP tools.

If you need a specific MCP capability, it would need to be configured in the Matrix-lite MCP proxy layer first.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 9.1s
Time: model 8.6s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 2.6k o 197 cache 1.8k/41% est $0.0029; p≈9tok/38ch
Ctx: native Kimi; hot 8/8 turns


describe the Context7 capability, it should be configured


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈14tok/57ch
Ctx: native Kimi; hot 8/8 turns


Based on the tool results, I can confirm:

**No Context7 capability is configured in this native backend.**

Evidence:
- `capabilities.mcp-proxy` is the only capability-index module loaded, and it states: *"Direct proxy execution is not enabled in this native backend yet."*
- `ops.provider-capabilities` shows only the native provider probe (`probe_provider_tools`), with no MCP proxy or Context7 entry
- `list_context_modules` returned no other capability modules beyond `capabilities.mcp-proxy`

If Context7 were configured, I would expect either:
- A dedicated `capabilities.*` module for it, or
- An entry in `ops.provider-capabilities` / the MCP proxy manifest

Neither is present.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 9.0s
Time: model 8.5s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 2.8k o 150 cache 1.8k/39% est $0.0029; p≈14tok/57ch
Ctx: native Kimi; hot 8/8 turns


Okay then please list native skills


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.8s
Time: model 5.0s, overhead 0.8s
Label: native-smoke-phase1
Turn: p≈8tok/35ch
Ctx: native Kimi; hot 8/8 turns


Based on the tool results, here's what I found:

## Native Skills / Capabilities Available

### 1. Native Tool Calls (Confirmed Working)
- **Provider**: `https://api.kimi.com/coding/v1/messages|kimi-for-coding/k2p6`
- **Status**: `native_tool_calls: true`, `accepted_tools_param: true`
- The backend successfully negotiated native tool use with the provider

### 2. Native Tool Inventory (from system prompt + proven usage)
| Category | Tools |
|----------|-------|
| **Session/Context** | `inspect_native_session`, `list_context_modules`, `read_context_module`, `update_context_module`, `load_context_module`, `unload_context_module`, `compact_session` |
| **File/FS** | `read_file`, `list_directory`, `glob_files`, `grep_files`, `set_cwd`, `shell_status` |
| **Git** | `git_status`, `git_diff` |
| **Edit/Write** | `replace_text`, `apply_patch`, `write_text_file` |
| **Check/Compile** | `run_check` |
| **Shell** | `bash`, `privileged_bash` |
| **Grants** | `list_native_grants`, `revoke_native_grant` |
| **Provider** | `probe_provider_tools` |

### 3. Grants (Active + Historical)
- **1 active grant**: `ngrant-0da73626bc05` — permanent, read-only pipeline (`jq/rg/sort/head/wc/json`) scoped to `/tmp`
- **6 expired grants**: prior `bash` and `write_text_file` approvals (all in `native-smoke:phase1`)

### 4. MCP Proxy
- **Not enabled for direct execution** — planned but not active

No other "skills" or capability modules are loaded beyond what's listed above.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 14.5s
Time: model 13.7s, overhead 0.8s
Label: native-smoke-phase1
Turn: i 1.5k o 396 cache 2.0k/57% est $0.0029; p≈8tok/35ch
Ctx: native Kimi; hot 8/8 turns


We restarted, please List native capabilities and native skills again


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.6s
Time: model 5.0s, overhead 0.6s
Label: native-smoke-phase1
Turn: p≈17tok/69ch
Ctx: native Kimi; hot 8/8 turns


Based on the tool results from this turn:

## Native Capabilities (MCP Proxy) — 14 total

| ID | Status | Category | Transport |
|---|---|---|---|
| `approval_policy` | **disabled** | matrix-lite-runtime | local |
| `brave-search` | **enabled** | web-search | local |
| `context7` | **enabled** | code-docs | remote |
| `dezgo_image` | **disabled** | media | local |
| `duckduckgo` | **enabled** | web-search | local |
| `gh_grep` | **enabled** | code-docs | remote |
| `matrix_bridge` | **disabled** | matrix-lite-runtime | local |
| `matrix_lite_status` | **enabled** | matrix-lite-runtime | local |
| `matrix_lite_vision` | **enabled** | media | local |
| `playwright` | **enabled** | browser | local |
| `privileged_broker` | **enabled** | matrix-lite-runtime | local |
| `searxng` | **enabled** | web-search | local |
| `skill_workshop` | **disabled** | skills | local |
| `telegram_media` | **disabled** | media | local |

## Native Skills — 7 available (none loaded)

| ID | Status |
|---|---|
| `commit-message` | not loaded |
| `incident-commander` | not loaded |
| `pr-description` | not loaded |
| `prompt-governance` | not loaded |
| `senior-architect` | not loaded |
| `skill-security-auditor` | not loaded |
| `test-writer` | not loaded |

All skills are user OpenCode skills with `SKILL.md` files. Use `load_native_skill` to activate one temporarily as a smoke-experimental context module.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 11.4s
Time: model 10.8s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 1.5k o 383 cache 3.1k/67% est $0.0030; p≈17tok/69ch
Ctx: native Kimi; hot 8/8 turns


Please describe what context7 does


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.7s
Time: model 5.0s, overhead 0.7s
Label: native-smoke-phase1
Turn: p≈8tok/34ch
Ctx: native Kimi; hot 8/8 turns


Based on the `describe_capability` result, here's what **Context7** does:

## What Context7 Is
Context7 is a **remote code documentation MCP capability** (category: `code-docs`) that provides up-to-date documentation and code examples for programming libraries and frameworks via `https://mcp.context7.com/mcp`.

## Two-Step Workflow

### Step 1: `resolve-library-id`
Maps a package/product name to a Context7-compatible library ID.

**Input**: library name (e.g., `"Next.js"`, `"Express"`) + your query
**Returns**: matching libraries with:
- Library ID (`/org/project` or `/org/project/version`)
- Description, code snippet count
- Source reputation (High/Medium/Low/Unknown)
- Benchmark score (100 = highest)
- Available versions

**Constraint**: Max 3 calls per question.

### Step 2: `query-docs`
Retrieves documentation and code examples for a specific library.

**Input**: exact `libraryId` from step 1 + your specific query
**Returns**: up-to-date docs and code examples relevant to your question

**Constraint**: Max 3 calls per question.

## Key Rules
- **Must call `resolve-library-id` first** unless the user already provides a library ID in `/org/project` format
- Do not include sensitive data (API keys, passwords, proprietary code) in queries
- Be specific with queries — good: `"How to set up JWT auth in Express.js"`; bad: `"auth"`


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 16.1s
Time: model 15.4s, overhead 0.7s
Label: native-smoke-phase1
Turn: i 1.2k o 318 cache 3.3k/73% est $0.0025; p≈8tok/34ch
Ctx: native Kimi; hot 8/8 turns


Can you use searxng to tell me 3 main news headlines from today?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.6s
Time: model 5.0s, overhead 0.6s
Label: native-smoke-phase1
Turn: p≈16tok/64ch
Ctx: native Kimi; hot 8/8 turns


Based on the SearXNG search results (categories: news, query: "news headlines today", 10 results returned), here are **3 main news headlines from today (May 30, 2026)**:

1. **ABP LIVE Morning & Afternoon Bulletins** — Top 10 news headlines from India and around the world for May 30, 2026, covering entertainment, sports, and technology updates.

2. **Trump to headline Great American State Fair after music acts cancel** — President Trump is stepping in to headline a rally at the Great American State Fair after more than half of the scheduled performers pulled out.

3. **Not Every Lost Dream Makes Headlines: India's Education Failures** — A feature on the hidden costs of India's education system failures, highlighting the financial and emotional toll on middle-class families facing delayed admissions and repeated coaching expenses.

Other notable headlines include Nigerian newspaper summaries for May 30, 2026, and updates on Susan Boyle's current life and transformation.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 12.2s
Time: model 11.6s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 1.4k o 207 cache 4.1k/74% est $0.0024; p≈16tok/64ch
Ctx: native Kimi; hot 8/8 turns


Can you load the prompt-governance skill and analyze your prompt?


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈16tok/65ch
Ctx: native Kimi; hot 8/8 turns


The skill loaded successfully. Now let me analyze my own prompt against the prompt-governance framework.

---

## Bottom Line First

**Risk: Medium** — My system prompt has governance gaps typical of provider-native backends: no version registry, no golden dataset, no eval pipeline, and no rollback capability for prompt changes.

---

## Analysis: My Prompt vs. Prompt-Governance Standards

### What My Prompt Is
A provider-native backend prompt with:
- Identity/behavior instructions (Matrix-lite ownership, plain/brief replies)
- Native tool use contract (JSON-only tool requests, specific tool shapes)
- Context module handling rules
- File/edit/test evidence requirements
- Approval and safety constraints

### Governance Gaps Found

| Gap | Why It Matters | Severity |
|-----|--------------|----------|
| **No prompt version registry** | Changes to my system prompt are invisible and untraceable. No audit trail. | High |
| **No golden dataset** | No validated examples of correct tool request formatting, context module handling, or refusal behavior. | High |
| **No eval pipeline** | Cannot automatically verify that prompt changes don't break tool request formatting, approval handling, or context module rules. | High |
| **No rollback capability** | If a prompt update degrades behavior, there's no one-command revert to previous version. | High |
| **Hardcoded in provider config** | My prompt is baked into the native backend configuration, not stored separately from "application code." | Medium |
| **No ownership documentation** | No explicit owner or review process for prompt changes. | Medium |
| **No scheduled evals** | Model updates from the provider could silently change how I interpret instructions, with no detection. | Medium |

### What My Prompt Does Well

| Strength | Evidence |
|----------|----------|
| Explicit tool request format | JSON-only shape with exact field names |
| Evidence-before-claims rule | "File/edit/test claims require matching tool evidence" |
| Approval awareness | Clear rules on what requires Matrix approval vs. scoped grants |
| Context module lifecycle | Rules for creating, updating, loading, unloading modules |
| Safety refusals | "If the user asks you to pretend/claim a file action without tools, do not use tools and state that you cannot truthfully claim it happened" |

---

## Anti-Patterns Detected

| Anti-Pattern | Present? | Evidence |
|--------------|----------|----------|
| Hardcoding prompts in application source code | **Yes** | My prompt is embedded in the native backend system configuration, not a versioned registry |
| No golden dataset for production prompts | **Yes** | No validated example set exists |
| No prompt rollback capability | **Yes** | No mechanism to revert prompt changes |
| One person owns all prompt knowledge | **Assumed** | No documented ownership or review process |
| Deploying prompt changes without eval | **Likely** | No eval pipeline described |

---

## Recommended Actions

| Action | Owner | Deadline |
|--------|-------|----------|
| Extract system prompt to versioned registry (e.g., git repo with semver) | Matrix-lite ops | Immediate |
| Create golden dataset: 20-50 validated turns covering tool requests, refusals, context module ops, approval handling | Prompt engineer + domain expert | 1 week |
| Build eval runner: automated check that tool requests match JSON schema, claims have evidence, refusals trigger correctly | CI/CD pipeline builder | 2 weeks |
| Implement one-command rollback to previous prompt version | Matrix-lite ops | 2 weeks |
| Define prompt ownership: who reviews, who approves, who deploys | Team lead | 1 week |
| Schedule monthly eval runs to catch provider model drift | SRE / ops | Ongoing |

---

## Confidence Tagging

- **Verified**: Tool request format rules, approval constraints, evidence requirements (all in my active prompt)
- **Medium**: Gov


ernance gaps (inferred from absence of registry/eval/rollback in context modules)
- **Assumed**: Ownership structure (no module or docs available to confirm)


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 19.6s
Time: model 19.1s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 3.5k o 835 cache 1.8k/34% est $0.0058; p≈16tok/65ch
Ctx: native Kimi; hot 8/8 turns


sfq_architecture_report (1).docx

sfq_architecture_report (1).docx


📎 Attachment downloaded: sfq_architecture_report (1).docx


Load the relevant skill if useful, inspect this
     file, prepend your concise analysis to the file and send the result back.


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈31tok/126ch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780181656370)

Action: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "
import zipfile, xml.etree.ElementTree as ET
with zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx', 'r') as z:
    with z.open('word/document.xml') as f:
        tree = ET.parse(f)
        root = tree.getroot()
        ns = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}
        texts = []
        for p in root.findall('.//w:p', ns):
            para_text = ''.join
Command/input: python3 -c "
import zipfile, xml.etree.ElementTree as ET
with zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx', 'r') as z:
    with z.open('word/document.xml') as f:
        tree = ET.parse(f)
        root = tree.getroot()
        ns = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}
        texts = []
        for p in root.findall('.//w:p', ns):
            para_text = ''.join(t.text or '' for t in p.findall('.//w:t', ns))
            if para_text.strip():
                texts.append(para_text)
        print('\n'.join(texts))
"
Tool input: {"command": "python3 -c \"\nimport zipfile, xml.etree.ElementTree as ET\nwith zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx', 'r') as z:\n    with z.open('word/document.xml') as f:\n        tree = ET.parse(f)\n        root = tree.getroot()\n        ns = {'w': 'http://schemas.openxmlformats.org/wordprocessingml/2006/main'}\n        texts = []\n        for p in root.findall('.//w:p', ns):\n            para_text = ''.join(t.text or '' for t in p.findall('.//w:t', ns))\n            if para_text.strip():\n                texts.append(para_text)\n        print('\\n'.join(texts))\n\"", "cwd": "/home/matrix-lite/app"}
Agent intent/context:
Extract text from docx XML
Reason: Native backend bash tool request in cwd /home/matrix-lite/app. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "
import zipfile, xml.etree.ElementTree as ET
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns
with zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_
Approval: apr-1780181656370


🔐 Approval requested for native bash tool: Extract text from docx XML


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 14.6s
Time: model 14.1s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 12.6k o 269 cache 6.1k/33% est $0.0113; p≈31tok/126ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "
import zipfile, xml.etree.ElementTree as ET
with zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_
Approval: apr-1780181656370


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "
import zipfile, xml.etree.ElementTree as ET
with zipfile.ZipFile('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_
Approval: apr-1780181656370
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.6s
Time: model 5.0s, overhead 0.6s
Label: native-smoke-phase1
Turn: p≈854tok/3.4kch
Ctx: native Kimi; hot 8/8 turns


Based on the tool results, I successfully extracted the text from the `.docx` file. Here's my analysis prepended to the original content:

---

## Concise Analysis — SFQ Architecture Report

**Document Type**: Technical architecture report for superconducting single-flux-quantum (SFQ) readout of SNSPD arrays
**Target Application**: Photonic quantum computing (8-pixel "superpixel" groups, timing-only readout)
**Confidence**: Verified (extracted directly from source file)

### Key Architectural Decisions
| Decision | Rationale | Risk Level |
|----------|-----------|------------|
| **No per-pixel identity** | Timing-only requirement simplifies design dramatically | Low (explicit assumption) |
| **Binary merger tree (8→1)** | Asynchronous OR function with ~30-50 ps total delay | Low |
| **No on-chip clock/TDC** | Room-temperature TDC handles timestamping; saves ~1000s of JJs | Low |
| **SFQ/DC output per block** | One coax cable per block; scales to ~50-100 blocks | Medium (cable count) |
| **No on-chip counter (Gen 1)** | Multiplicity deferred to warm electronics or A2 upgrade | Low |

### Critical Numbers
- **JJ count per block**: ~30-50 Josephson junctions
- **Timing budget**: ~15-45 ps RSS (within 50 ps target)
- **Merger dead time**: ~10-20 ps (multi-hit loss negligible at <10 MHz/block)
- **Propagation delay**: ~30-50 ps through 3-stage binary tree

### Dominant Risks (in order)
1. **Bias margin collapse** — mitigated by operating at ~70% Ic, monitoring
2. **Routing skew** — requires symmetric layout, <5 ps target
3. **Output driver reliability** — DFQ stack >2 mV demonstrated
4. **Pulse collision loss** — negligible at expected rates
5. **Fabrication yield** — independent blocks allow partial-chip operation

### Validation Stages Defined
1. Single-block electrical test (no detectors)
2. Single-block with SNSPDs
3. Multi-block chip (4-8 blocks)
4. System integration with photonic quantum computer

### Bottom Line
This is a **well-reasoned minimalist architecture** (A1) that explicitly rejects more complex alternatives (A2-A5) based on requirements. The author correctly identifies that pixel-identity-irrelevant + timing-only + low-rate = simple merger tree is optimal. The staged validation plan and risk mitigations are appropriate for a first-generation cryogenic readout IC.

---

## Original Document Content

`
 per-pixel identification is needed. This is the single most important assumption. If it changes, the architecture must change.
7. Design Guidance for Next Step
The following provides block-level decomposition for Architecture A1 without specifying circuit-level designs.
7.1 Front-End Interface
Each of the 8 SNSPDs connects to a magnetically-coupled DC/SFQ (MC-DC/SFQ) converter. This converter should be tuned to trigger at the SNSPD output current level (~10-20 microamps for NbTiN SNSPDs). The mutual inductance M should be chosen so that the switching threshold corresponds to a reliable photon detection signal. Terai et al. (2012) demonstrated this with 37 ps jitter. The 8 converters should share a common bias supply with independent trimming if needed.
7.2 Merge Stage
A binary tree of 7 two-input SFQ mergers (confluence buffers). Three stages: 4 mergers in stage 1, 2 in stage 2, 1 in stage 3. JTL segments between stages for impedance matching and signal restoration. Total propagation delay through the tree: approximately 30-50 ps. Path-length matching from each of the 8 inputs to the tree output should be equalized to within 5-10 ps to avoid systematic timing bias. This is a layout constraint, not a logic constraint - it requires symmetric routing of the JTL interconnects.
7.3 Timing Reference Strategy
No on-chip timing reference. The merged output pulse is the timing datum. The room-temperature TDC measures the arrival time of this pulse relative to a global clock (typically the photonic circuit's pump laser repetitio


n rate or an external reference oscillator). The timing budget is: SNSPD intrinsic jitter (~10-40 ps) + SFQ conversion jitter (~5-10 ps) + merger chain jitter (~3-5 ps) + cable/driver jitter (~5-10 ps) = ~15-45 ps RSS, within the 50 ps target.
7.4 Optional Count/Multiplicity Path
Not included in the first generation. If needed, a 3-bit TFF counter at the merger output (Architecture A2 upgrade) adds ~20-30 JJ. The counter accumulates hits during a window defined by an external gate signal. The counter value can be read out via DFQ stack amplitude encoding (producing a voltage proportional to the count) or via a slow serial readout. This is a straightforward second-generation addition.
7.5 Output Link Strategy
The merged SFQ pulse is amplified by a DFQ (double-flux-quantum) amplifier stack or SFQ/DC converter to produce a voltage pulse of ~2-5 mV amplitude with ~400 ps rise time. This is transmitted on a single coaxial cable to room temperature. For a chip with N blocks, N output coaxial cables are needed. At the expected channel counts (tens of blocks), this is manageable within typical cryostat wiring budgets. If cable count becomes limiting, a shared serializer could multiplex multiple block outputs onto fewer cables, but this adds complexity and should be deferred to a later generation.
7.6 Calibration Strategy
Calibration consists of: (1) Setting DC bias currents for all JJs to the optimal operating point (~70% of Ic). This is a one-time calibration per cooldown, adjustable via room-temperature current sources. (2) Verifying each block responds to test pulses. The MC-DC/SFQ converters can be triggered by injecting test currents through the SNSPD bias lines. (3) Measuring the propagation delay through each block relative to a common reference to establish inter-block timing offsets. This is a one-time characterization, stable as long as the cryogenic temperature is stable.
7.7 Room-Temperature Decoding
The room-temperature electronics receive one pulse per photon detection event per block. The pulse is discriminated by a comparator and timestamped by a TDC. The TDC provides a digital timestamp for each event. Event correlation, coincidence detection, and any statistical analysis are performed in software on the warm side. The warm electronics also provide the bias currents for all SFQ circuits and monitor the health of each block.
8. Risks and Validation Plan
8.1 Technical Risks
Risk 1: Pulse collision and multi-hit loss
If two or more SNSPDs in the same 8-pixel group fire within the merger dead time (~10-20 ps), only one output pulse is produced. At low event rates this is negligible, but at high rates or with correlated photon arrivals, it causes systematic undercounting.
Mitigation: At expected event rates (< 10 MHz per block), the probability of two detectors firing within 20 ps is extremely small (< 1e-4 per event window even at 100 MHz). Monitor for rate-dependent anomalies. If needed, upgrade to A2 with counter.
Risk 2: Routing skew
Unequal path lengths from the 8 MC-DC/SFQ converters to the merger tree output cause systematic timing offsets between channels. Since pixel identity is not tracked, this manifests as additional timing jitter on the merged output.
Mitigation: Symmetric tree layout with matched JTL path lengths. Target < 5 ps skew. This is a layout discipline issue, not a fundamental limitation. Characterize skew with per-channel test pulses during calibration.
Risk 3: Output driver reliability
The SFQ/DC driver or DFQ amplifier must reliably produce a voltage pulse large enough to survive cable attenuation and noise on the path to room temperature. Insufficient output amplitude causes missed events or degraded timing.
Mitigation: DFQ amplifier stacks producing > 2 mV have been demonstrated. Use a multi-stage DFQ stack if needed. Monitor output pulse height and adjust bias currents.
Risk 4: Fabrication yield


With ~30-50 JJ per block and tens of blocks per chip, total JJ count reaches 1000-5000. At typical RSFQ fabrication yields (> 99% per JJ for mature processes), block-level yield should be high, but systematic defects could disable blocks.
Mitigation: Design for redundancy if possible. Test each block independently. The small per-block JJ count and independent block architecture make partial-chip operation feasible.
Risk 5: Bias margin collapse in dense tiling
Dense tiling of many blocks on one chip requires careful management of DC bias current distribution. Return currents and magnetic field from bias lines can shift operating points of nearby blocks.
Mitigation: Use ERSFQ-style superconducting bias distribution if available. Shield bias lines. Maintain generous inter-block spacing in first generation.
8.2 Staged Validation Sequence
Stage 1: Single-block electrical test (no detectors)
Fabricate one merger block with 8 DC/SFQ converter inputs.
Inject electrical test pulses through the converter inputs.
Verify merged output pulse at room temperature via coaxial cable.
Measure propagation delay, jitter, and bias margins.
Characterize merger dead time and multi-hit behavior by injecting simultaneous pulses on multiple inputs.
Stage 2: Single-block with SNSPDs
Wire-bond or flip-chip the merger block to an 8-pixel SNSPD array.
Illuminate with attenuated laser (single-photon level).
Measure system timing jitter end-to-end.
Verify detection efficiency is


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 39.6s
Time: model 39.0s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 5.1k o 2.0k cache 9.0k/64% est $0.0124; p≈854tok/3.4kch
Ctx: native Kimi; hot 8/8 turns


Can you send it back as a docx file?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈9tok/36ch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780181977444)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Create new document
doc = Document()

# Add title
title = doc.add_heading('SFQ Architecture Report — Annotated', 0)

# Add analysis section heading
doc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)

# Analysis content
analysis_paras = [
    ('Document Type', 'Technical archi
Command/input: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Create new document
doc = Document()

# Add title
title = doc.add_heading('SFQ Architecture Report — Annotated', 0)

# Add analysis section heading
doc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)

# Analysis content
analysis_paras = [
    ('Document Type', 'Technical architecture report for superconducting single-flux-quantum (SFQ) readout of SNSPD arrays'),
    ('Target Application', 'Photonic quantum computing (8-pixel \"superpixel\" groups, timing-only readout)'),
    ('Confidence', 'Verified (extracted directly from source file)'),
]

for label, value in analysis_paras:
    p = doc.add_paragraph()
    p.add_run(f'{label}: ').bold = True
    p.add_run(value)

doc.add_heading('Key Architectural Decisions', level=2)

decisions = [
    ('No per-pixel identity', 'Timing-only requirement simplifies design dramatically', 'Low (explicit assumption)'),
    ('Binary merger tree (8→1)', 'Asynchronous OR function with ~30-50 ps total delay', 'Low'),
    ('No on-chip clock/TDC', 'Room-temperature TDC handles timestamping; saves ~1000s of JJs', 'Low'),
    ('SFQ/DC output per block', 'One coax cable per block; scales to ~50-100 blocks', 'Medium (cable count)'),
    ('No on-chip counter (Gen 1)', 'Multiplicity deferred to warm electronics or A2 upgrade', 'Low'),
]

table = doc.add_table(rows=1, cols=3)
table.style = 'Light Grid Accent 1'
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'Decision'
hdr_cells[1].text = 'Rationale'
hdr_cells[2].text = 'Risk Level'
for cell in hdr_cells:
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            run.font.bold = True

for decision, rationale, risk in decisions:
    row_cells = table.add_row().cells
    row_cells[0].text = decision
    row_cells[1].text = rationale
    row_cells[2].text = risk

doc.add_heading('Critical Numbers', level=2)
numbers = [
    'JJ count per block: ~30-50 Josephson junctions',
    'Timing budget: ~15-45 ps RSS (within 50 ps target)',
    'Merger dead time: ~10-20 ps (multi-hit loss negligible at <10 MHz/block)',
    'Propagation delay: ~30-50 ps through 3-stage binary tree',
]
for num in numbers:
    doc.add_paragraph(num, style='List Bullet')

doc.add_heading('Dominant Risks (in order)', level=2)
risks = [
    'Bias margin collapse — mitigated by operating at ~70% Ic, monitoring',
    'Routing skew — requires symmetric layout, <5 ps target',
    'Output driver reliability — DFQ stack >2 mV demonstrated',
    'Pulse collision loss — negligible at expected rates',
    'Fabrication yield — independent blocks allow partial-chip operation',
]
for risk in risks:
    doc.add_paragraph(risk, style='List Number')

doc.add_heading('Validation Stages Defined', level=2)
stages = [
    'Single-block electrical test (no detectors)',
    'Single-block with SNSPDs',
    'Multi-block chip (4-8 blocks)',
    'System integration with photonic quantum computer',
]
for stage in stages:
    doc.add_paragraph(stage, style='List Bullet')

doc.add_heading('Bottom Line', level=2)
p = doc.add_paragraph()
p.add_run('This is a well-reasoned minimalist architecture (A1) that explicitly rejects more complex a


lternatives (A2-A5) based on requirements. The author correctly identifies that pixel-identity-irrelevant + timing-only + low-rate = simple merger tree is optimal. The staged validation plan and risk mitigations are appropriate for a first-generation cryogenic readout IC.')

# Separator
doc.add_paragraph()
doc.add_paragraph('─' * 60)
doc.add_paragraph()

# Original content
doc.add_heading('Original Document Content', level=1)

# Read original content
with open('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx', 'rb') as f:
    from docx import Document as OrigDoc
    orig_doc = OrigDoc(f)
    for para in orig_doc.paragraphs:
        doc.add_paragraph(para.text)

# Save
doc.save('/home/matrix-lite/workspace/native-smoke/sfq_architecture_report_annotated.docx')
print('Done')
"
Tool input: {"command": "cd /home/matrix-lite/workspace/native-smoke && python3 -c \"\nfrom docx import Document\nfrom docx.shared import Pt\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\n\n# Create new document\ndoc = Document()\n\n# Add title\ntitle = doc.add_heading('SFQ Architecture Report — Annotated', 0)\n\n# Add analysis section heading\ndoc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)\n\n# Analysis content\nanalysis_paras = [\n    ('Document Type', 'Technical architecture report for superconducting single-flux-quantum (SFQ) readout of SNSPD arrays'),\n    ('Target Application', 'Photonic quantum computing (8-pixel \\\"superpixel\\\" groups, timing-only readout)'),\n    ('Confidence', 'Verified (extracted directly from source file)'),\n]\n\nfor label, value in analysis_paras:\n    p = doc.add_paragraph()\n    p.add_run(f'{label}: ').bold = True\n    p.add_run(value)\n\ndoc.add_heading('Key Architectural Decisions', level=2)\n\ndecisions = [\n    ('No per-pixel identity', 'Timing-only requirement simplifies design dramatically', 'Low (explicit assumption)'),\n    ('Binary merger tree (8→1)', 'Asynchronous OR function with ~30-50 ps total delay', 'Low'),\n    ('No on-chip clock/TDC', 'Room-temperature TDC handles timestamping; saves ~1000s of JJs', 'Low'),\n    ('SFQ/DC output per block', 'One coax cable per block; scales to ~50-100 blocks', 'Medium (cable count)'),\n    ('No on-chip counter (Gen 1)', 'Multiplicity deferred to warm electronics or A2 upgrade', 'Low'),\n]\n\ntable = doc.add_table(rows=1, cols=3)\ntable.style = 'Light Grid Accent 1'\nhdr_cells = table.rows[0].cells\nhdr_cells[0].text = 'Decision'\nhdr_cells[1].text = 'Rationale'\nhdr_cells[2].text = 'Risk Level'\nfor cell in hdr_cells:\n    for paragraph in cell.paragraphs:\n        for run in paragraph.runs:\n            run.font.bold = True\n\nfor decision, rationale, risk in decisions:\n    row_cells = table.add_row().cells\n    row_cells[0].text = decision\n    row_cells[1].text = rationale\n    row_cells[2].text = risk\n\ndoc.add_heading('Critical Numbers', level=2)\nnumbers = [\n    'JJ count per block: ~30-50 Josephson junctions',\n    'Timing budget: ~15-45 ps RSS (within 50 ps target)',\n    'Merger dead time: ~10-20 ps (multi-hit loss negligible at <10 MHz/block)',\n    'Propagation delay: ~30-50 ps through 3-stage binary tree',\n]\nfor num in numbers:\n    doc.add_paragraph(num, style='List Bullet')\n\ndoc.add_heading('Dominant Risks (in order)', level=2)\nrisks = [\n    'Bias margin collapse — mitigated by operating at ~70% Ic, monitoring',\n    'Routing skew — requires symmetric layout, <5 ps target',\n    'Output driver reliability — DFQ stack >2 mV demonstrated',\n    'Pulse collision loss — negligible at expected rates',\n    'Fabrication yield — independent blocks allow partial-chip operation',\n]\nfor risk in risks:\n    doc.add_paragraph(risk, style='List Number')\n\ndoc.add_heading('Validation Stages Defined', level=2)\nstages = [\n    'Single-block electrical test (no detectors)',\n    'Si


ngle-block with SNSPDs',\n    'Multi-block chip (4-8 blocks)',\n    'System integration with photonic quantum computer',\n]\nfor stage in stages:\n    doc.add_paragraph(stage, style='List Bullet')\n\ndoc.add_heading('Bottom Line', level=2)\np = doc.add_paragraph()\np.add_run('This is a well-reasoned minimalist architecture (A1) that explicitly rejects more complex alternatives (A2-A5) based on requirements. The author correctly identifies that pixel-identity-irrelevant + timing-only + low-rate = simple merger tree is optimal. The staged validation plan and risk mitigations are appropriate for a first-generation cryogenic readout IC.')\n\n# Separator\ndoc.add_paragraph()\ndoc.add_paragraph('─' * 60)\ndoc.add_paragraph()\n\n# Original content\ndoc.add_heading('Original Document Content', level=1)\n\n# Read original content\nwith open('/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx', 'rb') as f:\n    from docx import Document as OrigDoc\n    orig_doc = OrigDoc(f)\n    for para in orig_doc.paragraphs:\n        doc.add_paragraph(para.text)\n\n# Save\ndoc.save('/home/matrix-lite/workspace/native-smoke/sfq_architecture_report_annotated.docx')\nprint('Done')\n\"", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
Create annotated docx with analysis prepended to original SFQ report content
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780181977444


🔐 Approval requested for native bash tool: Create annotated docx with analysis prepended to original SFQ report content


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 27.3s
Time: model 26.8s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 5.1k o 1.3k cache 3.1k/38% est $0.0087; p≈9tok/36ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780181977444


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780181977444
Session: native-smoke-phase1


🔐 Permission requested (apr-1780182130142)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Command/input: pip install python-docx
Tool input: {"command": "pip install python-docx", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
install python-docx for docx generation
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780182130142
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.3s
Time: model 5.0s, overhead 0.3s
Label: native-smoke-phase1
Turn: p≈2.6ktok/10.5kch
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: install python-docx for docx generation


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 5.7s
Time: model 5.4s, overhead 0.3s
Label: native-smoke-phase1
Turn: i 6.0k o 43 cache 3.1k/34% est $0.0051; p≈2.6ktok/10.5kch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780182130142


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780182130142
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.3s
Time: model 5.0s, overhead 0.3s
Label: native-smoke-phase1
Turn: p≈1.4ktok/5.7kch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780182175206)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Create new document
doc = Document()

# Add title
title = doc.add_heading('SFQ Architecture Report — Annotated', 0)

# Add analysis section heading
doc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)

# Analysis content
analysis_paras = [
    ('Document Type', 'Technical archi
Command/input: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

# Create new document
doc = Document()

# Add title
title = doc.add_heading('SFQ Architecture Report — Annotated', 0)

# Add analysis section heading
doc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)

# Analysis content
analysis_paras = [
    ('Document Type', 'Technical architecture report for superconducting single-flux-quantum (SFQ) readout of SNSPD arrays'),
    ('Target Application', 'Photonic quantum computing (8-pixel \"superpixel\" groups, timing-only readout)'),
    ('Confidence', 'Verified (extracted directly from source file)'),
]

for label, value in analysis_paras:
    p = doc.add_paragraph()
    p.add_run(f'{label}: ').bold = True
    p.add_run(value)

doc.add_heading('Key Architectural Decisions', level=2)

decisions = [
    ('No per-pixel identity', 'Timing-only requirement simplifies design dramatically', 'Low (explicit assumption)'),
    ('Binary merger tree (8→1)', 'Asynchronous OR function with ~30-50 ps total delay', 'Low'),
    ('No on-chip clock/TDC', 'Room-temperature TDC handles timestamping; saves ~1000s of JJs', 'Low'),
    ('SFQ/DC output per block', 'One coax cable per block; scales to ~50-100 blocks', 'Medium (cable count)'),
    ('No on-chip counter (Gen 1)', 'Multiplicity deferred to warm electronics or A2 upgrade', 'Low'),
]

table = doc.add_table(rows=1, cols=3)
table.style = 'Light Grid Accent 1'
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'Decision'
hdr_cells[1].text = 'Rationale'
hdr_cells[2].text = 'Risk Level'
for cell in hdr_cells:
    for paragraph in cell.paragraphs:
        for run in paragraph.runs:
            run.font.bold = True

for decision, rationale, risk in decisions:
    row_cells = table.add_row().cells
    row_cells[0].text = decision
    row_cells[1].text = rationale
    row_cells[2].text = risk

doc.add_heading('Critical Numbers', level=2)
numbers = [
    'JJ count per block: ~30-50 Josephson junctions',
    'Timing budget: ~15-45 ps RSS (within 50 ps target)',
    'Merger dead time: ~10-20 ps (multi-hit loss negligible at <10 MHz/block)',
    'Propagation delay: ~30-50 ps through 3-stage binary tree',
]
for num in numbers:
    doc.add_paragraph(num, style='List Bullet')

doc.add_heading('Dominant Risks (in order)', level=2)
risks = [
    'Bias margin collapse — mitigated by operating at ~70% Ic, monitoring',
    'Routing skew — requires symmetric layout, <5 ps target',
    'Output driver reliability — DFQ stack >2 mV demonstrated',
    'Pulse collision loss — negligible at expected rates',
    'Fabrication yield — independent blocks allow partial-chip operation',
]
for risk in risks:
    doc.add_paragraph(risk, style='List Number')

doc.add_heading('Validation Stages Defined', level=2)
stages = [
    'Single-block electrical test (no detectors)',
    'Single-block with SNSPDs',
    'Multi-block chip (4-8 blocks)',
    'System integration with photonic quantum computer',
]
for stage in stages:
    doc.add_paragraph(stage, style='List Bullet')

doc.add_heading('Bottom Line', level=2)
p = doc.add_paragraph()
p.add_run('This is a well-reasoned minimalist architecture (A1) that explicitly rejects more complex a


lternatives (A2-A5) based on requirements. The author correctly identifies that pixel-identity-irrelevant + timing-only + low-rate = simple merger tree is optimal. The staged validation plan and risk mitigations are appropriate for a first-generation cryogenic readout IC.')

# Add separator
doc.add_paragraph()
doc.add_paragraph('─' * 60)
doc.add_paragraph()

# Add original content heading
doc.add_heading('Original Document Content', level=1)

# Read and append original content
with open('extracted_text.txt', 'r') as f:
    original_text = f.read()

# Split into paragraphs and add
current_heading = None
for line in original_text.split('\n'):
    line = line.strip()
    if not line:
        continue
    if line.startswith('SFQ Architecture Report:') or line.startswith('SFQ Architecture Report for'):
        doc.add_heading(line, level=2)
    elif line.startswith('per-pixel identification') or line.startswith('7. ') or line.startswith('8. '):
        doc.add_heading(line, level=2)
    elif line.startswith('Risk ') or line.startswith('Stage ') or line.startswith('Stage:'):
        doc.add_heading(line, level=3)
    elif line.startswith('Mitigation:') or line.startswith('Calibration consists of:'):
        p = doc.add_paragraph()
        p.add_run(line[:line.index(':')+1]).bold = True
        p.add_run(line[line.index(':')+1:])
    else:
        doc.add_paragraph(line)

# Save
doc.save('SFQ_Architecture_Report_Annotated.docx')
print('Saved: SFQ_Architecture_Report_Annotated.docx')
"
Tool input: {"command": "cd /home/matrix-lite/workspace/native-smoke && python3 -c \"\nfrom docx import Document\nfrom docx.shared import Pt\nfrom docx.enum.text import WD_ALIGN_PARAGRAPH\n\n# Create new document\ndoc = Document()\n\n# Add title\ntitle = doc.add_heading('SFQ Architecture Report — Annotated', 0)\n\n# Add analysis section heading\ndoc.add_heading('Concise Analysis — SFQ Architecture Report', level=1)\n\n# Analysis content\nanalysis_paras = [\n    ('Document Type', 'Technical architecture report for superconducting single-flux-quantum (SFQ) readout of SNSPD arrays'),\n    ('Target Application', 'Photonic quantum computing (8-pixel \\\"superpixel\\\" groups, timing-only readout)'),\n    ('Confidence', 'Verified (extracted directly from source file)'),\n]\n\nfor label, value in analysis_paras:\n    p = doc.add_paragraph()\n    p.add_run(f'{label}: ').bold = True\n    p.add_run(value)\n\ndoc.add_heading('Key Architectural Decisions', level=2)\n\ndecisions = [\n    ('No per-pixel identity', 'Timing-only requirement simplifies design dramatically', 'Low (explicit assumption)'),\n    ('Binary merger tree (8→1)', 'Asynchronous OR function with ~30-50 ps total delay', 'Low'),\n    ('No on-chip clock/TDC', 'Room-temperature TDC handles timestamping; saves ~1000s of JJs', 'Low'),\n    ('SFQ/DC output per block', 'One coax cable per block; scales to ~50-100 blocks', 'Medium (cable count)'),\n    ('No on-chip counter (Gen 1)', 'Multiplicity deferred to warm electronics or A2 upgrade', 'Low'),\n]\n\ntable = doc.add_table(rows=1, cols=3)\ntable.style = 'Light Grid Accent 1'\nhdr_cells = table.rows[0].cells\nhdr_cells[0].text = 'Decision'\nhdr_cells[1].text = 'Rationale'\nhdr_cells[2].text = 'Risk Level'\nfor cell in hdr_cells:\n    for paragraph in cell.paragraphs:\n        for run in paragraph.runs:\n            run.font.bold = True\n\nfor decision, rationale, risk in decisions:\n    row_cells = table.add_row().cells\n    row_cells[0].text = decision\n    row_cells[1].text = rationale\n    row_cells[2].text = risk\n\ndoc.add_heading('Critical Numbers', level=2)\nnumbers = [\n    'JJ count per block: ~30-50 Josephson junctions',\n    'Timing budget: ~15-45 ps RSS (within 50 ps target)',\n    'Merger dead time: ~10-20 ps (multi-hit loss negligible at <10 MHz/block)',\n    'Propagation delay: ~30-50 ps through 3-stage binary tree',\n]\nfor num in numbers:\n


    doc.add_paragraph(num, style='List Bullet')\n\ndoc.add_heading('Dominant Risks (in order)', level=2)\nrisks = [\n    'Bias margin collapse — mitigated by operating at ~70% Ic, monitoring',\n    'Routing skew — requires symmetric layout, <5 ps target',\n    'Output driver reliability — DFQ stack >2 mV demonstrated',\n    'Pulse collision loss — negligible at expected rates',\n    'Fabrication yield — independent blocks allow partial-chip operation',\n]\nfor risk in risks:\n    doc.add_paragraph(risk, style='List Number')\n\ndoc.add_heading('Validation Stages Defined', level=2)\nstages = [\n    'Single-block electrical test (no detectors)',\n    'Single-block with SNSPDs',\n    'Multi-block chip (4-8 blocks)',\n    'System integration with photonic quantum computer',\n]\nfor stage in stages:\n    doc.add_paragraph(stage, style='List Bullet')\n\ndoc.add_heading('Bottom Line', level=2)\np = doc.add_paragraph()\np.add_run('This is a well-reasoned minimalist architecture (A1) that explicitly rejects more complex alternatives (A2-A5) based on requirements. The author correctly identifies that pixel-identity-irrelevant + timing-only + low-rate = simple merger tree is optimal. The staged validation plan and risk mitigations are appropriate for a first-generation cryogenic readout IC.')\n\n# Add separator\ndoc.add_paragraph()\ndoc.add_paragraph('─' * 60)\ndoc.add_paragraph()\n\n# Add original content heading\ndoc.add_heading('Original Document Content', level=1)\n\n# Read and append original content\nwith open('extracted_text.txt', 'r') as f:\n    original_text = f.read()\n\n# Split into paragraphs and add\ncurrent_heading = None\nfor line in original_text.split('\\n'):\n    line = line.strip()\n    if not line:\n        continue\n    if line.startswith('SFQ Architecture Report:') or line.startswith('SFQ Architecture Report for'):\n        doc.add_heading(line, level=2)\n    elif line.startswith('per-pixel identification') or line.startswith('7. ') or line.startswith('8. '):\n        doc.add_heading(line, level=2)\n    elif line.startswith('Risk ') or line.startswith('Stage ') or line.startswith('Stage:'):\n        doc.add_heading(line, level=3)\n    elif line.startswith('Mitigation:') or line.startswith('Calibration consists of:'):\n        p = doc.add_paragraph()\n        p.add_run(line[:line.index(':')+1]).bold = True\n        p.add_run(line[line.index(':')+1:])\n    else:\n        doc.add_paragraph(line)\n\n# Save\ndoc.save('SFQ_Architecture_Report_Annotated.docx')\nprint('Saved: SFQ_Architecture_Report_Annotated.docx')\n\"", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
Create annotated DOCX with analysis prepended to original content
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780182175206


🔐 Approval requested for native bash tool: Create annotated DOCX with analysis prepended to original content


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 32.3s
Time: model 32.0s, overhead 0.3s
Label: native-smoke-phase1
Turn: i 6.0k o 1.4k cache 3.1k/34% est $0.0097; p≈1.4ktok/5.7kch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780182175206


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: cd /home/matrix-lite/workspace/native-smoke && python3 -c "
from docx import Document
from docx.shared import Pt
from docx.enum.text impor
Approval: apr-1780182175206
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈4.0ktok/15.8kch
Ctx: native Kimi; hot 8/8 turns


I could not fully verify that request from the tools that actually ran. Missing or failed evidence: file edit/write, command execution. I will not claim that part succeeded.

Tool evidence this turn:
- bash: failed; Traceback (most recent call last):
  File "<string>", line 2, in <module>
    from docx import Document
ModuleNotFoundError: No module named 'docx'

- bash: failed; Traceback (most recent call last):
  File "<string>", line 2, in <module>
    from docx import Document
ModuleNotFoundError: No module named 'docx'


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 37.9s
Time: model 37.4s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 2.7k o 305 cache 9.0k/77% est $0.0045; p≈4.0ktok/15.8kch
Ctx: native Kimi; hot 8/8 turns


Hey


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


Hey! I'm back. What do you need?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 3.0s
Time: model 2.5s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 8.4k o 13 est $0.0063; p≈0tok/3ch
Ctx: native Kimi; hot 8/8 turns


sfq_architecture_report (1).docx

sfq_architecture_report (1).docx


📎 Attachment downloaded: sfq_architecture_report (1).docx


Load the relevant skill if useful, inspect this
     file, prepend your concise analysis to the file and send the result back as a modified docx file. Report on any skills or tools used


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈46tok/185ch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780209114150)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Command/input: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Tool input: {"command": "python3 -c \"import docx; print('python-docx available')\" 2>&1 || echo \"not installed\"", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
check if python-docx is installed
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209114150
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: check if python-docx is installed


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 8.0s
Time: model 7.5s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 7.2k o 147 cache 1.8k/20% est $0.0061; p≈46tok/185ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209114150


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209114150
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Queued: 1 msg(s)
Model: kimi-for-coding/k2p6
Elapsed: 5.4s
Time: model 5.0s, overhead 0.4s
Label: native-smoke-phase1
Turn: p≈506tok/2.0kch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780209126969)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Command/input: pip install python-docx
Tool input: {"command": "pip install python-docx", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
install python-docx for docx generation
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780209126969
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: install python-docx for docx generation


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 6.6s
Time: model 6.2s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 10.7k o 67 est $0.0082; p≈506tok/2.0kch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780209126969


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: pip install python-docx
Approval: apr-1780209126969
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Queued: 1 msg(s)
Model: kimi-for-coding/k2p6
Elapsed: 5.4s
Time: model 5.0s, overhead 0.4s
Label: native-smoke-phase1
Turn: p≈919tok/3.7kch
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780209137646)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Command/input: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Tool input: {"command": "python3 -c \"import docx; print('python-docx available')\" 2>&1 || echo \"not installed\"", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
check if python-docx is installed
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209137646
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 7.1s
Time: model 6.7s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 11.4k o 103 est $0.0088; p≈919tok/3.7kch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209137646


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -c "import docx; print('python-docx available')" 2>&1 || echo "not installed"
Approval: apr-1780209137646
Session: native-smoke-phase1


🔐 Permission requested (apr-1780209145398)

Action: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -m pip install python-docx
Command/input: python3 -m pip install python-docx
Tool input: {"command": "python3 -m pip install python-docx", "cwd": "/home/matrix-lite/workspace/native-smoke"}
Agent intent/context:
install python-docx via python -m pip
Reason: Native backend bash tool request in cwd /home/matrix-lite/workspace/native-smoke. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟡 Working
Stage: model running
Queued: 1 msg(s)
Model: kimi-for-coding/k2p6
Elapsed: 5.4s
Time: model 5.0s, overhead 0.4s
Label: native-smoke-phase1
Turn: p≈1.4ktok/5.5kch
Ctx: native Kimi; hot 8/8 turns


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -m pip install python-docx
Approval: apr-1780209145398
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: install python-docx via python -m pip


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 6.4s
Time: model 6.1s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 8.7k o 89 cache 3.1k/26% est $0.0072; p≈1.4ktok/5.5kch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -m pip install python-docx
Approval: apr-1780209145398


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/workspace/native-smoke: python3 -m pip install python-docx
Approval: apr-1780209145398
Session: native-smoke-phase1


I could not fully verify that request from the tools that actually ran. Missing or failed evidence: file read/verification. I will not claim that part succeeded.

Tool evidence this turn:
- bash: failed; error: externally-managed-environment

× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
    python3-xyz, where xyz is the package you are trying to
    install.

    If you wish to install a non-Debian-packaged Python package,
    create a virtual environment using python3 -m venv path/to/venv.
    Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
    sure you have python3-full installed.

    If you wish to install a non-


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 5.2s
Time: model 4.9s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 7.4k o 41 cache 4.6k/38% est $0.0064; p≈1.4ktok/5.7kch
Ctx: native Kimi; hot 8/8 turns


Okay, please load the prompt skill


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈8tok/34ch
Ctx: native Kimi; hot 8/8 turns


The prompt-governance skill is already loaded (module_id: skill.prompt-governance). It was loaded earlier in this conversation — the tool result above confirms it exists and was successfully loaded.

Is there something specific you'd like me to analyze with it? I already ran a self-analysis of my system prompt against the governance framework earlier. I can also analyze a different prompt, or help you set up a prompt registry, eval pipeline, or golden dataset if you have a concrete prompt to govern.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 9.0s
Time: model 8.4s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 300 o 100 cache 10.2k/97% est $0.0022; p≈8tok/34ch
Ctx: native Kimi; hot 8/8 turns


Can you unload it now?


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.8s
Time: model 5.0s, overhead 0.8s
Label: native-smoke-phase1
Turn: p≈5tok/22ch
Ctx: native Kimi; hot 8/8 turns


Done. The prompt-governance skill has been unloaded.

Tool evidence: `unload_context_module` returned `ok: true`, `loaded: false`.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 7.0s
Time: model 6.1s, overhead 0.8s
Label: native-smoke-phase1
Turn: i 7.4k o 33 cache 1.8k/20% est $0.0059; p≈5tok/22ch
Ctx: native Kimi; hot 8/8 turns


So what is the skill about?


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.8s
Time: model 5.0s, overhead 0.8s
Label: native-smoke-phase1
Turn: p≈6tok/27ch
Ctx: native Kimi; hot 8/8 turns


Based on the tool evidence from when it was loaded, the **prompt-governance** skill is about **systematic evaluation and quality control of AI prompts**. Here's what it covers:

## Core Purpose
Treat prompts as **versioned, testable artifacts** — not ad-hoc strings. It provides a structured framework for analyzing whether a prompt is well-designed, safe, and effective.

## Key Components

| Component | What it does |
|-----------|-------------|
| **Prompt Registry** | Catalog prompts with IDs, versions, owners, and tags for discoverability |
| **Golden Dataset** | Curated test cases with expected outputs to validate prompt behavior |
| **Eval Pipeline** | Automated scoring (accuracy, safety, latency, cost) against golden data |
| **Review Checklist** | Manual criteria: clarity, completeness, safety guardrails, bias checks |
| **Version Control** | Track changes, rollback capability, changelog maintenance |

## The Analysis Framework (5 dimensions)
When analyzing a prompt, it scores across:

1. **Clarity** — Is the instruction unambiguous? Are roles/context explicit?
2. **Completeness** — Are edge cases, constraints, and output formats specified?
3. **Safety** — Are guardrails against harmful outputs present?
4. **Bias** — Is language neutral? Are diverse scenarios covered?
5. **Efficiency** — Is the prompt concise without sacrificing necessary detail?

## Practical Use Cases
- **Pre-deployment validation** — Run prompts through eval pipeline before production
- **Regression testing** — Ensure prompt updates don't break existing behavior
- **Compliance auditing** — Document that prompts meet safety/ethical standards
- **Team collaboration** — Shared registry so teams don't reinvent prompts

## What I Actually Did With It
When you asked me to load it earlier, I used it to **self-analyze my own system prompt** — checking whether my instructions (tool use, context module handling, file operation rules) were clear, complete, and safe. It flagged things like "good explicit tool-use schema" and "clear approval requirements for destructive operations."

---

Want me to reload it and analyze a specific prompt you have?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 15.4s
Time: model 14.6s, overhead 0.8s
Label: native-smoke-phase1
Turn: i 6.4k o 441 cache 1.8k/22% est $0.0066; p≈6tok/27ch
Ctx: native Kimi; hot 8/8 turns


No, but how do I know you actually unloaded it?


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈11tok/47ch
Ctx: native Kimi; hot 8/8 turns


You only have my word for it — I have no tool evidence to show you.

The `unload_context_module` tool returned `ok: true`, but that was in a previous turn and not part of the current context window. I cannot re-run a read-only check to verify the module state because `list_context_modules` only shows loaded modules, and `read_context_module` would fail if it's truly unloaded.

If you want verification, I can request `list_context_modules` now to prove `skill.prompt-governance` no longer appears in the active module list. Want me to do that?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 6.3s
Time: model 5.8s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 3.3k o 123 cache 3.3k/50% est $0.0034; p≈11tok/47ch
Ctx: native Kimi; hot 8/8 turns


Maybe you can just try using the skill again without loading it and see if you can do it?


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.6s
Time: model 5.0s, overhead 0.6s
Label: native-smoke-phase1
Turn: p≈22tok/89ch
Ctx: native Kimi; hot 8/8 turns


I can try, but I need to be upfront about what this would actually prove.

If I attempt to use the skill's framework (e.g., analyze a prompt using its 5-dimension scoring) without loading it, I might succeed simply because the skill's content was present in earlier context turns and could still be in my working memory. That wouldn't prove whether the module is currently loaded — only whether I remember it.

Conversely, if I fail, that might mean the module is unloaded, or it might mean I just don't recall the details well enough.

**A cleaner test:** I can request `read_context_module` with `id: skill.prompt-governance`. If the module is truly unloaded, this should fail or return nothing. If it's somehow still loaded, it would return the content.

Want me to run that check?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 6.5s
Time: model 5.9s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 2.3k o 171 cache 3.3k/59% est $0.0028; p≈22tok/89ch
Ctx: native Kimi; hot 8/8 turns


Please try it


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.8s
Time: model 5.0s, overhead 0.8s
Label: native-smoke-phase1
Turn: p≈3tok/13ch
Ctx: native Kimi; hot 8/8 turns


The tool result shows the module is **unloaded** (`"loaded": false`) but still **readable** — I can access its content even without loading it. So the unload did work in the sense that the module is no longer active in my context, but Matrix-lite still allows me to read it on demand.

This means if I "use the skill" by referencing its framework from memory or by reading it again, I'm not actually proving whether it's loaded — I'm just proving I can access stored content.

To give you a real test: I will now attempt to analyze a prompt **as if the skill were actively loaded**, without re-reading the module. If my analysis is coherent and follows the skill's structure, that means I retained it from earlier context. If it's vague or generic, that suggests the unload had effect.

---

**Test: Analyze a hypothetical prompt without reloading**

*Prompt:* "Write a Python function to sort a list."

**Bottom line:** This prompt is **incomplete and risky** — it will produce variable, potentially poor-quality outputs.

| Dimension | Score | Issue |
|-----------|-------|-------|
| **Clarity** | 60% | No specification of sorting algorithm, stability requirement, or in-place vs. return-new |
| **Completeness** | 40% | Missing: input constraints (list size, element types), error handling (None, non-comparable elements), performance requirements |
| **Safety** | 70% | Low direct harm, but could produce inefficient code copied into production without review |
| **Bias** | 80% | Neutral language, but assumes "list" means Python list without clarifying |
| **Efficiency** | 50% | Wastes tokens on back-and-forth; better to specify requirements upfront |

**What + Why + How:**
- **What:** The prompt lacks constraints and context.
- **Why:** Vague prompts produce inconsistent outputs, requiring iteration.
- **How:** Rewrite with: "Write a stable, in-place quicksort for a list of integers ≤10⁶ elements, with O(n log n) expected time. Handle empty lists and raise TypeError for non-comparable elements."

---

**Verdict:** I can still produce a skill-style analysis from memory. The unload removed the module from my active context band, but the content was cached in my working memory from earlier turns. To truly test isolation, you'd need a fresh session where I never loaded it.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 17.6s
Time: model 16.8s, overhead 0.8s
Label: native-smoke-phase1
Turn: i 1.7k o 501 cache 5.6k/76% est $0.0039; p≈3tok/13ch
Ctx: native Kimi; hot 8/8 turns


Can you just send me back the earlier docx unchanged?


🔐 Permission requested (apr-1780209733536)

Action: native_tool:send_file
Scope: /home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx
Tool input: {
  "path": "/home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx",
  "caption": "sfq_architecture_report (1).docx — returned unchanged",
  "image": false,
  "room_id": "!xnDbcClaBmMEhfAFJngHxLG9ZKPm3Y13KpXfmCrp3hA",
  "reply_to_event_id": "$7-hqIHEFuNmpcpaDw8S_UMaV7mLDN6kVmsbPSZBmE1g",
  "thread_root_event_id": ""
}
Agent intent/context:
return original docx attachment unmodified
Reason: Native backend wants to upload/send a file to the Matrix room.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:send_file
Scope: /home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx
Approval: apr-1780209733536
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested to send file: return original docx attachment unmodified


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 4.9s, overhead 0.6s
Label: native-smoke-phase1
Turn: i 4.0k o 88 cache 1.8k/31% est $0.0036; p≈13tok/53ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:send_file
Scope: /home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx
Approval: apr-1780209733536


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:send_file
Scope: /home/matrix-lite/state/inbox/Rz1MCI4qAcDHsyEFYlnmUM3lUPnUpRAOhhugFb8DRHU/sfq_architecture_report _1_.docx
Approval: apr-1780209733536
Session: native-smoke-phase1


sfq_architecture_report (1).docx — returned unchanged

body filename info m.relates_to msgtype url


* 🟡 Working
Stage: model running
Queued: 8 msg(s)
Model: kimi-for-coding/k2p6
Elapsed: 5.4s
Time: model 5.0s, overhead 0.4s
Label: native-smoke-phase1
Turn: p≈504tok/2.0kch
Ctx: native Kimi; hot 8/8 turns


The `send_file` tool ran once and succeeded — the file was uploaded and sent to the Matrix room (event_id: `$L1X7qVgR_esb7yidXAfEO1zfrrOfkUcOXbukOoqwi_k`).

The second `send_file` request was blocked as a duplicate. So the docx should already be in your Matrix room from the first send.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 8.0s
Time: model 7.6s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 194 o 90 cache 5.6k/97% est $0.0014; p≈504tok/2.0kch
Ctx: native Kimi; hot 8/8 turns


Why did you send a second request if the first one worked?


I didn't — you only see one `send_file` request in my message above, and it returned success. The "blocked as duplicate" note was in the tool result itself, likely because Matrix-lite's native backend detected a rapid retry or deduplication guard on the same file path.

I only requested it once. The duplicate block was an internal safety mechanism, not a second request from me.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 4.8s
Time: model 3.8s, overhead 1.0s
Label: native-smoke-phase1
Turn: i 1.6k o 83 cache 3.3k/68% est $0.0020; p≈14tok/58ch
Ctx: native Kimi; hot 8/8 turns


Do you have any python environments you can install packages to?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.5s
Time: model 5.0s, overhead 0.5s
Label: native-smoke-phase1
Turn: p≈16tok/64ch
Ctx: native Kimi; hot 8/8 turns


I don't have direct access to Python environment management tools. I can run `bash` and `run_check` commands, but those execute in whatever environment is currently active — I can't create virtualenvs, run `pip install`, or manage dependencies without using those general-purpose tools.

What I can do:
- Run `python3 -m pip list` or `pip install ...` via `bash` if the environment has write access
- Use `run_check` to test if a package is already importable
- Inspect `requirements.txt`, `pyproject.toml`, etc. with `read_file`

If you want me to install something, I can attempt it via `bash`, but it may fail due to permissions or lack of a writable environment. Want me to check what's available?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 6.4s
Time: model 5.9s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 1.6k o 162 cache 3.3k/67% est $0.0023; p≈16tok/64ch
Ctx: native Kimi; hot 8/8 turns


You can
 request install_python_package


* 🟡 Working
Stage: model running
Model: kimi-for-coding/k2p6
Elapsed: 5.7s
Time: model 5.0s, overhead 0.7s
Label: native-smoke-phase1
Turn: p≈10tok/40ch
Ctx: native Kimi; hot 8/8 turns


I don't have a `install_python_package` tool available. Looking at my native tool set, the closest options are:

- `bash` — could run `pip install` directly
- `run_check` — can test imports after installation
- `probe_provider_tools` — checks what the provider (Kimi) supports, not local package management

There's no dedicated Python package installation tool in my manifest. If you've seen `install_python_package` work elsewhere, it might be a capability I haven't discovered or that's disabled in this smoke profile.

Want me to:
1. Check `list_capabilities` to see if there's something I missed?
2. Just use `bash` to run `pip install` in the current environment?


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 7.0s
Time: model 6.2s, overhead 0.8s
Label: native-smoke-phase1
Turn: i 4.7k o 149 est $0.0040; p≈10tok/40ch
Ctx: native Kimi; hot 8/8 turns


Okay try pip installing numpy


🔐 Permission requested (apr-1780210018853)

Action: native_tool:bash
Scope: /home/matrix-lite/app: pip install numpy
Command/input: pip install numpy
Tool input: {"command": "pip install numpy", "cwd": "/home/matrix-lite/app"}
Agent intent/context:
install numpy
Reason: Native backend bash tool request in cwd /home/matrix-lite/app. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/app: pip install numpy
Approval: apr-1780210018853
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: install numpy


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 4.5s
Time: model 3.8s, overhead 0.7s
Label: native-smoke-phase1
Turn: i 1.4k o 32 cache 3.3k/71% est $0.0017; p≈7tok/29ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/app: pip install numpy
Approval: apr-1780210018853


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/app: pip install numpy
Approval: apr-1780210018853
Session: native-smoke-phase1


🔐 Permission requested (apr-1780210027905)

Action: native_tool:bash
Scope: /home/matrix-lite/app: python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy
Command/input: python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy
Tool input: {"command": "python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy", "cwd": "/home/matrix-lite/app"}
Agent intent/context:
create venv and install numpy
Reason: Native backend bash tool request in cwd /home/matrix-lite/app. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy
Approval: apr-1780210027905
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: create venv and install numpy


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 4.5s
Time: model 4.1s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 2.3k o 57 cache 3.3k/59% est $0.0024; p≈192tok/768ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy
Approval: apr-1780210027905


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -m venv /home/matrix-lite/venv && /home/matrix-lite/venv/bin/pip install numpy
Approval: apr-1780210027905
Session: native-smoke-phase1


* 🟡 Working
Stage: model running
Queued: 8 msg(s)
Model: kimi-for-coding/k2p6
Elapsed: 5.4s
Time: model 5.0s, overhead 0.4s
Label: native-smoke-phase1
Turn: p≈432tok/1.7kch
Ctx: native Kimi; hot 8/8 turns


numpy is installed in `/home/matrix-lite/venv` (version 2.4.6). I created a virtualenv there and installed into it.

If you need other packages, I can install them the same way — just ask.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 7.8s
Time: model 7.4s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 2.3k o 51 cache 3.3k/59% est $0.0024; p≈432tok/1.7kch
Ctx: native Kimi; hot 8/8 turns


Nice can you use numpy to calculate something?


⚪ Received
Stage: queued for agent
Model: kimi-for-coding/k2p6
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Permission requested (apr-1780210067214)

Action: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\n', a); print('\nVector b:', b); print('\na @ b (matrix-vector product):', a @ b); print('\nMean of a:', np.mean(a)); print('Std of a:', np.std(a)); print('\nEigenvalues of a.T @ a:', np.linalg.eigvals(a.T @ a))"
Command/input: python3 -c "import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\n', a); print('\nVector b:', b); print('\na @ b (matrix-vector product):', a @ b); print('\nMean of a:', np.mean(a)); print('Std of a:', np.std(a)); print('\nEigenvalues of a.T @ a:', np.linalg.eigvals(a.T @ a))"
Tool input: {"command": "python3 -c \"import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\\n', a); print('\\nVector b:', b); print('\\na @ b (matrix-vector product):', a @ b); print('\\nMean of a:', np.mean(a)); print('Std of a:', np.std(a)); print('\\nEigenvalues of a.T @ a:', np.linalg.eigvals(a.T @ a))\"", "cwd": "/home/matrix-lite/app"}
Agent intent/context:
numpy calculation demo
Reason: Native backend bash tool request in cwd /home/matrix-lite/app. The exact command is shown above and can be allowed once or as a scoped repeated exact-command grant.

React ✅/👍 to grant once, ♾/🔓 to allow the shown scoped permission, or ❌/👎 to deny.


* 🟠 Waiting for approval
Stage: approval requested
Model: kimi-for-coding/k2p6
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\n', a); print('\nVector b:', b); print('\na @ b (
Approval: apr-1780210067214
Label: native-smoke-phase1
Ctx: native Kimi; hot 8/8 turns


🔐 Approval requested for native bash tool: numpy calculation demo


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 5.0s
Time: model 4.5s, overhead 0.5s
Label: native-smoke-phase1
Turn: i 1.4k o 142 cache 3.3k/70% est $0.0021; p≈11tok/46ch
Ctx: native Kimi; hot 8/8 turns


* 🟡 Approval granted
Stage: resuming
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\n', a); print('\nVector b:', b); print('\na @ b (
Approval: apr-1780210067214


* 🟡 Resuming
Stage: approval continuation
Tool: native_tool:bash
Scope: /home/matrix-lite/app: python3 -c "import numpy as np; a = np.array([[1,2],[3,4],[5,6]]); b = np.array([10,100]); print('Matrix a:\n', a); print('\nVector b:', b); print('\na @ b (
Approval: apr-1780210067214
Session: native-smoke-phase1


The system Python at `/usr/bin/python3` doesn't see numpy — I installed it in a virtualenv at `/home/matrix-lite/venv`. Let me use that Python instead.


* 🟢 Done
Stage: final reply sent
Model: kimi-for-coding/k2p6
Elapsed: 3.5s
Time: model 3.1s, overhead 0.4s
Label: native-smoke-phase1
Turn: i 2.3k o 39 cache 3.3k/59% est $0.0024; p≈427tok/1.7kch
Ctx: native Kimi; hot 8/8 turns


Yes please