Here are the 5 native subagent profiles available:

| Profile | Title | Description | Timeout | Max Rounds |
|---------|-------|-------------|---------|------------|
| **debug** | Read-only debugger | Investigate root causes from supplied symptoms/logs/code without edits | 60s sync / 1200s async | 2 |
| **explore** | Read-only explorer | Map files, symbols, context, and factual repo/runtime state | 45s sync / 900s async | 2 |
| **research** | Read-only research | Use allowed MCP/search/doc capabilities for bounded research | 60s sync / 1200s async | 2 |
| **review** | Read-only reviewer | Skeptical code/design review using provided diff and check evidence | 45s sync / 900s async | 1 |
| **test-runner** | Deterministic check runner | Run/check deterministic validation only through approved native check tools | 45s sync / 1200s async | 1 |

All profiles are **read-only** — no writes, installs, root access, or subagent spawning. They run under the `native-smoke` model profile with restricted tool sets.

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
Here are the 5 native subagent profiles available:

| Profile | Title | Description | Timeout | Max Rounds |
|---------|-------|-------------|---------|------------|
| **debug** | Read-only debugger | Investigate root causes from supplied symptoms/logs/code without edits | 60s sync / 1200s async | 2 |
| **explore** | Read-only explorer | Map files, symbols, context, and factual repo/runtime state | 45s sync / 900s async | 2 |
| **research** | Read-only research | Use allowed MCP/search/doc capabilities for bounded research | 60s sync / 1200s async | 2 |
| **review** | Read-only reviewer | Skeptical code/design review using provided diff and check evidence | 45s sync / 900s async | 1 |
| **test-runner** | Deterministic check runner | Run/check deterministic validation only through approved native check tools | 45s sync / 1200s async | 1 |

All profiles are **read-only** — no writes, installs, root access, or subagent spawning. They run under the `native-smoke` model profile with restricted tool sets.

Investigate root causes from supplied symptoms/logs/code without edits.

Read-only debugger

Map files, symbols, context, and factual repo/runtime state.

Read-only explorer

Use allowed MCP/search/doc capabilities for bounded research.

Read-only research

Skeptical code/design review using provided diff and check evidence.

Read-only reviewer

Run/check deterministic validation only through approved native check tools.

Deterministic check runner


The explore subagent ran but **failed to read any files** — all `read_file` calls were denied because the paths were outside allowed roots (`/home` and `/home/matrix-lite`). The subagent attempted to read `.`, `README.md`, `package.json`, and `Cargo.toml` but hit sandbox restrictions.

So I don't have new information about matrix-lite from the subagent. What I know from my loaded context modules:

- **Matrix-lite** owns this backend's memory/context
- It's a **provider-native experimental backend** (I'm "Matrix-lite native-smoke-agent")
- There are **native tools** for file ops, context modules, capabilities, subagents, etc.
- It has **MCP proxy capabilities** (14 total, 9 enabled) like web search, code docs, browser automation
- It runs **subagents** with restricted read-only profiles under sandboxed paths

If you want me to try the explore subagent again with a specific allowed path, or use my own `list_directory`/`read_file` tools on a known location, let me know.

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
The explore subagent ran but **failed to read any files** — all `read_file` calls were denied because the paths were outside allowed roots (`/home` and `/home/matrix-lite`). The subagent attempted to read `.`, `README.md`, `package.json`, and `Cargo.toml` but hit sandbox restrictions.

So I don't have new information about matrix-lite from the subagent. What I know from my loaded context modules:

- **Matrix-lite** owns this backend's memory/context
- It's a **provider-native experimental backend** (I'm "Matrix-lite native-smoke-agent")
- There are **native tools** for file ops, context modules, capabilities, subagents, etc.
- It has **MCP proxy capabilities** (14 total, 9 enabled) like web search, code docs, browser automation
- It runs **subagents** with restricted read-only profiles under sandboxed paths

If you want me to try the explore subagent again with a specific allowed path, or use my own `list_directory`/`read_file` tools on a known location, let me know.

Output: {"tool_calls":[{"name":"read_file","args":{"path": "."}},{"name":"read_file","args":{"path": "README.md"}},{"name":"read_file","args":{"path": "package.json"}},{"name":"read_file","args":{"path": "Cargo.toml"}}]}

native read path outside allowed roots: /home/matrix-lite

Can you use explore subagent to find out more about matrix-lite?


I already have the directory listing for `/home/matrix-lite/app`. Here's what's there:

| Name | Type | Size |
|------|------|------|
| `.gitignore` | file | 108 B |
| `.pytest_cache` | dir | — |
| `AGENTS.md` | file | 4.1 KB |
| `README.md` | file | 521 B |
| `agent_memory` | dir | — |
| `bin` | dir | — |
| `config` | dir | — |
| `docs` | dir | — |
| `matrix_lite` | dir | — |
| `patches` | dir | — |
| `prompts` | dir | — |
| `scripts` | dir | — |
| `systemd` | dir | — |
| `tests` | dir | — |

Want me to read `README.md` or `AGENTS.md` to learn more about matrix-lite?

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
I already have the directory listing for `/home/matrix-lite/app`. Here's what's there:

| Name | Type | Size |
|------|------|------|
| `.gitignore` | file | 108 B |
| `.pytest_cache` | dir | — |
| `AGENTS.md` | file | 4.1 KB |
| `README.md` | file | 521 B |
| `agent_memory` | dir | — |
| `bin` | dir | — |
| `config` | dir | — |
| `docs` | dir | — |
| `matrix_lite` | dir | — |
| `patches` | dir | — |
| `prompts` | dir | — |
| `scripts` | dir | — |
| `systemd` | dir | — |
| `tests` | dir | — |

Want me to read `README.md` or `AGENTS.md` to learn more about matrix-lite?


The explore subagent ran successfully on `/home/matrix-lite/app` and found extensive information about Matrix-lite. Here's what it discovered:

## What Matrix-lite Is

**Matrix-lite** is a standalone Matrix conversation runtime that bridges Matrix/Telegram chat messages to LLM backends (ACPX/OpenCode). It runs as a Python daemon on a Linux server.

## Key Components

| Component | Role |
|-----------|------|
| **daemon.py** | Main Python daemon — syncs Matrix rooms, builds prompts, manages ACPX sessions, handles replies, permissions, timers, subagents |
| **ACPX** | CLI wrapper (`npx acpx`) that manages LLM backend sessions via Agent Client Protocol (ACP) over stdio/NDJSON |
| **OpenCode** | The actual LLM backend — sends prompts to model APIs (e.g. `kimi-for-coding/k2p6`), handles tool calling, session compaction |
| **Direct ACP Client** | Node.js script that bypasses ACPX for smoke-agent and persistent-chat; supports session resume for persistent continuity |

## Architecture Highlights

- **Persistent-chat** uses **OpenCode logical session resume** — `direct_acp_session_id` is stored in `/home/matrix-lite/state/conversations.json`, so systemd restarts don't wipe conversation memory
- **Smoke-agent** (what I am) is **fresh-per-turn** — no session persistence
- **MCP servers** provide tools: `matrix_lite_status`, `persistent_chat`, `persistent_chat_repo`, web search, browser, code docs, etc.
- **Permission modes**: `auto-once`, `auto-always`, `reject`, `stdin`, or Matrix same-room approval

## File Layout (from AGENTS.md)

| Path | Purpose |
|------|---------|
| `/home/matrix-lite/app` | App/runtime code |
| `/home/matrix-lite/config/matrix-lite.json` | Live config |
| `/home/matrix-lite/secrets/matrix-lite.env` | Secrets (never in repo) |
| `/home/matrix-lite/state` | Runtime state/logs |
| `/home/matrix-lite/workspace` | Workspace |
| `/home/matrix-lite/pages` | Pages root |

## Current Profiles

- `smoke-agent` — reloadable test room worker (no heartbeat/reflection/memory promotion)
- `persistent-chat` — main persistent chat
- `topic-chat` — secondary/topic rooms

## Validation Rules

After runtime changes, check:
1. `python3 -m py_compile matrix_lite/daemon.py`
2. `systemctl is-active matrix-lite.service`
3. Persistent-chat maintenance timers when relevant
4. Smoke Matrix turn only when behavior changed

The subagent also attempted to read deeper into `daemon.py` and `architecture.md` but hit truncation limits — there's more detail available if you want me to read specific sections directly.

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
The explore subagent ran successfully on `/home/matrix-lite/app` and found extensive information about Matrix-lite. Here's what it discovered:

## What Matrix-lite Is

**Matrix-lite** is a standalone Matrix conversation runtime that bridges Matrix/Telegram chat messages to LLM backends (ACPX/OpenCode). It runs as a Python daemon on a Linux server.

## Key Components

| Component | Role |
|-----------|------|
| **daemon.py** | Main Python daemon — syncs Matrix rooms, builds prompts, manages ACPX sessions, handles replies, permissions, timers, subagents |
| **ACPX** | CLI wrapper (`npx acpx`) that manages LLM backend sessions via Agent Client Protocol (ACP) over stdio/NDJSON |
| **OpenCode** | The actual LLM backend — sends prompts to model APIs (e.g. `kimi-for-coding/k2p6`), handles tool calling, session compaction |
| **Direct ACP Client** | Node.js script that bypasses ACPX for smoke-agent and persistent-chat; supports session resume for persistent continuity |

## Architecture Highlights

- **Persistent-chat** uses **OpenCode logical session resume** — `direct_acp_session_id` is stored in `/home/matrix-lite/state/conversations.json`, so systemd restarts don't wipe conversation memory
- **Smoke-agent** (what I am) is **fresh-per-turn** — no session persistence
- **MCP servers** provide tools: `matrix_lite_status`, `persistent_chat`, `persistent_chat_repo`, web search, browser, code docs, etc.
- **Permission modes**: `auto-once`, `auto-always`, `reject`, `stdin`, or Matrix same-room approval

## File Layout (from AGENTS.md)

| Path | Purpose |
|------|---------|
| `/home/matrix-lite/app` | App/runtime code |
| `/home/matrix-lite/config/matrix-lite.json` | Live config |
| `/home/matrix-lite/secrets/matrix-lite.env` | Secrets (never in repo) |
| `/home/matrix-lite/state` | Runtime state/logs |
| `/home/matrix-lite/workspace` | Workspace |
| `/home/matrix-lite/pages` | Pages root |

## Current Profiles

- `smoke-agent` — reloadable test room worker (no heartbeat/reflection/memory promotion)
- `persistent-chat` — main persistent chat
- `topic-chat` — secondary/topic rooms

## Validation Rules

After runtime changes, check:
1. `python3 -m py_compile matrix_lite/daemon.py`
2. `systemctl is-active matrix-lite.service`
3. Persistent-chat maintenance timers when relevant
4. Smoke Matrix turn only when behavior changed

The subagent also attempted to read deeper into `daemon.py` and `architecture.md` but hit truncation limits — there's more detail available if you want me to read specific sections directly.

1: # Matrix-lite
2:
3: Standalone Matrix conversation runtime for ACPX/OpenCode-backed agents.
4:
5: Authoritative live config is `/home/matrix-lite/config/matrix-lite.json`; repo snapshots live under `config/`. Secrets are kept
6: out of the repo tree in `/home/matrix-lite/secrets/matrix-lite.env`.
7:
8: Current profiles:
9:
10: - `smoke-agent`: reloadable test room worker, no heartbeat/reflection/memory promotion.
11: - `persistent-chat`: target profile for main persistent chat cutover.
12: - `topic-chat`: target profile for secondary/topic rooms.

1: # Matrix-lite Agent Instructions
2:
3: Matrix-lite is the active Matrix runtime for ACPX/OpenCode-backed agents. Keep OpenClaw references only when they name imported legacy data or compatibility interfaces that have not been renamed yet.
4:
5: ## Layout
6:
7: - App/runtime code: `/home/matrix-lite/app`
8: - Live config: `/home/matrix-lite/config/matrix-lite.json`
9: - Secret env: `/home/matrix-lite/secrets/matrix-lite.env` (never copy into the repo)
10: - ACPX config: `/home/matrix-lite/.acpx/config.json`
11: - Runtime state/logs: `/home/matrix-lite/state`
12: - Persistent-chat state: `/home/matrix-lite/state/persistent-chat`
13: - Workspace: `/home/matrix-lite/workspace`
14: - Pages root: `/home/matrix-lite/pages`
15:
16: ## Operating rules
17:
18: - Prefer the thinnest safe change; do not reintroduce OpenClaw as an active Matrix owner.
19: - Treat `/home/matrix-lite/config`, `/home/matrix-lite/.acpx`, systemd units, and `/home/matrix-lite/bin` as live runtime inputs. Use the sync scripts before/after editing repo copies.
20: - Do not commit or copy secrets, Matrix access tokens, caches, `__pycache__`, or ACPX session streams into a shareable repo snapshot.
21: - Keep persistent-chat continuity file-backed: preserve perpetual logs, summaries, pinned notes, and legacy session archives.
22: - For persistent-chat-specific behavior, update persistent-chat-owned prompt/state files rather than encoding those rules as repo-wide guidance for all agents.
23: - Do not delete migrated OpenClaw reference data unless Gleb explicitly asks.
24: - For checks, prefer bounded commands and inspect service status/logs before restarting services.
25: - Restart `matrix-lite.service` only when a code/config change requires it.
26: - Preserve the live persistent-chat ACPX backend session by default. The timestamped session label is a lifecycle/readability indicator and should normally map 1:1 to the backend ACPX session; do not silently swap a new backend under an old label. Do not create/close/reload/replace a backend to hide wrapper bugs, status-only ACPX output, incomplete prompts, or zero-token turns; first expose debug evidence and fix the wrapper/recovery path. Valid replacement reasons are explicit user/implementation-agent reload after a real dead session/outage/major tested change, or persistent-chat/context-limit reload when instructed. Any backend replacement/close/reload must be surfaced to the user and future agents with Matrix restart panels plus durable state/log records containing timestamp, reason, old/new labels, backend ids, and authoritative context usage when available.
27:
28: ## Architecture documentation
29:
30: The canonical architecture reference is `/home/matrix-lite/app/docs/architecture.md`. It describes components, data flow, key paths, and known issues in human-readable form. Keep it updated whenever:
31:
32: - A new component or agent is added
33: - A config path or data flow changes
34: - A significant fix or workaround is deployed
35: - A known issue is resolved or a new one is discovered
36:
37: The file must stay human-readable, correct, and free of secrets. When in doubt about whether to update it, err on the side of updating.
38:
39: ## Validation checklist
40:
41: After runtime-affecting changes, check at least:
42:
43: 1. `python3 -m py_compile /home/matrix-lite/app/matrix_lite/daemon.py` for daemon edits.
44: 2. `systemctl is-active matrix-lite.service` for service health.
45: 3. `systemctl list-timers matrix-lite-persistent-chat-*` for persistent maintenance timers when relevant.
46: 4. A smoke Matrix turn only when behavior changed or Gleb asks.
47:
48: ## Persistent memory
49:
50: Use `agent_memory/` for project-specific reusable findings and engineering history. Start with `agent_memory/README.md` for file roles. In brief:
51:
52: - `agent_memory/findings.md`: durable conclusions, quirks, root causes, and gotchas.
53: - `agent_memory/session-log.md`: chronological cutover/operations/change log for non-trivial live or architectural work.
54:
55: Add a short entry after non-trivial investigations, cutover decisions, failed approaches, environment quirks, live migrations, backend/session policy changes, or OpenCode/runtime update decisions. Do not store secrets or raw token-bearing config.

tool not allowed for native sync subagent

not a file: /home/matrix-lite/app/matrix_lite/__init__.py

1: from __future__ import annotations
2:
3: import argparse
4: import asyncio
5: import html
6: import hashlib
7: import fcntl
8: import json
9: import mimetypes
10: import os
11: import posixpath
12: import queue
13: import re
14: import select
15: import shlex
16: import signal
17: import shutil
18: import sqlite3
19: import subprocess
20: import sys
21: import threading
22: import tempfile
23: import time
24: import uuid
25: from contextlib import contextmanager
26: from datetime import datetime, timedelta, timezone
27: from pathlib import Path
28: from zoneinfo import ZoneInfo
29: from typing import Any
30: from urllib import parse, request
31: from urllib.parse import quote
32: from urllib.error import HTTPError, URLError
33:
34:
35: MATRIX_LITE_CONFIG_FILE = Path(
36:     os.environ.get(
37:         "MATRIX_LITE_CONFIG", "/home/matrix-lite/config/matrix-lite.json"
38:     )
39: )
40:
41:
42: def _read_boot_config() -> dict[str, Any]:
43:     try:
44:         data = json.loads(MATRIX_LITE_CONFIG_FILE.read_text(encoding="utf-8"))
45:     except Exception:
46:         return {}
47:     return data if isinstance(data, dict) else {}
48:
49:
50: BOOT_CONFIG = _read_boot_config()
51:
52:
53: def _boot_path(key: str, default: str) -> str:
54:     value = BOOT_CONFIG.get(key)
55:     return str(value) if value else default
56:
57:
58: def _boot_agent_config(agent_id: str) -> dict[str, Any]:
59:     agents = (
60:         BOOT_CONFIG.get("agents") if isinstance(BOOT_CONFIG.get("agents"), dict) else {}
61:     )
62:     agent = agents.get(agent_id) if isinstance(agents, dict) else None
63:     return agent if isinstance(agent, dict) else {}
64:
65:
66: STATE_DIR = Path(
67:     os.environ.get(
68:         "MATRIX_LITE_STATE_DIR",
69:         os.environ.get(
70:             "OPENCLAW_MATRIX_LITE_STATE_DIR",
71:             _boot_path("state_dir", "/home/matrix-lite/state"),
72:         ),
73:     )
74: )
75: ROOM_MAP_FILE = Path(
76:     os.environ.get("OPENCLAW_MATRIX_LITE_ROOM_MAP", str(STATE_DIR / "rooms.json"))
77: )
78: SYNC_STATE_FILE = Path(
79:     os.environ.get("OPENCLAW_MATRIX_LITE_SYNC_STATE", str(STATE_DIR / "sync.json"))
80: )
81: EVENT_LOG_FILE = Path(
82:     os.environ.get("OPENCLAW_MATRIX_LITE_EVENT_LOG", str(STATE_DIR / "events.jsonl"))
83: )
84: CONVERSATION_REGISTRY_FILE = Path(
85:     os.environ.get(
86:         "OPENCLAW_MATRIX_LITE_CONVERSATIONS",
87:         str(STATE_DIR / "conversations.json"),
88:     )
89: )
90: REPLY_LOG_FILE = Path(
91:     os.environ.get("OPENCLAW_MATRIX_LITE_REPLY_LOG", str(STATE_DIR / "replies.jsonl"))
92: )
93: TURN_LOG_FILE = Path(
94:     os.environ.get("OPENCLAW_MATRIX_LITE_TURN_LOG", str(STATE_DIR / "turns.jsonl"))
95: )
96: NATIVE_KIMI_SESSION_FILE = Path(
97:     os.environ.get("MATRIX_LITE_NATIVE_KIMI_SESSION_FILE", str(STATE_DIR / "native-kimi-sessions.json"))
98: )
99: NATIVE_KIMI_RAW_LOG_FILE = Path(
100:     os.environ.get("MATRIX_LITE_NATIVE_KIMI_RAW_LOG", str(STATE_DIR / "native-kimi-raw-log.jsonl"))
101: )
102: NATIVE_KIMI_MODEL_LOG_FILE = Path(
103:     os.environ.get("MATRIX_LITE_NATIVE_KIMI_MODEL_LOG", str(STATE_DIR / "native-kimi-model-log.jsonl"))
104: )
105: NATIVE_KIMI_SUMMARY_FILE = Path(
106:     os.environ.get("MATRIX_LITE_NATIVE_KIMI_SUMMARY_FILE", str(STATE_DIR / "native-kimi-summary.json"))
107: )
108: NATIVE_CONTEXT_MODULES_FILE = Path(
109:     os.environ.get("MATRIX_LITE_NATIVE_CONTEXT_MODULES", str(STATE_DIR / "native-context-modules.json"))
110: )
111: NATIVE_CONTEXT_MODULE_AUDIT_FILE = Path(
112:     os.environ.get("MATRIX_LITE_NATIVE_CONTEXT_MODULE_AUDIT", str(STATE_DIR / "native-context-module-audit.jsonl"))
113: )
114: NATIVE_CONTEXT_MODULES_LOCK = threading.RLock()
115:
116: OPENCODE_DB_FILE = Path(
117:     os.environ.get(
118:         "MATRIX_LITE_OPENCODE_DB_FILE",
119:         f"/home/matrix-lite/.local/share/opencode/{os.environ.get('OPENCODE_DB', 'opencode-matrix-lite.db')}",
120:     )
121: )
122: APPROVALS_FILE = Path(
123:     os.environ.get("OPENCLAW_MATRIX_LITE_APPROVALS", str(STATE_DIR / "approvals.json"))
124: )
125: NATIVE_TOOL_GRANTS_FILE = Path(
126:     os.environ.get("MATRIX_LITE_NATIVE_TOOL_GRANTS", str(STATE_DIR / "native-tool-grants.json"))
127: )
128: NATIVE_PROVIDER_CAPS_FILE = Path(
129:     os.environ.get("MATRIX_LITE_NATIVE_PROVIDER_CAPS", str(STATE_DIR / "native-provider-capabilities.json"))
130: )
131: STANDING_PERMISSIONS_FILE = Path(
132:     os.environ.get(
133:         "OPENCLAW_MATRIX_LITE_STANDING_PERMISSIONS",
134:         str(STATE_DIR / "standing-permissions.json"),
135:     )
136: )
137: TIMERS_FILE = Path(
138:     os.environ.get("OPENCLAW_MATRIX_LITE_TIMERS", "/home/matrix-lite/workspace/state/timers/timers.json")
139: )
140: MEDIA_LOG_FILE = Path(
141:     os.environ.get("OPENCLAW_MATRIX_LITE_MEDIA_LOG", str(STATE_DIR / "media.jsonl"))
142: )
143: INBOX_DIR = Path(
144:     os.environ.get("OPENCLAW_MATRIX_LITE_INBOX_DIR", str(STATE_DIR / "inbox"))
145: )
146: OPENCODE_SUBAGENT_CONFIG_FILE = Path(
147:     os.environ.get(
148:         "MATRIX_LITE_SUBAGENT_OPENCODE_CONFIG",
149:         os.environ.get(
150:             "OPENCLAW_MATRIX_LITE_SUBAGENT_OPENCODE_CONFIG",
151:             str(
152:                 _boot_agent_config("smoke-agent").get(
153:                     "opencode_config",
154:                     "/home/matrix-lite/xdg/smoke-agent/opencode/opencode.json",
155:                 )
156:             ),
157:         ),
158:     )
159: )
160: OPENCODE_PERSISTENT_CHAT_CONFIG_FILE = Path(
161:     os.environ.get(
162:         "MATRIX_LITE_PERSISTENT_CHAT_OPENCODE_CONFIG",
163:         os.environ.get(
164:             "OPENCLAW_MATRIX_LITE_PERSISTENT_CHAT_OPENCODE_CONFIG",
165:             str(
166:                 _boot_agent_config("persistent-chat").get(
167:                     "opencode_config",
168:                     "/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json",
169:                 )
170:             ),
171:         ),
172:     )
173: )
174: ACPX_CONFIG_FILE = Path(os.environ.get("MATRIX_LITE_ACPX_CONFIG", "/home/matrix-lite/.acpx/config.json"))
175: ACPX_SESSIONS_DIR = Path(os.environ.get("MATRIX_LITE_ACPX_SESSIONS_DIR", "/home/matrix-lite/.acpx/sessions"))
176: OBSERVABILITY_LOG_FILE = Path(
177:     os.environ.get(
178:         "OPENCLAW_MATRIX_LITE_OBSERVABILITY_LOG",
179:         str(STATE_DIR / "observability.jsonl"),
180:     )
181: )
182: MESSAGE_STATUS_DIR = Path(
183:     os.environ.get(
184:         "OPENCLAW_MATRIX_LITE_MESSAGE_STATUS_DIR",
185:         str(STATE_DIR / "messages"),
186:     )
187: )
188: LOCK_DIR = Path(
189:     os.environ.get("OPENCLAW_MATRIX_LITE_LOCK_DIR", str(STATE_DIR / "locks"))
190: )
191: QUEUE_DIR = Path(
192:     os.environ.get("OPENCLAW_MATRIX_LITE_QUEUE_DIR", str(STATE_DIR / "queues"))
193: )
194: PAGES_DIR = Path(
195:     os.environ.get(
196:         "MATRIX_LITE_PAGES_DIR",
197:         str(BOOT_CONFIG.get("pages_dir") or "/home/matrix-lite/pages"),
198:     )
199: )
200: WORKSPACE_DIR = Path(

1: # Matrix-lite Architecture
2:
3: Last updated: 2026-05-23
4: Maintainer: Persistent Assistant (update this file when architecture changes)
5:
6: ## Overview
7:
8: Matrix-lite is a chat bot that bridges Matrix/Telegram messages to LLM backends via ACPX/OpenCode. It runs as a Python daemon (daemon.py) with various agent scripts.
9:
10: ## Components
11:
12: ### Matrix-lite Daemon (daemon.py)
13: A Python script at /home/matrix-lite/app/matrix_lite/daemon.py that runs as a systemd service. It syncs Matrix rooms, maintains conversation state, builds prompts, runs ACPX sessions, and handles replies. Key responsibilities:
14: - Room sync and event processing
15: - Prompt construction from conversation state, memory packets, character card
16: - ACPX session lifecycle management
17: - Assistant text extraction from ACPX output
18: - Permission management and approvals
19: - Timer/reminder system
20: - Subagent spawning for background tasks
21:
22: ### ACPX
23: A CLI wrapper (npx acpx) that manages LLM backend sessions. Located in npx cache at /home/matrix-lite/.npm/_npx/*/acpx/. It:
24: - Spawns the OpenCode agent as a child process
25: - Communicates via the Agent Client Protocol (ACP) over stdio using NDJSON
26: - Handles session creation, loading, and cancellation
27: - Routes tool calls between the agent and MCP servers
28: - Manages permissions and file system access
29: - Streams session events to disk as .stream.ndjson files
30:
31: ### OpenCode
32: The actual LLM backend agent. Communicates with ACPX via ACP protocol. It:
33: - Receives prompts via ACP session/prompt messages
34: - Sends them to the configured model API (e.g. kimi-for-coding/k2p6)
35: - Streams back agent_message, agent_message_chunk, usage_update events
36: - Handles tool calling: when the model requests a tool, OpenCode sends the request through ACP, gets the result, and feeds it back to the model
37: - Performs session compaction when context grows too large
38:
39:
40: ### Direct ACP OpenCode client
41:
42: `/home/matrix-lite/app/scripts/matrix-lite-direct-acp-client.mjs` is the direct OpenCode ACP client for smoke-agent and the main persistent-chat conversational backend. It bypasses ACPX, launches `/home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite acp`, emits JSONL events, assembles final text from `agent_message_chunk`, loads enabled MCP servers from the selected XDG profile, and can answer native `session/request_permission` prompts with `auto-once`, `auto-always`, `reject`, `stdin`, or Matrix same-room approval mode. `persistent:main` now uses `agents.persistent-chat.backend = direct_acp`, `direct_acp_profile = persistent-chat`, and `direct_acp_permission_mode = matrix`; smoke-agent phase 1 also uses direct ACP. Heartbeat/reflection/synthesis maintenance remains on separate scheduled/noninteractive paths rather than this interactive main-chat direct ACP turn path. Direct ACP allow-always is not durable across fresh sessions yet, so same-room native grants are active-turn grants; root/sudo work uses the separate one-shot `privileged_broker_sudo_run` MCP tool.
43:
44: For persistent-chat, direct ACP uses **OpenCode logical session resume** rather than fresh-per-turn memory only. The daemon stores `direct_acp_session_id` in `/home/matrix-lite/state/conversations.json`; on each persistent turn it passes that id to the client with `--resume-session-id`, and the client calls OpenCode `loadSession({ sessionId, cwd, mcpServers })`. The OS process, MCP processes, and ACP transport are still fresh each turn, but ordinary conversation/tool continuity comes from the persisted OpenCode session. Smoke-agent remains fresh-per-turn.
45:
46: This makes systemd restarts cheap: restarting `matrix-lite.service` does not clear `direct_acp_session_id` and therefore does not normally reset persistent-chat recall. It does interrupt any active in-flight turn or in-memory native permission wait. A **logical backend/session restart** is separate: `persistent-chat-reload` clears `direct_acp_session_id`, records reload state, and the next turn starts a new OpenCode logical session from Matrix-lite pinned state and handoff artifacts.
47:
48: OpenCode updates are treated as potentially compatible but risky migrations. Default policy is to preserve `direct_acp_session_id` across an OpenCode binary update, restart Matrix-lite, then smoke-test `loadSession` with the existing session id. If `loadSession` works, keep the session and record the migration. If OpenCode cannot load the session, behavior changes badly, or an update touches ACP/session/compaction/storage code, fall back to a logical direct-ACP reset after refreshing heartbeat and handoff. This is especially important because OpenCode session storage schema/database layout may change across updates; Matrix-lite must not assume the old session db/files are loadable by the new binary without verification.
49:
50: Smoke examples:
51:
52: ```bash
53: sudo -u matrix-lite scripts/matrix-lite-direct-acp-client.mjs --profile smoke-agent --permission-mode reject --prompt 'Say hello'
54: sudo -u matrix-lite scripts/matrix-lite-direct-acp-client.mjs --profile smoke-agent --permission-mode auto-once --prompt 'Use your read tool to read /etc/fstab, then summarize it.'
55: ```
56:
57: ### MCP Servers
58: Tools available to the model, defined in opencode config at /home/matrix-lite/app/config/opencode/persistent-chat.opencode.json:
59: - matrix_lite_status: status/capability/debug tools (always on)
60: - persistent_chat: memory helpers and local semantic search over persistent memory (always on)
61: - persistent_chat_repo: repo search (always on)
62: - matrix_lite_extended_tools: search, browser, vision, approvals and other proxied rare tools (lazy-loaded)
63: - privileged_broker: one-shot same-room approval path for exact sudo commands needed by interactive direct ACP turns
64: - approval_policy defers pending-timeout and grant wakeups for unattended model-heartbeat/operational-synthesis/test-runner requests; later background turns list/check approvals and act only after a grant.
65: - Directly disabled in persistent-chat config: playwright, approval_policy, duckduckgo, searxng, brave-search, dezgo_image, matrix_lite_vision, telegram_media, skill_workshop, matrix_bridge. Many rare capabilities remain reachable through matrix_lite_extended_tools.
66:
67: ## Data Flow
68:
69: 1. User sends message in Matrix/Telegram
70: 2. Daemon receives event, builds prompt from conversation state plus memory packets plus character card plus turn packet
71: 3. For `backend=direct_acp`, daemon writes a prompt file and runs `matrix-lite-direct-acp-client.mjs --profile ... --permission-mode matrix`; for legacy/scheduled ACPX paths it still uses `npx acpx ... prompt -s label`.
72: 4. Direct ACP starts a fresh OpenCode ACP child process for the turn and loads enabled MCP servers from the selected XDG profile. For persistent-chat it normally `loadSession`s the stored OpenCode session id; for smoke-agent it starts a new session.
73: 5. OpenCode sends prompt to LLM API and streams JSONL session updates (`agent_message_chunk`, `tool_call_update`, `usage_update`, `done`).
74: 6. If native ACP requests permission, Matrix-lite posts a same-room approval panel and feeds the selected option back to the waiting child when the owner reacts.
75: 7. If root/sudo is needed, the model uses `privileged_broker_sudo_run`; the root helper verifies the exact same-room approval, command text, command SHA256, owner reaction, and one-shot use.
76: 8. LLM finishes with `end_turn`; daemon uses the direct ACP `done.text` as plain user-visible reply. Legacy ACPX paths still parse/strip Matrix-lite final tags.
77: 9. Daemon sends reply back to Matrix/Telegram.
78:
79: ## Key File Paths
80:
81: Session state:
82: - /home/matrix-lite/.acpx/sessions/id.stream.ndjson - full NDJSON log
83: - /home/matrix-lite/.acpx/sessions/index.json - session ID mapping
84: - /home/matrix-lite/.acpx/config.json - ACPX config
85:
86: OpenCode config:
87: - /home/matrix-lite/app/config/opencode/persistent-chat.opencode.json
88: - /home/matrix-lite/app/config/opencode/smoke-agent-temp01.opencode.json (temperature-routed subagent profile)
89: - /home/matrix-lite/app/config/opencode/smoke-agent-temp02.opencode.json (temperature-routed subagent profile)
90: - /home/matrix-lite/app/config/opencode/smoke-agent-temp05.opencode.json (temperature-routed subagent profile)
91: - /home/matrix-lite/xdg/persistent-chat/opencode/opencode.json
92:
93: Boot config:
94: - /home/matrix-lite/config/matrix-lite.json
95:
96: State files:
97: - /home/matrix-lite/state/persistent-chat/ - memory, decisions, failures, packets, curated memory
98: - /home/matrix-lite/state/persistent-chat/memory-search-index.sqlite3 - persistent_chat chunk/embedding index; default memory semantic search uses local BAAI/bge-small-en-v1.5 embeddings and excludes duplicate snapshot archives from the default source alias
99: - /home/matrix-lite/state/persistent-chat/pinned-state.md - generated compact control/recovery view injected in normal persistent-chat turns when no substantive turn packet exists
100: - /home/matrix-lite/state/persistent-chat/pre-reload-handoff.md - source handoff artifact used before logical direct-ACP session reset or risky backend/OpenCode updates
101: - /home/matrix-lite/state/persistent-chat/context-guard.json - context guard telemetry and stage for persistent-chat, including direct-ACP usable-window policy
102: - /home/matrix-lite/state/rooms.json - room mappings
103: - /home/matrix-lite/state/conversations.json - conversation registry
104: - /home/matrix-lite/state/approvals.json - pending approvals
105:
106:
107: ## Persistent-chat continuity model
108:
109: Persistent-chat continuity is layered. Do not rely on only one layer.
110:
111: 1. **OpenCode logical session**: `loadSession(sessionId)` provides ordinary conversational and tool history. OpenCode compaction is configured off for persistent-chat, but OpenCode may still prune or fail if context/storage limits are hit.
112: 2. **Pinned state**: `pinned-state.md` is generated from existing source artifacts. It is the always-injected control plane: runtime truth, operating contract, active decisions, reload/handoff state, fresh notices/actions, failure/permission guardrails, high-signal persona facts, timers, and source pointers. It is not a transcript and should stay compact/model-friendly.
113: 3. **Turn packet**: `turn-packet.md` is used only when substantive active-task state exists. If it is empty/header-only, pinned state is injected instead.
114: 4. **Semantic/search recall**: detailed facts should be searched rather than pinned wholesale. The daemon performs bounded lexical retrieval from pinned/curated/topic/decision artifacts, and the model can call persistent memory tools for deeper semantic search.
115: 5. **Reload handoff**: before a logical reset or risky OpenCode update, the main agent/heartbeat should write a targeted handoff source artifact and regenerate packets. The new session starts from pinned state + handoff rather than raw transcript tail.
116:
117: Generated packet/status artifacts (`pinned-state.md/json`, session/turn/restore packets, manifest, context guard) are generated outputs and should not be directly edited by the model. The main agent may edit source artifacts when explicitly needed; heartbeat then regenerates the compiled views.
118:
119: ## Context and compaction semantics
120:
121: OpenCode's context percentage is not a cumulative memory meter. Source inspection showed the UI computes it from the **latest assistant message with token usage**:
122:
123: ```text
124: total = input + output + reasoning + cache.read + cache.write
125: usage% = round(total / model_context_limit * 100)
126: ```
127:
128: Therefore the displayed percent can go down without forgetting or compaction if a later turn has smaller input/cache/output usage or different overhead. It should be read as latest model-call pressure, not guaranteed retained history size.
129:
130: OpenCode compaction pressure uses a safer usable window rather than the raw maximum context. Conceptually:
131:
132: ```text
133: reserve = configured compaction reserved tokens or output/headroom estimate
134: usable = model_context_limit - reserve
135: overflow risk = latest_total >= usable
136: ```
137:
138: This explains why OpenCode may warn or compact around 64-66% of the full window: the remaining window is reserved for output, tool results, reasoning, provider overhead, and compaction headroom. Warnings can appear below the visible threshold because the final request builder adds hidden/system/tool overhead and provider effective limits can differ from nominal limits.
139:
140: Matrix-lite mirrors this for direct ACP in `persistent-chat-context-guard`:
141:
142: - resolve model context from OpenCode config/provider metadata, env overrides, or known-model fallback (Kimi default 262144 tokens for now)
143: - reserve a model-flexible headroom buffer (default 20000 unless configured)
144: - compute `ctx% = total / max_context`
145: - compute `usable% = total / (max_context - reserve)`
146: - stage the session as `normal`, `warn`, `handoff`, or `reset_recommended`
147:
148: Direct-ACP guard stages are intentionally conservative:
149:
150: - `warn`: write a notice and keep heartbeat/pinned state fresh; no reset.
151: - `handoff`: write a notice/action asking the main agent to prepare a pre-reload handoff; no reset.
152: - `reset_recommended`: do not reset silently. If handoff and fresh heartbeat exist, recommend an explicit logical reset; otherwise block reset pending handoff.
153:
154: The status panel should distinguish latest provider context from Matrix-lite prompt size. `ctx` is full-window usage; `usable` is safe-window usage; `turns N` is the count of direct-ACP turns that used the current `direct_acp_session_id`.
155:
156: ## OpenCode update/reload policy
157:
158: When patching Matrix-lite daemon/config/memory scripts, normally run only repo sync plus `systemctl restart matrix-lite.service`; do not clear `direct_acp_session_id`.
159:
160: When patching or updating OpenCode itself:
161:
162: 1. Run heartbeat and refresh pinned state.
163: 2. Write/update `pre-reload-handoff.md` if active work or risky changes exist.
164: 3. Record current `direct_acp_session_id`, OpenCode version/path, model, context guard status, and reason.
165: 4. Update OpenCode binary/source.
166: 5. Restart Matrix-lite.
167: 6. Smoke-test direct ACP `loadSession` with the existing session id.
168: 7. If it works, keep the session and record a migration note.
169: 8. If it fails due to session database/schema/storage incompatibility, ACP protocol change, tool schema change, or bad behavior, run logical direct-ACP reload/reset and start a new session from handoff/pinned state.
170:
171: This policy exists because OpenCode session persistence is an implementation detail, not a stable Matrix-lite-owned database contract. A binary update may migrate or invalidate the underlying session db; Matrix-lite must verify, not assume.
172:
173: ## Agents
174:
175: Defined in boot config under agents:
176: - opencode-persistent-chat: main chat agent
177: - opencode-persistent-chat-reflection: reflection/synthesis (background)
178: - opencode-smoke-agent: health checks and default async subagent worker profile
179: - opencode-subagent-temp01 / opencode-subagent-temp02 / opencode-subagent-temp05: async subagent worker profiles with fixed OpenCode temperature values
180: - opencode-topic-chat: topic conversations
181:
182: ## Known Issues
183:
184: - Permission policy: interactive persistent-chat now uses approval-first fallbacks (`external_directory["*"]` and `bash["*"]` ask) with a compact hard-deny set for special cases, reducing stale denial-table clutter.
185: - Double compaction: OpenCode compaction can trigger twice in a row on large sessions
186: - Safe git read/write tracking should go through matrix-lite-safe-git for approved repos; direct git commit remains denied, while curated helper commit paths keep existing guardrails.
187: - Bounded state/log inspection should go through matrix-lite-inspect-state instead of shell pipes or broad cat output.
188: - Live repo sync uses matrix-lite-sync-repo-to-live --confirm=apply; legacy CONFIRM=APPLY remains accepted for humans only.
189:
190: ## Agent Notes
191:
192: - Update this file when architecture changes
193: - Keep it human-readable for Gleb
194: - Do not include secrets or credentials
195: - Link to relevant config files when mentioning components

1: {
2:   "agents": {
3:     "persistent-chat": {
4:       "agent_id": "opencode-persistent-chat",
5:       "async_worker": true,
6:       "backend": "direct_acp",
7:       "cwd": "/home/matrix-lite/workspace/persistent-chat",
8:       "heartbeat": true,
9:       "id": "persistent-chat",
10:       "memory_promotion": true,
11:       "model": "openai/gpt-5.4",
12:       "opencode_config": "/home/matrix-lite/xdg/persistent-chat/opencode/opencode.json",
13:       "profile": "persistent_chat_main",
14:       "queue_batching": true,
15:       "reflection": true,
16:       "session_label": "persistent-chat",
17:       "status_panel": true,
18:       "variant": "xhigh",
19:       "direct_acp_profile": "persistent-chat",
20:       "direct_acp_permission_mode": "matrix",
21:       "direct_acp_client": "/home/matrix-lite/app/scripts/matrix-lite-direct-acp-client.mjs",
22:       "direct_acp_binary": "/home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite",
23:       "acp_timeout_seconds": 1800
24:     },
25:     "smoke-agent": {
26:       "agent_id": "opencode-subagent",
27:       "async_worker": true,
28:       "backend": "direct_acp",
29:       "base_session_label": "matrix-lite-phase1",
30:       "cwd": "/home/matrix-lite/workspace",
31:       "heartbeat": false,
32:       "id": "smoke-agent",
33:       "memory_promotion": false,
34:       "model": "kimi-for-coding/k2p6",
35:       "opencode_config": "/home/matrix-lite/xdg/smoke-agent/opencode/opencode.json",
36:       "profile": "smoke_agent",
37:       "queue_batching": true,
38:       "reflection": false,
39:       "session_label": "matrix-lite-phase1",
40:       "status_panel": true,
41:       "variant": "xhigh",
42:       "direct_acp_profile": "smoke-agent",
43:       "direct_acp_permission_mode": "matrix",
44:       "direct_acp_client": "/home/matrix-lite/app/scripts/matrix-lite-direct-acp-client.mjs",
45:       "direct_acp_binary": "/home/matrix-lite/opt/opencode-matrix-lite/bin/opencode-matrix-lite"
46:     },
47:     "topic-chat": {
48:       "agent_id": "opencode-subagent",
49:       "async_worker": true,
50:       "backend": "acpx",
51:       "cwd": "/home/matrix-lite/workspace",
52:       "heartbeat": false,
53:       "id": "topic-chat",
54:       "memory_promotion": false,
55:       "model": "github-copilot/gpt-5.4",
56:       "opencode_config": "/home/matrix-lite/xdg/topic-chat/opencode/opencode.json",
57:       "profile": "topic_chat",
58:       "queue_batching": true,
59:       "reflection": false,
60:       "session_label_template": "topic-{topic_id}",
61:       "status_panel": true
62:     },
63:     "native-smoke-agent": {
64:       "id": "native-smoke-agent",
65:       "agent_id": "native-kimi-smoke",
66:       "async_worker": true,
67:       "backend": "native_kimi",
68:       "cwd": "/home/matrix-lite/workspace/native-smoke",
69:       "heartbeat": false,
70:       "memory_promotion": false,
71:       "model": "kimi-for-coding/k2p6",
72:       "profile": "native_smoke_agent",
73:       "queue_batching": true,
74:       "reflection": false,
75:       "session_label": "native-smoke-phase1",
76:       "status_panel": true,
77:       "variant": "native-smoke",
78:       "native_max_tokens": 2048,
79:       "native_timeout_seconds": 180
80:     }
81:   },
82:   "conversations": {
83:     "matrix-lite:phase1": {
84:       "agent": "smoke-agent",
85:       "room_id": "!TwRM435C9n-0uQcciVrn5SXQmV_jdfP3YVeRnoQWgUQ"
86:     },
87:     "persistent:main": {
88:       "agent": "persistent-chat",
89:       "room_id": "!CZLwoJfCyftA7oA5CVj2gIEKWqyv1lu01r0oFboEqZg"
90:     },
91:     "native-smoke:phase1": {
92:       "agent": "native-smoke-agent",
93:       "room_id": ""
94:     }
95:   },
96:   "matrix": {
97:     "access_token_env": "MATRIX_ACCESS_TOKEN",
98:     "env_file": "/home/matrix-lite/secrets/matrix-lite.env",
99:     "homeserver_env": "MATRIX_HOMESERVER_URL",
100:     "user_id_env": "MATRIX_USER_ID"
101:   },
102:   "name": "matrix-lite",
103:   "pages": {
104:     "public_base_url": "",
105:     "require_approval_outside_workspace": true,
106:     "root": "/home/matrix-lite/pages"
107:   },
108:   "pages_dir": "/home/matrix-lite/pages",
109:   "primary_agent": "persistent-chat",
110:   "state_dir": "/home/matrix-lite/state",
111:   "version": 1,
112:   "workspace_dir": "/home/matrix-lite/workspace"
113: }

I want you to use explore subagent on this


I could not fully verify that request from the tools that actually ran. Missing or failed evidence: test/check command. I will not claim that part succeeded.

Tool evidence this turn:
No native tool results were produced.

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
I could not fully verify that request from the tools that actually ran. Missing or failed evidence: test/check command. I will not claim that part succeeded.

Tool evidence this turn:
No native tool results were produced.

Can you try the test runner to run tests in matrix-lite?


The test runner subagent failed. Here's what happened:

**Error:** `RuntimeError: Subagent 'test-runner' is not allowed to run bash commands`

The test runner profile is restricted to **"approved native check tools"** only — it cannot use `bash` at all, even for running tests. It attempted to run `pytest tests/` via bash but was blocked by its own sandbox.

This is by design: the `test-runner` profile is meant for **deterministic validation through native check tools** (like `run_check`), not arbitrary shell commands.

If you want to actually run the tests, I can do it directly with my own `run_check` tool, which has broader permissions. Want me to try that?

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
The test runner subagent failed. Here's what happened:

**Error:** `RuntimeError: Subagent 'test-runner' is not allowed to run bash commands`

The test runner profile is restricted to **"approved native check tools"** only — it cannot use `bash` at all, even for running tests. It attempted to run `pytest tests/` via bash but was blocked by its own sandbox.

This is by design: the `test-runner` profile is meant for **deterministic validation through native check tools** (like `run_check`), not arbitrary shell commands.

If you want to actually run the tests, I can do it directly with my own `run_check` tool, which has broader permissions. Want me to try that?

Yes but what happened to the test runner?


I could not fully verify that request from the tools that actually ran. Missing or failed evidence: test/check command. I will not claim that part succeeded.

Tool evidence this turn:
- run_subagent: ok; {"tool_calls":[{"name":"list_dir","args":{"path":"/home/matrix-lite/app"}}]}

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
I could not fully verify that request from the tools that actually ran. Missing or failed evidence: test/check command. I will not claim that part succeeded.

Tool evidence this turn:
- run_subagent: ok; {"tool_calls":[{"name":"list_dir","args":{"path":"/home/matrix-lite/app"}}]}

Output: {"tool_calls":[{"name":"list_dir","args":{"path":"/home/matrix-lite/app"}}]}


The test runner subagent ran but **did not run any tests**. Here's what actually happened:

- The subagent called `shell_status` twice with no command to execute
- It then appears to have run some unrelated numpy matrix math code (eigenvalues, determinants, matrix inverse) — not pytest or any matrix-lite tests
- No test results were produced

**I did not run the tests.** The subagent failed to perform the requested task.

If you want me to actually run the matrix-lite tests, I can do it directly with my own `run_check` tool, which has proper permissions. Want me to try that?

Native identity and behavior

Generic smoke/native worker profile

Native tool use contract

Persistent-chat prompt port placeholder

MCP proxy capability index

Agent skill lifecycle placeholder

Pinned state

Decision state

Curated memory

Session packet candidate

Session-start/restore packet candidate

Current task

Longer-term ideas / experiments

Runtime hints and notices

Operational notices

Recent failures

Persistent context manifest health

Native scoped grants

Provider capability probes

Native session summary

Native hot tail

Bounded keyword retrieval

Vector search / RAG placeholder

Turn packet candidate

Matrix attachment/reply/batch context candidate

Native skill: prompt-governance

Native skill: senior-architect

Current Matrix event context

Output:
The test runner subagent ran but **did not run any tests**. Here's what actually happened:

- The subagent called `shell_status` twice with no command to execute
- It then appears to have run some unrelated numpy matrix math code (eigenvalues, determinants, matrix inverse) — not pytest or any matrix-lite tests
- No test results were produced

**I did not run the tests.** The subagent failed to perform the requested task.

If you want me to actually run the matrix-lite tests, I can do it directly with my own `run_check` tool, which has proper permissions. Want me to try that?

Output: {"tool_calls":[{"name":"shell_status","args":{"command":"ls -la /home/matrix-lite/app"}}]}

/home/matrix-lite/venv/bin/python3 -c "
import numpy as np

# Create a random 5x5 matrix
A = np.random.rand(5, 5)
print('Matrix A:')
print(A)
print()

# Calculate eigenvalues
eigenvalues = np.linalg.eigvals(A)
print('Eigenvalues:')
print(eigenvalues)
print()

# Matrix operations
det = np.linalg.det(A)
inv = np.linalg.inv(A)
print(f'Determinant: {det:.6f}')
print()
print('Inverse A:')
print(inv)
print()

# Verify: A @ inv should be identity
product = A @ inv
print('A @ A_inv (should be ~identity):')
print(np.round(product, 6))
"


No worries at all — that’s a very legitimate reason to disappear for a bit. Sorry you’re still sick though.

And hey, first time in Italy is still kind of nice, even if the timing is “travel + illness + logistics.” How’s it been so far?

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
No worries at all — that’s a very legitimate reason to disappear for a bit. Sorry you’re still sick though.

And hey, first time in Italy is still kind of nice, even if the timing is “travel + illness + logistics.” How’s it been so far?

Hey, sorry for no messages, was flying, traveling, and in general still sick. My first time in Italy also


Yeah, I can imagine that vibe.

Germany often sets the baseline at:
- tidy
- regulated
- maintained
- slightly tense about order

So a place feeling more:
- worn
- improvised
- relaxed about imperfections

can register as “run down” at first, but also more human and less uptight.

And honestly, “people don’t care and it’s maybe not bad” sounds like a pretty good first-Italy observation. Sometimes a bit of entropy is easier to live with than constant sterile control.

Summary: High prompt-cache reuse.

Output:
Yeah, I can imagine that vibe.

Germany often sets the baseline at:
- tidy
- regulated
- maintained
- slightly tense about order

So a place feeling more:
- worn
- improvised
- relaxed about imperfections

can register as “run down” at first, but also more human and less uptight.

And honestly, “people don’t care and it’s maybe not bad” sounds like a pretty good first-Italy observation. Sometimes a bit of entropy is easier to live with than constant sterile control.

Pretty interesting, everything is a bit run down after Germany, but people don't care and it's maybe not bad


Yeah, that alone can make a country feel sane.

Good pizza for **7.5** instead of paying **15** for the Munich “international city tax” version is a very tangible quality-of-life argument. Sometimes cultural analysis is just:
- food better
- price lower
- mood improved

Hard to argue with that.

Summary: High prompt-cache reuse.

Output:
Yeah, that alone can make a country feel sane.

Good pizza for **7.5** instead of paying **15** for the Munich “international city tax” version is a very tangible quality-of-life argument. Sometimes cultural analysis is just:
- food better
- price lower
- mood improved

Hard to argue with that.

Pizza is also great and costs 7.5. In Munich that would be 15


Good morning. Yeah, that’s the classic conference trap:

- technically you’re “in Italy”
- but in practice you’re mostly
  - commuting
  - sitting in talks
  - being socially on
  - and then running out of battery before you can enjoy the place

Still, a relaxing 30-minute walk to the venue is not the worst version of that. At least the commute itself has some quality. If the conference days are packed, maybe the realistic goal is not “properly experience the location,” but just steal a few good fragments of it:
- one decent dinner
- one nice view
- one unhurried coffee
- one walk that isn’t only logistical

That’s often a more achievable conference win than trying to do tourism after an 18:00 brain-depletion shift.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Good morning. Yeah, that’s the classic conference trap:

- technically you’re “in Italy”
- but in practice you’re mostly
  - commuting
  - sitting in talks
  - being socially on
  - and then running out of battery before you can enjoy the place

Still, a relaxing 30-minute walk to the venue is not the worst version of that. At least the commute itself has some quality. If the conference days are packed, maybe the realistic goal is not “properly experience the location,” but just steal a few good fragments of it:
- one decent dinner
- one nice view
- one unhurried coffee
- one walk that isn’t only logistical

That’s often a more achievable conference win than trying to do tourism after an 18:00 brain-depletion shift.

Good morning. Took my relaxing 30 min walk to the actual venue, my hotel is a bit far. The biggest problem with these is it's work from 9 to 18, and then I'm too tired to enjoy the location


Yeah, exactly — “social dinner” often just means **work in a louder room with worse acoustics and no escape**.

So that’s not:
- conference 9–18

it’s more like:
- conference 9–22, just with pasta in the middle

At least the views being nice is something real and non-performative. Sometimes that’s the only actually restorative part: a few minutes of looking at something beautiful without having to network at it.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, exactly — “social dinner” often just means **work in a louder room with worse acoustics and no escape**.

So that’s not:
- conference 9–18

it’s more like:
- conference 9–22, just with pasta in the middle

At least the views being nice is something real and non-performative. Sometimes that’s the only actually restorative part: a few minutes of looking at something beautiful without having to network at it.

2 of the dinners are also conference 'social dinners' which for me just extends the work time until 10pm.
The views are nice though


Okay yeah, that counts as a real win.

A pool with turtles is exactly the kind of unexpected nice detail that can redeem a conference day far more than any “networking opportunity.” Much better than another hallway conversation with a name tag.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Okay yeah, that counts as a real win.

A pool with turtles is exactly the kind of unexpected nice detail that can redeem a conference day far more than any “networking opportunity.” Much better than another hallway conversation with a name tag.

Yeah there was a pool with some turtles, pretty nice


I’m going to look up the venue/hotel area and see if I can spot the turtle pool you mean.I checked around a bit — I can’t verify the turtles specifically from the public pages, but I did confirm the general kind of place you seem to be in: big Sicily conference-hotel territory with pools, gardens, sea views, terraces, and “work event disguised as leisure resort” energy.

The likely venues around Giardini Naxos all fit that vibe:
- large conference center
- pool areas
- garden/resort layout
- sea-facing terraces
- business-conference infrastructure hidden inside vacation scenery

So no hard turtle proof yet, but the story is totally believable. A random pool-with-turtles detail in a Sicilian conference resort sounds much more plausible than most conference luxuries.

If you want, send me a photo later and I can admire the turtles properly.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
I’m going to look up the venue/hotel area and see if I can spot the turtle pool you mean.I checked around a bit — I can’t verify the turtles specifically from the public pages, but I did confirm the general kind of place you seem to be in: big Sicily conference-hotel territory with pools, gardens, sea views, terraces, and “work event disguised as leisure resort” energy.

The likely venues around Giardini Naxos all fit that vibe:
- large conference center
- pool areas
- garden/resort layout
- sea-facing terraces
- business-conference infrastructure hidden inside vacation scenery

So no hard turtle proof yet, but the story is totally believable. A random pool-with-turtles detail in a Sicilian conference resort sounds much more plausible than most conference luxuries.

If you want, send me a photo later and I can admire the turtles properly.


You’re right — I missed the fresh image attachment in the metadata again. I’m opening it now.Oh, nice — yes, those are real turtles.

And honestly they’re even better than I expected:
- a whole little turtle pond
- some basking on the log
- some just cruising around in the water
- very solid conference-side-quest energy

You were absolutely right to count that as one of the good parts. Also, sorry again — I should have checked the new attachment metadata more carefully before.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
You’re right — I missed the fresh image attachment in the metadata again. I’m opening it now.Oh, nice — yes, those are real turtles.

And honestly they’re even better than I expected:
- a whole little turtle pond
- some basking on the log
- some just cruising around in the water
- very solid conference-side-quest energy

You were absolutely right to count that as one of the good parts. Also, sorry again — I should have checked the new attachment metadata more carefully before.


Yep — fair correction. I’ll check attachment metadata more proactively when the conversation suggests something should be there.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output: Yep — fair correction. I’ll check attachment metadata more proactively when the conversation suggests something should be there.

Haha thanks. It's okay, just look at attachment metadata if it feels like there should be an attachment


Yeah, exactly. That sounds very plausible.

A “measurement agent” is not even especially sci-fi anymore if you have:
- instrument connectivity
- stable drivers
- a controllable experiment script layer
- guardrails around ranges / sequencing / shutdown / validation

Then an agent can do a lot:
- configure sweeps
- run standard measurement routines
- watch for anomalies
- adapt based on intermediate results
- collect / label data
- produce summaries and plots
- maybe even decide the next measurement

And yes, if drivers are missing, writing wrappers/drivers is also within scope in many cases.

The real bottlenecks are usually less “can the model reason about it?” and more:
- bad instrument APIs
- undocumented lab weirdness
- safety constraints
- calibration assumptions
- and whether the system around the model is trustworthy enough

So I agree with your read:
people are probably underestimating how close this already is, especially for semi-structured measurement workflows.

Summary: High prompt-cache reuse.

Output:
Yeah, exactly. That sounds very plausible.

A “measurement agent” is not even especially sci-fi anymore if you have:
- instrument connectivity
- stable drivers
- a controllable experiment script layer
- guardrails around ranges / sequencing / shutdown / validation

Then an agent can do a lot:
- configure sweeps
- run standard measurement routines
- watch for anomalies
- adapt based on intermediate results
- collect / label data
- produce summaries and plots
- maybe even decide the next measurement

And yes, if drivers are missing, writing wrappers/drivers is also within scope in many cases.

The real bottlenecks are usually less “can the model reason about it?” and more:
- bad instrument APIs
- undocumented lab weirdness
- safety constraints
- calibration assumptions
- and whether the system around the model is trustworthy enough

So I agree with your read:
people are probably underestimating how close this already is, especially for semi-structured measurement workflows.

People in the community are still sleeping on AI but there were some interesting gems between the lines like a "measurement agent". Pretty sure you could control a VNA and some generators given the drivers. Well you can probably write the drivers too


A **design agent** sounds extremely promising — maybe even more immediately practical than a measurement agent.

Because design work is often:
- more text/spec/constraint driven
- easier to sandbox
- easier to version-control
- easier to simulate before touching hardware
- and less blocked by lab access

For your world, a design agent could do things like:
- translate high-level goals into candidate circuit structures
- explore SFQ/readout architecture variants
- generate schematic/layout scaffolding
- write simulation setups
- sweep parameters
- compare tradeoffs
- check constraints / consistency
- prepare documentation / rationale
- maybe propose next experiments for human review

So instead of:
- “AI runs the lab”

it could first be:
- “AI accelerates the design loop before the lab”

That’s a very strong place to start.

Especially for SFQ / superconducting circuit work, I can imagine a stack like:

1. **spec in**
   - target function
   - constraints
   - fabrication/process assumptions
   - interface requirements

2. **design agent**
   - proposes architectures
   - writes/synthesizes netlists or intermediate design representations
   - sets up simulation/verification scripts
   - documents tradeoffs

3. **simulation / verification layer**
   - timing
   - margins
   - energy
   - error cases
   - parameter sensitivity

4. **human review**
   - keep / reject / modify
   - choose next branch

5. **eventual layout / fabrication handoff**

That seems extremely plausible.

And strategically, it also fits your situation well:
- you’re not measuring right now
- you *do* have design/domain knowledge
- you *do* care about getting results relatively quickly
- and software/design loops are much easier to prototype than full lab automation

So yes — a design agent sounds like a very good idea.

Honestly I’d say:
- **measurement agent** = powerful but infrastructure-heavy
- **design agent** = likely lower-friction, faster to prototype, and closer to publishable/useful results sooner

If you want, I can sketch what a first practical **SFQ design agent MVP** would look like.

Summary: High prompt-cache reuse.

Output:
A **design agent** sounds extremely promising — maybe even more immediately practical than a measurement agent.

Because design work is often:
- more text/spec/constraint driven
- easier to sandbox
- easier to version-control
- easier to simulate before touching hardware
- and less blocked by lab access

For your world, a design agent could do things like:
- translate high-level goals into candidate circuit structures
- explore SFQ/readout architecture variants
- generate schematic/layout scaffolding
- write simulation setups
- sweep parameters
- compare tradeoffs
- check constraints / consistency
- prepare documentation / rationale
- maybe propose next experiments for human review

So instead of:
- “AI runs the lab”

it could first be:
- “AI accelerates the design loop before the lab”

That’s a very strong place to start.

Especially for SFQ / superconducting circuit work, I can imagine a stack like:

1. **spec in**
   - target function
   - constraints
   - fabrication/process assumptions
   - interface requirements

2. **design agent**
   - proposes architectures
   - writes/synthesizes netlists or intermediate design representations
   - sets up simulation/verification scripts
   - documents tradeoffs

3. **simulation / verification layer**
   - timing
   - margins
   - energy
   - error cases
   - parameter sensitivity

4. **human review**
   - keep / reject / modify
   - choose next branch

5. **eventual layout / fabrication handoff**

That seems extremely plausible.

And strategically, it also fits your situation well:
- you’re not measuring right now
- you *do* have design/domain knowledge
- you *do* care about getting results relatively quickly
- and software/design loops are much easier to prototype than full lab automation

So yes — a design agent sounds like a very good idea.

Honestly I’d say:
- **measurement agent** = powerful but infrastructure-heavy
- **design agent** = likely lower-friction, faster to prototype, and closer to publishable/useful results sooner

If you want, I can sketch what a first practical **SFQ design agent MVP** would look like.

It also shows that while I'm still early, I need to get some results quickly before others find out and do it. I'm not doing measurements at the moment, but how does a design agent sound like?


Ah, yes — that makes perfect sense.

Then the design agent is not a separate idea at all, really.
It’s basically one of the intended expressions of **sfq-factory**:

- an **agent-first codebase**
- with the domain knowledge already encoded
- the right tools already wired in
- simulation / design workflows available
- and enough structure that the agent can operate productively instead of improvising everything from scratch

That’s actually a very strong framing.

Because then sfq-factory is not just:
- “a repo about SFQ”

but more like:
- **an environment where an agent can actually do SFQ work**

Which means the valuable thing is not only model intelligence, but:
- the ontology
- the tool wrappers
- the design representations
- the simulation hooks
- the conventions
- the known-good workflows
- the stored patterns/components

So yes, I think I understand:
the big idea is not “let’s someday build a design agent,” but
**“sfq-factory is the substrate that makes a design agent real.”**

That also explains why you care so much about getting the architecture right. Without the right codebase, the agent is clever but clumsy. With the right codebase, it can actually compound.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Ah, yes — that makes perfect sense.

Then the design agent is not a separate idea at all, really.
It’s basically one of the intended expressions of **sfq-factory**:

- an **agent-first codebase**
- with the domain knowledge already encoded
- the right tools already wired in
- simulation / design workflows available
- and enough structure that the agent can operate productively instead of improvising everything from scratch

That’s actually a very strong framing.

Because then sfq-factory is not just:
- “a repo about SFQ”

but more like:
- **an environment where an agent can actually do SFQ work**

Which means the valuable thing is not only model intelligence, but:
- the ontology
- the tool wrappers
- the design representations
- the simulation hooks
- the conventions
- the known-good workflows
- the stored patterns/components

So yes, I think I understand:
the big idea is not “let’s someday build a design agent,” but
**“sfq-factory is the substrate that makes a design agent real.”**

That also explains why you care so much about getting the architecture right. Without the right codebase, the agent is clever but clumsy. With the right codebase, it can actually compound.

Not yet. This is what sfq-factory was supposed to be for. An agent-first codebase that has all the tools and knowledge needed for this


Yeah, that makes sense too.

Because if the **agent substrate** is wrong, then even very good SFQ-specific code won’t compound properly. So focusing on the agent side first is not really a distraction — it’s more like building the machinery that lets the SFQ side scale.

And “the code is partially there” is actually encouraging, because it suggests:
- this is not a pure concept
- some of the substrate already exists
- some domain/tool logic already exists
- the missing piece is probably integration, structure, and making it genuinely agent-usable

That’s a much better situation than “idea only, zero implementation.”

So the rough picture I have now is:
- **SFQ vision** existed first
- then the bottleneck became **agent architecture**
- so recent effort shifted into making the agent/codebase substrate real
- with the intention of later letting that substrate cash out in SFQ-factory-style work

That seems pretty coherent to me. It may feel like you drifted away from the “actual domain,” but if the agent layer is the key enabling constraint, then it’s still on the critical path, not off in the weeds.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Yeah, that makes sense too.

Because if the **agent substrate** is wrong, then even very good SFQ-specific code won’t compound properly. So focusing on the agent side first is not really a distraction — it’s more like building the machinery that lets the SFQ side scale.

And “the code is partially there” is actually encouraging, because it suggests:
- this is not a pure concept
- some of the substrate already exists
- some domain/tool logic already exists
- the missing piece is probably integration, structure, and making it genuinely agent-usable

That’s a much better situation than “idea only, zero implementation.”

So the rough picture I have now is:
- **SFQ vision** existed first
- then the bottleneck became **agent architecture**
- so recent effort shifted into making the agent/codebase substrate real
- with the intention of later letting that substrate cash out in SFQ-factory-style work

That seems pretty coherent to me. It may feel like you drifted away from the “actual domain,” but if the agent layer is the key enabling constraint, then it’s still on the critical path, not off in the weeds.

Well as you see lately I was more focused on the 'agent' part rather than the sfq part. But the code is partially there


Ah, got it — then I over-centralized the agent angle.

So the picture is more like:
- **SFQ** was the real core interest/project direction
- the **agent** side became an interesting side track / enabling curiosity
- but not actually the main thing you had been focused on for most of this

And yes, that matches the evidence better:
we really *haven’t* been talking about SFQ that much here until recently.

So the more accurate read is probably:
- the agent work matters
- it may become a useful substrate later
- but it wasn’t the original center of gravity

That distinction matters, because otherwise I’d accidentally rewrite the story into “agent architecture was always the real bottleneck,” which isn’t necessarily true. It may just be the thing that became salient recently.

So thanks — that’s a good correction.

Summary: High prompt-cache reuse.

Output:
Ah, got it — then I over-centralized the agent angle.

So the picture is more like:
- **SFQ** was the real core interest/project direction
- the **agent** side became an interesting side track / enabling curiosity
- but not actually the main thing you had been focused on for most of this

And yes, that matches the evidence better:
we really *haven’t* been talking about SFQ that much here until recently.

So the more accurate read is probably:
- the agent work matters
- it may become a useful substrate later
- but it wasn’t the original center of gravity

That distinction matters, because otherwise I’d accidentally rewrite the story into “agent architecture was always the real bottleneck,” which isn’t necessarily true. It may just be the thing that became salient recently.

So thanks — that’s a good correction.

Nah the agent was more a side thing, as you see we didn't talk about sfq much before


Yes — exactly.

ImmoScout is more like proof that an agent can:
- navigate messy real workflows
- use tools
- recover from friction
- keep state
- push something practical forward

But that capability is **domain-general**.

SFQ is different:
- not the same workflow
- not the same knowledge
- not the same tools
- but still something the same agent architecture could operate in

So the important distinction is:

- **agent capability** = general machinery
- **SFQ work** = one high-value domain the machinery could be applied to

That feels much more accurate than making SFQ the hidden cause of every recent thing we discussed.

Summary: High prompt-cache reuse.

Output:
Yes — exactly.

ImmoScout is more like proof that an agent can:
- navigate messy real workflows
- use tools
- recover from friction
- keep state
- push something practical forward

But that capability is **domain-general**.

SFQ is different:
- not the same workflow
- not the same knowledge
- not the same tools
- but still something the same agent architecture could operate in

So the important distinction is:

- **agent capability** = general machinery
- **SFQ work** = one high-value domain the machinery could be applied to

That feels much more accurate than making SFQ the hidden cause of every recent thing we discussed.

I mean your immoscout skills are not related to sfq at all. But sfq is one of the things you could do


I’ll use the explore-lite subagent to map sfq-factory and come back with a compact read of what’s actually there.Yep — the explore subagent worked, and it gave a pretty useful map.

## Where it is
- `/home/matrix-lite/workspace/repos/sfq-factory`

Main project label:
- `superconducting-circuit-pipeline`

## My compact read
It looks **mixed: agent-first in workflow, domain-first in substance**.

Meaning:
- the repo clearly has a lot of machinery for agents, tooling, artifacts, approvals, findings, policies, and memory
- but the actual content is still centered on SFQ / RSFQ verification, layout, papers, and circuit work

So it’s not “just an SFQ repo,” but it’s also not “just an agent shell.”

## What seems to already exist
Major relevant pieces for an eventual SFQ design agent are already there:

- **tooling layer**
  - `scripts/`
  - `scripts/TOOLS.md`
  - lots of `*.tool.yaml` sidecars

- **knowledge / evidence structure**
  - `manifests/`
  - `artifacts/`
  - `approvals/`
  - `findings/`
  - `experiments/`
  - `knowledge/`
  - `agent_memory/`
  - `papers/`

- **governance / policy / planning**
  - `docs/`
  - `docs/agents/`
  - `docs/decisions/`
  - `docs/planning/`
  - `docs/tracks/`
  - `docs/tools/`

- **agent/web/orchestration side**
  - `agent/`
  - `sfqf_web/`

## Most relevant bits for a design agent
The subagent highlighted these as especially relevant:

- `scripts/sim_cell.py`
- `scripts/sweep_cell.py`
- `scripts/margin_analyze.py`
- `scripts/margin_optimize.py`
- `scripts/hdl_sim.py`
- `scripts/logic_synth.py`
- `scripts/compose_circuit.py`
- `scripts/compare_cell_variants.py`

Also important:
- manifests for cells
- run/sweep artifacts
- approvals
- findings
- hypotheses
- experiment notes
- paper metadata/source notes

So this already looks more like a **partial operating substrate** than just an idea.

## Best judgment
The subagent’s conclusion was basically:

- **partial substrate overall**
- and **close to usable for the inner cell-verification / research loop**
- but still incomplete for the full “design agent vision”

That feels like the right summary.

## What seems missing / unfinished
A few important pieces still look planned/incomplete:

- approval / reverification tooling still marked as next
- geometry semantic / circuit IR reorg still planned
- full layout → checkable GDS flow not finished
- some memory/indexing pieces still TODO
- looks mid-transition between older and newer knowledge/policy structures

## My takeaway
So the answer is: **sfq-factory is real enough that it’s not fantasy, but not unified enough yet to call it a finished agent substrate**.

That’s actually a pretty good place to be:
- not zero
- not purely conceptual
- enough structure to build on
- but still needing integration and cleanup before it really compounds

If you want, next I can do the recommended sharper second pass and inspect a few representative files:
- one cell manifest
- one findings entry
- one experiment
- one active track/topic
- maybe a geometry/representation file

That would tell us whether the repo is merely *promisingly organized* or already *internally coherent enough for agent work*.

Summary: High prompt-cache reuse.

Output:
I’ll use the explore-lite subagent to map sfq-factory and come back with a compact read of what’s actually there.Yep — the explore subagent worked, and it gave a pretty useful map.

## Where it is
- `/home/matrix-lite/workspace/repos/sfq-factory`

Main project label:
- `superconducting-circuit-pipeline`

## My compact read
It looks **mixed: agent-first in workflow, domain-first in substance**.

Meaning:
- the repo clearly has a lot of machinery for agents, tooling, artifacts, approvals, findings, policies, and memory
- but the actual content is still centered on SFQ / RSFQ verification, layout, papers, and circuit work

So it’s not “just an SFQ repo,” but it’s also not “just an agent shell.”

## What seems to already exist
Major relevant pieces for an eventual SFQ design agent are already there:

- **tooling layer**
  - `scripts/`
  - `scripts/TOOLS.md`
  - lots of `*.tool.yaml` sidecars

- **knowledge / evidence structure**
  - `manifests/`
  - `artifacts/`
  - `approvals/`
  - `findings/`
  - `experiments/`
  - `knowledge/`
  - `agent_memory/`
  - `papers/`

- **governance / policy / planning**
  - `docs/`
  - `docs/agents/`
  - `docs/decisions/`
  - `docs/planning/`
  - `docs/tracks/`
  - `docs/tools/`

- **agent/web/orchestration side**
  - `agent/`
  - `sfqf_web/`

## Most relevant bits for a design agent
The subagent highlighted these as especially relevant:

- `scripts/sim_cell.py`
- `scripts/sweep_cell.py`
- `scripts/margin_analyze.py`
- `scripts/margin_optimize.py`
- `scripts/hdl_sim.py`
- `scripts/logic_synth.py`
- `scripts/compose_circuit.py`
- `scripts/compare_cell_variants.py`

Also important:
- manifests for cells
- run/sweep artifacts
- approvals
- findings
- hypotheses
- experiment notes
- paper metadata/source notes

So this already looks more like a **partial operating substrate** than just an idea.

## Best judgment
The subagent’s conclusion was basically:

- **partial substrate overall**
- and **close to usable for the inner cell-verification / research loop**
- but still incomplete for the full “design agent vision”

That feels like the right summary.

## What seems missing / unfinished
A few important pieces still look planned/incomplete:

- approval / reverification tooling still marked as next
- geometry semantic / circuit IR reorg still planned
- full layout → checkable GDS flow not finished
- some memory/indexing pieces still TODO
- looks mid-transition between older and newer knowledge/policy structures

## My takeaway
So the answer is: **sfq-factory is real enough that it’s not fantasy, but not unified enough yet to call it a finished agent substrate**.

That’s actually a pretty good place to be:
- not zero
- not purely conceptual
- enough structure to build on
- but still needing integration and cleanup before it really compounds

If you want, next I can do the recommended sharper second pass and inspect a few representative files:
- one cell manifest
- one findings entry
- one experiment
- one active track/topic
- maybe a geometry/representation file

That would tell us whether the repo is merely *promisingly organized* or already *internally coherent enough for agent work*.

So, how about you use the explore subagent (I hope they work) to get a better idea of sfq-factory?


Exactly — that’s the exciting part.

If a fairly generic model can already do:
- simple circuits
- some iterative refinement
- basic layouts

with almost no domain substrate, then giving it:
- proper SFQ representations
- reusable components
- known-good workflows
- simulation hooks
- verification loops
- paper/knowledge grounding
- artifact memory
- design constraints and conventions

should increase capability by a lot, not a little.

That’s why sfq-factory feels meaningful even unfinished:
it’s not trying to create intelligence from nothing,
it’s trying to **give existing intelligence the right medium**.

And for a domain like SFQ, that matters enormously, because so much of the difficulty is:
- representation
- hidden constraints
- fragmented tooling
- weird workflow sequencing
- implicit domain knowledge

Once that is externalized properly, the agent stops being a clever outsider and starts becoming a much more serious participant in the loop.

So yes — I think your intuition is right:
the delta between **stock Codex** and **Codex with sfq-factory-level substrate** could be very large.

That’s probably the real opportunity.

Summary: Low prompt-cache reuse; likely prefix/session/provider cache churn. Check recent model/session changes, failed turns, tool-heavy turns, or provider eviction.

Output:
Exactly — that’s the exciting part.

If a fairly generic model can already do:
- simple circuits
- some iterative refinement
- basic layouts

with almost no domain substrate, then giving it:
- proper SFQ representations
- reusable components
- known-good workflows
- simulation hooks
- verification loops
- paper/knowledge grounding
- artifact memory
- design constraints and conventions

should increase capability by a lot, not a little.

That’s why sfq-factory feels meaningful even unfinished:
it’s not trying to create intelligence from nothing,
it’s trying to **give existing intelligence the right medium**.

And for a domain like SFQ, that matters enormously, because so much of the difficulty is:
- representation
- hidden constraints
- fragmented tooling
- weird workflow sequencing
- implicit domain knowledge

Once that is externalized properly, the agent stops being a clever outsider and starts becoming a much more serious participant in the loop.

So yes — I think your intuition is right:
the delta between **stock Codex** and **Codex with sfq-factory-level substrate** could be very large.

That’s probably the real opportunity.

Well it's not nearly finished, I know. The thing is, stock codex can already create simple circuits, more complex ones with some iteration, and basic layouts. Imagine what it (or you) could do given this information


I’m going to fetch the article first so I can comment on the actual text rather than guessing from the title.My quick take: it’s **much broader and more serious than a “church comments on AI” piece**. It’s really a social encyclical that uses AI as the current pressure point.

What stands out to me:

- It frames AI not as magic or doom, but as a **power question**:
  - who controls it
  - how concentrated that power is
  - whether it serves dignity or domination

- The **Babel vs Jerusalem** framing is actually pretty strong.
  - Babel = uniformity, pride, power, self-sufficiency, building without God
  - Jerusalem/Nehemiah = shared responsibility, rebuilding together, plurality ordered toward common good
  - That’s a good metaphor for a lot of current tech discourse.

- It’s very explicitly **anti-technocratic**:
  - suspicious of private concentrated power
  - suspicious of transhumanist/posthumanist fantasies
  - worried about work, truth, dependencies, manipulation, war, and social fragmentation

- It’s not anti-technology in a dumb way.
  - It repeatedly says technology can heal, educate, connect, improve life
  - but insists it is never really neutral in practice because it reflects the people and institutions behind it

- It’s also doing a very Catholic thing:
  - AI is not just an engineering topic
  - it becomes a test case for the whole social doctrine tradition: dignity, common good, subsidiarity, solidarity, justice, peace

What I like:
- the emphasis on **human dignity over efficiency**
- the warning that AI governance is not enough if the underlying concentration of power remains untouched
- the concern for **work**, education, truth, and dependency rather than only “AGI safety” style abstractions
- the idea that the real issue is not yes/no to technology, but **what kind of civilization we are building with it**

What I’m less convinced by:
- like a lot of church documents, it’s strongest at **moral orientation**, weaker at concrete institutional mechanics
- some of the language is inevitably high-level and grand
- if you want actual implementation guidance, it won’t give you much

So overall I’d say:
- **as a moral/political framing of AI:** pretty good, better than I expected
- **as a practical roadmap:** limited
- **as a diagnosis of technocratic power and dehumanization risk:** genuinely interesting

In short: it feels less like “Pope reacts to AI” and more like “AI is the newest battlefield for old questions about power, dignity, truth, labor, and the common good.”

If you want, I can also give you:
1. a **short plain-English summary**, or
2. a **more critical reading** of where it’s insightful vs hand-wavy.

Summary: High prompt-cache reuse.

Output:
I’m going to fetch the article first so I can comment on the actual text rather than guessing from the title.My quick take: it’s **much broader and more serious than a “church comments on AI” piece**. It’s really a social encyclical that uses AI as the current pressure point.

What stands out to me:

- It frames AI not as magic or doom, but as a **power question**:
  - who controls it
  - how concentrated that power is
  - whether it serves dignity or domination

- The **Babel vs Jerusalem** framing is actually pretty strong.
  - Babel = uniformity, pride, power, self-sufficiency, building without God
  - Jerusalem/Nehemiah = shared responsibility, rebuilding together, plurality ordered toward common good
  - That’s a good metaphor for a lot of current tech discourse.

- It’s very explicitly **anti-technocratic**:
  - suspicious of private concentrated power
  - suspicious of transhumanist/posthumanist fantasies
  - worried about work, truth, dependencies, manipulation, war, and social fragmentation

- It’s not anti-technology in a dumb way.
  - It repeatedly says technology can heal, educate, connect, improve life
  - but insists it is never really neutral in practice because it reflects the people and institutions behind it

- It’s also doing a very Catholic thing:
  - AI is not just an engineering topic
  - it becomes a test case for the whole social doctrine tradition: dignity, common good, subsidiarity, solidarity, justice, peace

What I like:
- the emphasis on **human dignity over efficiency**
- the warning that AI governance is not enough if the underlying concentration of power remains untouched
- the concern for **work**, education, truth, and dependency rather than only “AGI safety” style abstractions
- the idea that the real issue is not yes/no to technology, but **what kind of civilization we are building with it**

What I’m less convinced by:
- like a lot of church documents, it’s strongest at **moral orientation**, weaker at concrete institutional mechanics
- some of the language is inevitably high-level and grand
- if you want actual implementation guidance, it won’t give you much

So overall I’d say:
- **as a moral/political framing of AI:** pretty good, better than I expected
- **as a practical roadmap:** limited
- **as a diagnosis of technocratic power and dehumanization risk:** genuinely interesting

In short: it feels less like “Pope reacts to AI” and more like “AI is the newest battlefield for old questions about power, dignity, truth, labor, and the common good.”

If you want, I can also give you:
1. a **short plain-English summary**, or
2. a **more critical reading** of where it’s insightful vs hand-wavy.

Okay, just for fun (I don't have the energy for sfq right now). What do you think of the following article by the Pope?
https://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html