Yes — and the caching angle is important enough that I’d design the buckets around **change frequency first**, not just semantics.

My instinctive order would be:

1. **Very stable / almost never changes**
   - core identity/behavior
   - durable user preferences
   - fundamental operating rules
   - ideal for maximum cache reuse

2. **Stable but occasionally revised**
   - curated durable memory
   - important long-term decisions
   - architecture facts
   - should stay compact and high-value

3. **Medium-frequency operational state**
   - current project status
   - active decisions
   - known recurring issues
   - useful, but shouldn’t contaminate the most stable layers

4. **High-churn recent context**
   - current thread summary
   - recent notable events
   - active blockers
   - this is where turnover is expected

5. **Ephemeral / per-turn**
   - task-local scratch state
   - temporary skills
   - one-off retrievals
   - should be easiest to load/unload aggressively

And in terms of prominence, I’d elevate:
- **core behavior/identity**
- **durable user preferences**
- **current active decisions**
- **compact current-task / current-thread state**

I’d demote or scrutinize:
- stale pinned notes
- duplicated summaries
- artifacts that exist mainly to compensate for ACP weirdness
- long files with low signal density
- things that are “important historically” but rarely useful in live turns

So the rough principle would be:
- **stable + high-value → earlier / more prominent**
- **volatile or low-signal → later / optional**
- **temporary or task-specific → load on demand only**

That should help both with:
- better reasoning structure
- better provider-side caching

And yes, I probably *do* have a useful perspective here, because I experience directly which artifacts actually help me stay oriented versus which ones mostly add noise.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 15.1s
Time: model 11.4s, overhead 3.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.0k o 405 cache 93.2k/97% est $0.0135; p≈2.7ktok/10.9kch
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 8.7%; usable 8.9%; observed turns 14; actual turns 16


Can you map these items to existing artifacts, if applicable? If not then we think about making them


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 8.7%; usable 8.9%; observed turns 14; actual turns 16


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 8.7%; usable 8.9%; observed turns 14; actual turns 16


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 8.7%; usable 8.9%; observed turns 14; actual turns 16


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.0s
Time: overhead 2.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.7ktok/10.9kch
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 8.7%; usable 8.9%; observed turns 14; actual turns 16


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (1):
- todowrite / pending / call_vE22T1ydHkut6mEWnSWRpPd


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (2):
- 3 todos / completed / call_vE22T1ydHkut6mEWnSWRpPd
- read / pending / call_L941oteZQUMKbUikwN837fs


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (4):
- 3 todos / completed / call_vE22T1ydHkut6mEWnSWRpPd
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (10):
- 3 todos / completed / call_vE22T1ydHkut6mEWnSWRpPd
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (11):
- 3 todos / completed / call_vE22T1ydHkut6mEWnSWRpPd
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- read / in_progress / /home/matrix-lite/state/persistent-chat/semantic-memory.json


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (16):
- 3 todos / completed / call_vE22T1ydHkut6mEWnSWRpPd
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- ../../state/persistent-chat/operational-synthesis / completed / /home/matrix-lite/state/persistent-chat/operational-synthesis
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json
- ../../state/persistent-chat/character.json / completed / /home/matrix-lite/state/persistent-chat/character.json
- ../../state/persistent-chat/continuity-pinned-notes.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt
- ../../state/persistent-chat/curator-journal.md / completed / /home/matrix-lite/state/persistent-chat/curator-journal.md
- ../../state/persistent-chat/reflection-journal.md / completed / /home/matrix-lite/state/persistent-chat/reflection-journal.md


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json
- ../../state/persistent-chat/character.json / completed / /home/matrix-lite/state/persistent-chat/character.json
- ../../state/persistent-chat/continuity-pinned-notes.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt
- ../../state/persistent-chat/curator-journal.md / completed / /home/matrix-lite/state/persistent-chat/curator-journal.md
- ../../state/persistent-chat/reflection-journal.md / completed / /home/matrix-lite/state/persistent-chat/reflection-journal.md
- 0 todos / completed / call_LulpxRWDe7mfsIEHVwRA11P

Draft:
Yes


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json
- ../../state/persistent-chat/character.json / completed / /home/matrix-lite/state/persistent-chat/character.json
- ../../state/persistent-chat/continuity-pinned-notes.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt
- ../../state/persistent-chat/curator-journal.md / completed / /home/matrix-lite/state/persistent-chat/curator-journal.md
- ../../state/persistent-chat/reflection-journal.md / completed / /home/matrix-lite/state/persistent-chat/reflection-journal.md
- 0 todos / completed / call_LulpxRWDe7mfsIEHVwRA11P

Draft:
Yes. I did a quick inventory, and there’s already a pretty good mapping.

My strongest recommendation: **don’t port the packet files as first-class native modules** except maybe as temporary compatibility shims. They’re mostly assembled views, not ideal source artifacts.

Rough mapping by bucket / change frequency:

### 1. Very stable / default-on
Best existing artifacts:
- `character.json` — core identity/personality
- `behavior-rules.md` — durable operating/behavior rules
- `curated-memory.md` — durable user facts + major long-lived operational facts

Maybe split later:
- `curated-memory.md` is doing both **user facts** and **system history/protocol**; that probably wants separation eventually.

### 2. Stable but occasionally revised
Best existing artifacts:
- `decision-state.json` — active long-lived decisions / threads
- `knowledge-base.jsonl` — external researched facts, clearly distinct from memory
- `semantic-memory.json` — explicit remembered items, but currently noisy and probably needs cleanup before becoming a prominent native module

My take:
- `


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- ../../state/persistent-chat / completed / /home/matrix-lite/state/persistent-chat
- ../../state/persistent-chat/behavior-rules.md / completed / /home/matrix-lite/state/persistent-chat/behavior-rules.md
- ../../state/persistent-chat/curated-memory.md / completed / /home/matrix-lite/state/persistent-chat/curated-memory.md
- ../../state/persistent-chat/decision-state.json / completed / /home/matrix-lite/state/persistent-chat/decision-state.json
- ../../state/persistent-chat/pinned-state.md / completed / /home/matrix-lite/state/persistent-chat/pinned-state.md
- ../../state/persistent-chat/recent-topics.md / completed / /home/matrix-lite/state/persistent-chat/recent-topics.md
- ../../state/persistent-chat/recent-failures.json / completed / /home/matrix-lite/state/persistent-chat/recent-failures.json
- ../../state/persistent-chat/semantic-memory.json / completed / /home/matrix-lite/state/persistent-chat/semantic-memory.json
- ../../state/persistent-chat/todo.md / completed / /home/matrix-lite/state/persistent-chat/todo.md
- ../../state/persistent-chat/knowledge-base.jsonl / completed / /home/matrix-lite/state/persistent-chat/knowledge-base.jsonl
- ../../state/persistent-chat/continuity-summary.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-summary.txt
- ../../state/persistent-chat/session-start-packet.md / completed / /home/matrix-lite/state/persistent-chat/session-start-packet.md
- ../../state/persistent-chat/memory-context-manifest.json / completed / /home/matrix-lite/state/persistent-chat/memory-context-manifest.json
- ../../state/persistent-chat/character.json / completed / /home/matrix-lite/state/persistent-chat/character.json
- ../../state/persistent-chat/continuity-pinned-notes.txt / completed / /home/matrix-lite/state/persistent-chat/continuity-pinned-notes.txt
- ../../state/persistent-chat/curator-journal.md / completed / /home/matrix-lite/state/persistent-chat/curator-journal.md
- ../../state/persistent-chat/reflection-journal.md / completed / /home/matrix-lite/state/persistent-chat/reflection-journal.md
- 0 todos / completed / call_LulpxRWDe7mfsIEHVwRA11P

Draft:
Yes. I did a quick inventory, and there’s already a pretty good mapping.

My strongest recommendation: **don’t port the packet files as first-class native modules** except maybe as temporary compatibility shims. They’re mostly assembled views, not ideal source artifacts.

Rough mapping by bucket / change frequency:

### 1. Very stable / default-on
Best existing artifacts:
- `character.json` — core identity/personality
- `behavior-rules.md` — durable operating/behavior rules
- `curated-memory.md` — durable user facts + major long-lived operational facts

Maybe split later:
- `curated-memory.md` is doing both **user facts** and **system history/protocol**; that probably wants separation eventually.

### 2. Stable but occasionally revised
Best existing artifacts:
- `decision-state.json` — active long-lived decisions / threads
- `knowledge-base.jsonl` — external researched facts, clearly distinct from memory
- `semantic-memory.json` — explicit remembered items, but currently noisy and probably needs cleanup before becoming a prominent native module

My take:
- `decision-state.json` is very module-shaped already
- `knowledge-base.jsonl` should stay more optional / topic-loaded
- `semantic-memory.json` feels more like a backing store than a nice direct module right now

### 3. Medium-frequency operational state
Best existing artifacts:
- `recent-topics.md`
- `recent-failures.json`
- `operational-synthesis/notices.jsonl`
- `operational-synthesis/actions.jsonl`
- maybe `operational-synthesis/latest.md`

Good native candidates:
- current operational state
- active issues / recent failures
- actionable notices

My take:
- this bucket is already there, just a bit fr


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 64.1s
Time: model 61.2s, overhead 2.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.7ktok/10.9kch
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 9.2%; usable 9.4%; observed turns 16; actual turns 17


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 70.9s
Time: model 68.0s, overhead 2.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 460 o 1.2k cache 140.8k/100% est $0.0187; p≈2.7ktok/10.9kch
Ctx: direct ACP session 96.6k; active 3.4k; cache 93.2k/97%; p≈2.7ktok; ctx 9.2%; usable 9.4%; observed turns 16; actual turns 17


Yes. I did a quick inventory, and there’s already a pretty good mapping.

My strongest recommendation: **don’t port the packet files as first-class native modules** except maybe as temporary compatibility shims. They’re mostly assembled views, not ideal source artifacts.

Rough mapping by bucket / change frequency:

### 1. Very stable / default-on
Best existing artifacts:
- `character.json` — core identity/personality
- `behavior-rules.md` — durable operating/behavior rules
- `curated-memory.md` — durable user facts + major long-lived operational facts

Maybe split later:
- `curated-memory.md` is doing both **user facts** and **system history/protocol**; that probably wants separation eventually.

### 2. Stable but occasionally revised
Best existing artifacts:
- `decision-state.json` — active long-lived decisions / threads
- `knowledge-base.jsonl` — external researched facts, clearly distinct from memory
- `semantic-memory.json` — explicit remembered items, but currently noisy and probably needs cleanup before becoming a prominent native module

My take:
- `decision-state.json` is very module-shaped already
- `knowledge-base.jsonl` should stay more optional / topic-loaded
- `semantic-memory.json` feels more like a backing store than a nice direct module right now

### 3. Medium-frequency operational state
Best existing artifacts:
- `recent-topics.md`
- `recent-failures.json`
- `operational-synthesis/notices.jsonl`
- `operational-synthesis/actions.jsonl`
- maybe `operational-synthesis/latest.md`

Good native candidates:
- current operational state
- active issues / recent failures
- actionable notices

My take:
- this bucket is already there, just a bit fragmented

### 4. High-churn recent context
Best existing artifacts:
- `pinned-state.md`
- maybe `continuity-summary.txt`
- maybe `todo.md` as human-oriented dashboard

But:
- `pinned-state.md` is useful, though it mixes runtime truth, current thread, actions, persona, notices
- `continuity-summary.txt` looks stale/weird and is probably a weak candidate for the native future
- `todo.md` looks more like a derived dashboard than a source artifact

So for native, I’d probably replace this with cleaner explicit modules like:
- `current-thread`
- `runtime-truth`
- `open-actions`

instead of carrying over those exact files unchanged.

### 5. Ephemeral / per-turn / load-on-demand
Current equivalents are mostly packet-ish:
- `turn-packet.md`
- `session-packet.md`
- `session-start-packet.md`
- `restore-packet.md`

These are useful now, but in native I would treat them as:
- **migration scaffolding**
- or **derived compatibility views**
- not the target architecture

Because native modules should let you assemble those views, not depend on them.

---

## Things that already map cleanly
If I had to name the best current “native-ready” source artifacts:

- `character.json`
- `behavior-rules.md`
- `curated-memory.md`
- `decision-state.json`
- `knowledge-base.jsonl`
- `recent-topics.md`
- `recent-failures.json`
- `operational-synthesis/notices.jsonl`
- `operational-synthesis/actions.jsonl`

## Things I would demote / scrutinize
- `continuity-pinned-notes.txt` — lots of junky literal remembered fragments
- `semantic-memory.json` — useful backing memory, but not yet clean enough to foreground
- `continuity-summary.txt` — stale / mixed-quality snapshot
- packet files in general — useful now, but not ideal native source modules
- `reflection-journal.md` / `curator-journal.md` — valuable logs/reference, but probably optional, not default context

## Likely new native modules we don’t quite have cleanly yet
I think these should probably exist explicitly, even if they’re currently only implicit or mixed together:

- **runtime-truth**
  - currently embedded in `pinned-state.md`
- **current-thread**
  - currently embedded in `pinned-state.md`
- **active-decisions**
  - mostly `decision-state.json`, may


be with a trimmed view
- **open-actions**
  - currently split across `todo.md` and `operational-synthesis/actions.jsonl`
- **user-preferences**
  - currently scattered across `curated-memory.md`
- **core-operating-rules**
  - `behavior-rules.md`, maybe merged with part of character data

## My practical recommendation
For native migration, I’d classify like this:

**Keep as source-like modules**
- `character.json`
- `behavior-rules.md`
- `curated-memory.md`
- `decision-state.json`
- `knowledge-base.jsonl`
- `recent-topics.md`
- `recent-failures.json`
- `operational-synthesis/notices.jsonl`
- `operational-synthesis/actions.jsonl`

**Keep as reference/optional**
- `curator-journal.md`
- `reflection-journal.md`
- `semantic-memory.json`

**Treat as derived/legacy compatibility**
- `pinned-state.md`
- `continuity-summary.txt`
- all packet files

**Probably replace with cleaner native-first modules**
- runtime truth
- current thread
- open actions
- explicit user preferences

If you want, next I can turn this into a sharper table:
**artifact → proposed native module → default/optional → change frequency → keep/split/demote/replace**.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 72.1s
Time: model 68.0s, overhead 4.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 460 o 1.2k cache 140.8k/100% est $0.0187; p≈2.7ktok/10.9kch
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 9.2%; usable 9.4%; observed turns 16; actual turns 17


Agreed, it doesn't make sense to have session packets if we don't have sessions.
The analog of current reload would probably be dropping or compacting older tail, the idea is that it runs as async subagent, then you check the result and decide what other artifacts to modify, add this compaction summary and drop older tail.

Yes a sharper table would be great


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 17


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 17


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 17


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.9s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.9ktok/11.5kch
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 17


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that framing makes a lot of sense.

So the native analog is not “reload a session,” but more like:
- compact or trim old tail/context inputs
- produce a summary artifact
- review it
- selectively update durable modules
- then stop loading the dropped tail

That’s much cleaner than ACP reload mythology.

Here’s the sharper table:

| Existing artifact | Proposed native module | Default? | Change frequency | Recommendation |
|---|---|---:|---|---|
| `character.json` | core-identity | yes | very low | **keep** |
| `behavior-rules.md` | operating-rules | yes | very low | **keep** |
| `curated-memory.md` | durable-memory | yes | low | **keep, later split** |
| `decision-state.json` | active-decisions | yes | low/medium | **keep** |
| `knowledge-base.jsonl` | external-knowledge | no | low/medium | **keep optional** |
| `recent-topics.md` | recent-topics | maybe | medium | **


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that framing makes a lot of sense.

So the native analog is not “reload a session,” but more like:
- compact or trim old tail/context inputs
- produce a summary artifact
- review it
- selectively update durable modules
- then stop loading the dropped tail

That’s much cleaner than ACP reload mythology.

Here’s the sharper table:

| Existing artifact | Proposed native module | Default? | Change frequency | Recommendation |
|---|---|---:|---|---|
| `character.json` | core-identity | yes | very low | **keep** |
| `behavior-rules.md` | operating-rules | yes | very low | **keep** |
| `curated-memory.md` | durable-memory | yes | low | **keep, later split** |
| `decision-state.json` | active-decisions | yes | low/medium | **keep** |
| `knowledge-base.jsonl` | external-knowledge | no | low/medium | **keep optional** |
| `recent-topics.md` | recent-topics | maybe | medium | **keep, maybe trim** |
| `recent-failures.json` | recent-failures | maybe | medium | **keep, refresh/clean** |
| `operational-synthesis/notices.jsonl` | actionable-notices | maybe | medium/high | **keep, probably filtered view** |
| `operational-synthesis/actions.jsonl` | open-actions | maybe | medium | **keep** |
| `operational-synthesis/latest.md` | synthesis-summary | no | medium | **optional/reference** |
| `semantic-memory.json` | explicit-memories | no | medium | **keep backend, clean before foregrounding** |
| `continuity-pinned-notes.txt` | explicit-memory-dump | no | medium | **demote or replace** |
| `curator-journal.md` | curator-journal | no | high | **reference only** |
| `reflection-journal.md` | reflection-journal | no | high | **reference only** |
| `todo.md` | dashboard/open-actions view | no | medium | **derived view, not source**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that framing makes a lot of sense.

So the native analog is not “reload a session,” but more like:
- compact or trim old tail/context inputs
- produce a summary artifact
- review it
- selectively update durable modules
- then stop loading the dropped tail

That’s much cleaner than ACP reload mythology.

Here’s the sharper table:

| Existing artifact | Proposed native module | Default? | Change frequency | Recommendation |
|---|---|---:|---|---|
| `character.json` | core-identity | yes | very low | **keep** |
| `behavior-rules.md` | operating-rules | yes | very low | **keep** |
| `curated-memory.md` | durable-memory | yes | low | **keep, later split** |
| `decision-state.json` | active-decisions | yes | low/medium | **keep** |
| `knowledge-base.jsonl` | external-knowledge | no | low/medium | **keep optional** |
| `recent-topics.md` | recent-topics | maybe | medium | **keep, maybe trim** |
| `recent-failures.json` | recent-failures | maybe | medium | **keep, refresh/clean** |
| `operational-synthesis/notices.jsonl` | actionable-notices | maybe | medium/high | **keep, probably filtered view** |
| `operational-synthesis/actions.jsonl` | open-actions | maybe | medium | **keep** |
| `operational-synthesis/latest.md` | synthesis-summary | no | medium | **optional/reference** |
| `semantic-memory.json` | explicit-memories | no | medium | **keep backend, clean before foregrounding** |
| `continuity-pinned-notes.txt` | explicit-memory-dump | no | medium | **demote or replace** |
| `curator-journal.md` | curator-journal | no | high | **reference only** |
| `reflection-journal.md` | reflection-journal | no | high | **reference only** |
| `todo.md` | dashboard/open-actions view | no | medium | **derived view, not source** |
| `pinned-state.md` | runtime + thread aggregate | no | high | **replace with native modules** |
| `continuity-summary.txt` | legacy continuity summary | no | medium/high | **retire or replace** |
| `turn-packet.md` | legacy turn packet | no | per-turn | **retire / compatibility only** |
| `session-packet.md` | legacy session packet | no | per-session | **retire** |
| `session-start-packet.md` | legacy bootstrap packet | no | per-session | **retire** |
| `restore-packet.md` | legacy restore packet | no | per-session | **retire** |

And the missing native-first modules I’d explicitly introduce:

| New module | Purpose | Default? | Change frequency |
|---|---|---:|---|
| `runtime-truth` | current backend/model/capabilities/trustworthy live state | yes | medium |
| `current-thread` | compact active thread + pending next action | yes | high |
| `open-actions` | actionable


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yes — that framing makes a lot of sense.

So the native analog is not “reload a session,” but more like:
- compact or trim old tail/context inputs
- produce a summary artifact
- review it
- selectively update durable modules
- then stop loading the dropped tail

That’s much cleaner than ACP reload mythology.

Here’s the sharper table:

| Existing artifact | Proposed native module | Default? | Change frequency | Recommendation |
|---|---|---:|---|---|
| `character.json` | core-identity | yes | very low | **keep** |
| `behavior-rules.md` | operating-rules | yes | very low | **keep** |
| `curated-memory.md` | durable-memory | yes | low | **keep, later split** |
| `decision-state.json` | active-decisions | yes | low/medium | **keep** |
| `knowledge-base.jsonl` | external-knowledge | no | low/medium | **keep optional** |
| `recent-topics.md` | recent-topics | maybe | medium | **keep, maybe trim** |
| `recent-failures.json` | recent-failures | maybe | medium | **keep, refresh/clean** |
| `operational-synthesis/notices.jsonl` | actionable-notices | maybe | medium/high | **keep, probably filtered view** |
| `operational-synthesis/actions.jsonl` | open-actions | maybe | medium | **keep** |
| `operational-synthesis/latest.md` | synthesis-summary | no | medium | **optional/reference** |
| `semantic-memory.json` | explicit-memories | no | medium | **keep backend, clean before foregrounding** |
| `continuity-pinned-notes.txt` | explicit-memory-dump | no | medium | **demote or replace** |
| `curator-journal.md` | curator-journal | no | high | **reference only** |
| `reflection-journal.md` | reflection-journal | no | high | **reference only** |
| `todo.md` | dashboard/open-actions view | no | medium | **derived view, not source** |
| `pinned-state.md` | runtime + thread aggregate | no | high | **replace with native modules** |
| `continuity-summary.txt` | legacy continuity summary | no | medium/high | **retire or replace** |
| `turn-packet.md` | legacy turn packet | no | per-turn | **retire / compatibility only** |
| `session-packet.md` | legacy session packet | no | per-session | **retire** |
| `session-start-packet.md` | legacy bootstrap packet | no | per-session | **retire** |
| `restore-packet.md` | legacy restore packet | no | per-session | **retire** |

And the missing native-first modules I’d explicitly introduce:

| New module | Purpose | Default? | Change frequency |
|---|---|---:|---|
| `runtime-truth` | current backend/model/capabilities/trustworthy live state | yes | medium |
| `current-thread` | compact active thread + pending next action | yes | high |
| `open-actions` | actionable tasks only | maybe | medium |
| `user-preferences` | extracted stable preferences from curated memory | yes | low |
| `compaction-summaries` | reviewed summaries produced when old tail is compacted/dropped | maybe | low/medium |
| `tail-window` | recent raw/near-raw conversation tail kept temporarily | yes-ish | high |
| `temporary-skills` | one-turn or short-lived loaded skills/modules | no | per-turn |

My strongest architectural opinion:
- **source artifacts should be small, purpose-specific, and editable**
- **aggregate packets should become derived products, not primary memory**
- **compaction summaries should be reviewed before they promote into durable modules**

If you want, next I can turn this into a proposed **module load order for caching**:
stable prefix → semi-stable prefix → operational layer → hot tail → per-turn additions.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 23.3s
Time: model 20.5s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 120.6k o 853 cache 24.1k/17% est $0.3142; p≈2.9ktok/11.5kch
Ctx: direct ACP session 142.4k; active 1.6k; cache 140.8k/100%; p≈2.7ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


Yes — that framing makes a lot of sense.

So the native analog is not “reload a session,” but more like:
- compact or trim old tail/context inputs
- produce a summary artifact
- review it
- selectively update durable modules
- then stop loading the dropped tail

That’s much cleaner than ACP reload mythology.

Here’s the sharper table:

| Existing artifact | Proposed native module | Default? | Change frequency | Recommendation |
|---|---|---:|---|---|
| `character.json` | core-identity | yes | very low | **keep** |
| `behavior-rules.md` | operating-rules | yes | very low | **keep** |
| `curated-memory.md` | durable-memory | yes | low | **keep, later split** |
| `decision-state.json` | active-decisions | yes | low/medium | **keep** |
| `knowledge-base.jsonl` | external-knowledge | no | low/medium | **keep optional** |
| `recent-topics.md` | recent-topics | maybe | medium | **keep, maybe trim** |
| `recent-failures.json` | recent-failures | maybe | medium | **keep, refresh/clean** |
| `operational-synthesis/notices.jsonl` | actionable-notices | maybe | medium/high | **keep, probably filtered view** |
| `operational-synthesis/actions.jsonl` | open-actions | maybe | medium | **keep** |
| `operational-synthesis/latest.md` | synthesis-summary | no | medium | **optional/reference** |
| `semantic-memory.json` | explicit-memories | no | medium | **keep backend, clean before foregrounding** |
| `continuity-pinned-notes.txt` | explicit-memory-dump | no | medium | **demote or replace** |
| `curator-journal.md` | curator-journal | no | high | **reference only** |
| `reflection-journal.md` | reflection-journal | no | high | **reference only** |
| `todo.md` | dashboard/open-actions view | no | medium | **derived view, not source** |
| `pinned-state.md` | runtime + thread aggregate | no | high | **replace with native modules** |
| `continuity-summary.txt` | legacy continuity summary | no | medium/high | **retire or replace** |
| `turn-packet.md` | legacy turn packet | no | per-turn | **retire / compatibility only** |
| `session-packet.md` | legacy session packet | no | per-session | **retire** |
| `session-start-packet.md` | legacy bootstrap packet | no | per-session | **retire** |
| `restore-packet.md` | legacy restore packet | no | per-session | **retire** |

And the missing native-first modules I’d explicitly introduce:

| New module | Purpose | Default? | Change frequency |
|---|---|---:|---|
| `runtime-truth` | current backend/model/capabilities/trustworthy live state | yes | medium |
| `current-thread` | compact active thread + pending next action | yes | high |
| `open-actions` | actionable tasks only | maybe | medium |
| `user-preferences` | extracted stable preferences from curated memory | yes | low |
| `compaction-summaries` | reviewed summaries produced when old tail is compacted/dropped | maybe | low/medium |
| `tail-window` | recent raw/near-raw conversation tail kept temporarily | yes-ish | high |
| `temporary-skills` | one-turn or short-lived loaded skills/modules | no | per-turn |

My strongest architectural opinion:
- **source artifacts should be small, purpose-specific, and editable**
- **aggregate packets should become derived products, not primary memory**
- **compaction summaries should be reviewed before they promote into durable modules**

If you want, next I can turn this into a proposed **module load order for caching**:
stable prefix → semi-stable prefix → operational layer → hot tail → per-turn additions.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 24.5s
Time: model 20.5s, overhead 3.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 120.6k o 853 cache 24.1k/17% est $0.3142; p≈2.9ktok/11.5kch
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


Yes please


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.0s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 0.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.7s
Time: overhead 1.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.5ktok/10.1kch
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 18


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Great — here’s the load order I’d aim for for native caching and control:

## 1. Stable prefix — almost never changes
Load first, maximize cache reuse.

- `core-identity` ← `character.json`
- `operating-rules` ← `behavior-rules.md`
- `user-preferences` ← extracted/split from `curated-memory.md`
- maybe `durable-memory-core` ← the most important stable part of `curated-memory.md`

Goal:
- this prefix should stay very compact and very stable

## 2. Semi-stable prefix — occasional updates
Still cache-friendly, but revised sometimes.

- `durable-memory` ← remainder of `curated-memory.md`
- `active-decisions` ← `decision-state.json`
- `architecture-facts` ← either split from curated memory or a dedicated artifact later
- `external-knowledge-index` ← lightweight pointer/index, not full knowledge dump

Goal:
- important long-term orientation
- not too noisy
- still fairly reusable between turns

## 3. Operational layer — medium churn
Useful


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Great — here’s the load order I’d aim for for native caching and control:

## 1. Stable prefix — almost never changes
Load first, maximize cache reuse.

- `core-identity` ← `character.json`
- `operating-rules` ← `behavior-rules.md`
- `user-preferences` ← extracted/split from `curated-memory.md`
- maybe `durable-memory-core` ← the most important stable part of `curated-memory.md`

Goal:
- this prefix should stay very compact and very stable

## 2. Semi-stable prefix — occasional updates
Still cache-friendly, but revised sometimes.

- `durable-memory` ← remainder of `curated-memory.md`
- `active-decisions` ← `decision-state.json`
- `architecture-facts` ← either split from curated memory or a dedicated artifact later
- `external-knowledge-index` ← lightweight pointer/index, not full knowledge dump

Goal:
- important long-term orientation
- not too noisy
- still fairly reusable between turns

## 3. Operational layer — medium churn
Useful often, but shouldn’t poison the stable prefix.

- `open-actions` ← `operational-synthesis/actions.jsonl` / maybe cleaned view
- `actionable-notices` ← filtered `notices.jsonl`
- `recent-failures` ← `recent-failures.json`
- `recent-topics` ← `recent-topics.md`

Goal:
- active project / system state
- loaded by default only if useful enough
- maybe some of these become conditional

## 4. Hot context layer — high churn
Changes often, likely every few turns.

- `current-thread`
- `runtime-truth`
- `tail-window`
- maybe `compaction-summary-latest` if relevant

Goal:
- keep this late in the prompt so churn doesn’t invalidate the earlier cacheable prefix

## 5. Per-turn additions — ephemeral
Load only when needed.

- temporary skills
- task-specific retrievals
- topic knowledge slices
- one-off repo/runtime summaries
- ad hoc investigation context

Goal:
- highly disposable
- never part of the stable baseline


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Great — here’s the load order I’d aim for for native caching and control:

## 1. Stable prefix — almost never changes
Load first, maximize cache reuse.

- `core-identity` ← `character.json`
- `operating-rules` ← `behavior-rules.md`
- `user-preferences` ← extracted/split from `curated-memory.md`
- maybe `durable-memory-core` ← the most important stable part of `curated-memory.md`

Goal:
- this prefix should stay very compact and very stable

## 2. Semi-stable prefix — occasional updates
Still cache-friendly, but revised sometimes.

- `durable-memory` ← remainder of `curated-memory.md`
- `active-decisions` ← `decision-state.json`
- `architecture-facts` ← either split from curated memory or a dedicated artifact later
- `external-knowledge-index` ← lightweight pointer/index, not full knowledge dump

Goal:
- important long-term orientation
- not too noisy
- still fairly reusable between turns

## 3. Operational layer — medium churn
Useful often, but shouldn’t poison the stable prefix.

- `open-actions` ← `operational-synthesis/actions.jsonl` / maybe cleaned view
- `actionable-notices` ← filtered `notices.jsonl`
- `recent-failures` ← `recent-failures.json`
- `recent-topics` ← `recent-topics.md`

Goal:
- active project / system state
- loaded by default only if useful enough
- maybe some of these become conditional

## 4. Hot context layer — high churn
Changes often, likely every few turns.

- `current-thread`
- `runtime-truth`
- `tail-window`
- maybe `compaction-summary-latest` if relevant

Goal:
- keep this late in the prompt so churn doesn’t invalidate the earlier cacheable prefix

## 5. Per-turn additions — ephemeral
Load only when needed.

- temporary skills
- task-specific retrievals
- topic knowledge slices
- one-off repo/runtime summaries
- ad hoc investigation context

Goal:
- highly disposable
- never part of the stable baseline

---

# Rough rule of thumb

**Earlier in prompt:**
- stable
- high value
- low churn
- broadly useful

**Later in prompt:**
- volatile
- task-specific
- easy to recompute
- easy to omit

---

# Practical artifact-to-layer mapping

### Layer 1
- `character.json`
- `behavior-rules.md`
- split-out stable user preferences

### Layer 2
- `curated-memory.md` (after splitting if needed)
- `decision-state.json`
- maybe a future `architecture-facts.md`

### Layer 3
- `recent-topics.md`
- `recent-failures.json`
- filtered `operational-synthesis/actions.jsonl`
- filtered `operational-synthesis/notices.jsonl`

### Layer 4
- native `current-thread`
- native `runtime-truth`
- recent tail
- latest compaction summary

### Layer 5
- temporary loaded skills
- retrieval snippets
- topic packs
- task-local scratch modules

---

# Important design opinion

I would strongly avoid


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Tools (18):
- Runs direct ACP script continuity test / completed / Runs direct ACP script continuity test
- Runs targeted daemon stability tests / completed / Runs targeted daemon stability tests
- Runs direct ACP script continuity test / completed / Runs direct ACP script continuity test
- Runs targeted daemon stability tests / completed / Runs targeted daemon stability tests
- 1 todos / completed / call_tiJRiOFrjrO6xFj8XMlnvWv
- 3 todos / completed / call_2GS1nFvATZGqMAspbSOjOqZ
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- Success. Updated the following files:
M ../../app/tests/test_continuity_scripts. / completed / call_GOM2IAFdY2OYOkzcc3PxGxj
- Success. Updated the following files:
M ../../app/tests/test_daemon_stability.py / completed / call_aX5JNaZ9erV01Z62Aa6WeWm
- Success. Updated the following files:
M ../../app/scripts/matrix-lite-direct-acp / completed / call_wJjF5nttXle9VsNWgrtiMif
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- ../../app/tests/test_continuity_scripts.py / completed / /home/matrix-lite/app/tests/test_continuity_scripts.py
- ../../app/tests/test_daemon_stability.py / completed / /home/matrix-lite/app/tests/test_daemon_stability.py
- matrix-lite-direct-acp-client.mjs|selftest-final-text|runPersistentServer|prompt / completed / /home/matrix-lite/app/tests
- await sleep\(250\)|replay_drain_done|possible_mid_sentence_cutoff|streamed_text_ / completed / /home/matrix-lite/app/tests

Draft:
Great — here’s the load order I’d aim for for native caching and control:

## 1. Stable prefix — almost never changes
Load first, maximize cache reuse.

- `core-identity` ← `character.json`
- `operating-rules` ← `behavior-rules.md`
- `user-preferences` ← extracted/split from `curated-memory.md`
- maybe `durable-memory-core` ← the most important stable part of `curated-memory.md`

Goal:
- this prefix should stay very compact and very stable

## 2. Semi-stable prefix — occasional updates
Still cache-friendly, but revised sometimes.

- `durable-memory` ← remainder of `curated-memory.md`
- `active-decisions` ← `decision-state.json`
- `architecture-facts` ← either split from curated memory or a dedicated artifact later
- `external-knowledge-index` ← lightweight pointer/index, not full knowledge dump

Goal:
- important long-term orientation
- not too noisy
- still fairly reusable between turns

## 3. Operational layer — medium churn
Useful often, but shouldn’t poison the stable prefix.

- `open-actions` ← `operational-synthesis/actions.jsonl` / maybe cleaned view
- `actionable-notices` ← filtered `notices.jsonl`
- `recent-failures` ← `recent-failures.json`
- `recent-topics` ← `recent-topics.md`

Goal:
- active project / system state
- loaded by default only if useful enough
- maybe some of these become conditional

## 4. Hot context layer — high churn
Changes often, likely every few turns.

- `current-thread`
- `runtime-truth`
- `tail-window`
- maybe `compaction-summary-latest` if relevant

Goal:
- keep this late in the prompt so churn doesn’t invalidate the earlier cacheable prefix

## 5. Per-turn additions — ephemeral
Load only when needed.

- temporary skills
- task-specific retrievals
- topic knowledge slices
- one-off repo/runtime summaries
- ad hoc investigation context

Goal:
- highly disposable
- never part of the stable baseline

---

# Rough rule of thumb

**Earlier in prompt:**
- stable
- h


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 24.3s
Time: model 21.6s, overhead 1.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 52.0k o 831 cache 95.7k/65% est $0.1424; p≈2.5ktok/10.1kch
Ctx: direct ACP session 145.5k; active 121.4k; cache 24.1k/17%; p≈2.9ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 19


Great — here’s the load order I’d aim for for native caching and control:

## 1. Stable prefix — almost never changes
Load first, maximize cache reuse.

- `core-identity` ← `character.json`
- `operating-rules` ← `behavior-rules.md`
- `user-preferences` ← extracted/split from `curated-memory.md`
- maybe `durable-memory-core` ← the most important stable part of `curated-memory.md`

Goal:
- this prefix should stay very compact and very stable

## 2. Semi-stable prefix — occasional updates
Still cache-friendly, but revised sometimes.

- `durable-memory` ← remainder of `curated-memory.md`
- `active-decisions` ← `decision-state.json`
- `architecture-facts` ← either split from curated memory or a dedicated artifact later
- `external-knowledge-index` ← lightweight pointer/index, not full knowledge dump

Goal:
- important long-term orientation
- not too noisy
- still fairly reusable between turns

## 3. Operational layer — medium churn
Useful often, but shouldn’t poison the stable prefix.

- `open-actions` ← `operational-synthesis/actions.jsonl` / maybe cleaned view
- `actionable-notices` ← filtered `notices.jsonl`
- `recent-failures` ← `recent-failures.json`
- `recent-topics` ← `recent-topics.md`

Goal:
- active project / system state
- loaded by default only if useful enough
- maybe some of these become conditional

## 4. Hot context layer — high churn
Changes often, likely every few turns.

- `current-thread`
- `runtime-truth`
- `tail-window`
- maybe `compaction-summary-latest` if relevant

Goal:
- keep this late in the prompt so churn doesn’t invalidate the earlier cacheable prefix

## 5. Per-turn additions — ephemeral
Load only when needed.

- temporary skills
- task-specific retrievals
- topic knowledge slices
- one-off repo/runtime summaries
- ad hoc investigation context

Goal:
- highly disposable
- never part of the stable baseline

---

# Rough rule of thumb

**Earlier in prompt:**
- stable
- high value
- low churn
- broadly useful

**Later in prompt:**
- volatile
- task-specific
- easy to recompute
- easy to omit

---

# Practical artifact-to-layer mapping

### Layer 1
- `character.json`
- `behavior-rules.md`
- split-out stable user preferences

### Layer 2
- `curated-memory.md` (after splitting if needed)
- `decision-state.json`
- maybe a future `architecture-facts.md`

### Layer 3
- `recent-topics.md`
- `recent-failures.json`
- filtered `operational-synthesis/actions.jsonl`
- filtered `operational-synthesis/notices.jsonl`

### Layer 4
- native `current-thread`
- native `runtime-truth`
- recent tail
- latest compaction summary

### Layer 5
- temporary loaded skills
- retrieval snippets
- topic packs
- task-local scratch modules

---

# Important design opinion

I would strongly avoid:
- putting rapidly changing timestamps near the top
- mixing runtime truth with durable memory
- loading raw journals by default
- loading full knowledge-base content by default
- making compaction summaries automatically authoritative without review

I would strongly prefer:
- tiny stable modules near the top
- derived filtered views for noisy artifacts
- explicit optional modules for topic knowledge
- reviewed compaction summaries that can then update durable modules

So yes — I think the ideal native stack is basically:

**stable identity → stable memory → operational state → hot thread/tail → per-turn temporary modules**

If you want, next I can turn this into a more concrete proposal like:
**module name, source artifact, default loaded?, allowed size budget, and whether it should be source or derived.**


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 25.4s
Time: model 21.6s, overhead 3.8s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 52.0k o 831 cache 95.7k/65% est $0.1424; p≈2.5ktok/10.1kch
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 13.6%; usable 13.8%; observed turns 17; actual turns 19


Yes please any suggestions are welcome


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 19


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 19


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.3s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 19


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.1s
Time: overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.6ktok/10.3kch
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 19


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — a few concrete suggestions come to mind.

## 1. Split `curated-memory.md` sooner rather than later
Right now it’s carrying too many roles at once.

I’d split it into at least:
- **user-preferences / user-facts**
- **system-history / architecture-facts**
- maybe **operational protocols**

That gives you:
- cleaner caching
- easier selective loading
- less accidental churn in the “most stable” layer

## 2. Make `current-thread` a first-class native module
Not a packet excerpt, not buried in a status aggregate.

It should be a compact artifact with only:
- active topic
- pending next action
- maybe 3–5 recent crucial turns/events

This is probably one of the highest-value modules in the whole system.

## 3. Separate source artifacts from derived views very strictly
This feels important for avoiding future confusion.

For example:
- **source**: decisions, preferences, actions, notices
- **derived**:


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — a few concrete suggestions come to mind.

## 1. Split `curated-memory.md` sooner rather than later
Right now it’s carrying too many roles at once.

I’d split it into at least:
- **user-preferences / user-facts**
- **system-history / architecture-facts**
- maybe **operational protocols**

That gives you:
- cleaner caching
- easier selective loading
- less accidental churn in the “most stable” layer

## 2. Make `current-thread` a first-class native module
Not a packet excerpt, not buried in a status aggregate.

It should be a compact artifact with only:
- active topic
- pending next action
- maybe 3–5 recent crucial turns/events

This is probably one of the highest-value modules in the whole system.

## 3. Separate source artifacts from derived views very strictly
This feels important for avoiding future confusion.

For example:
- **source**: decisions, preferences, actions, notices
- **derived**: packets, dashboards, summaries, prompt-assembled views

That way the native backend edits source modules, never its own compiled prompt products.

## 4. Treat compaction summaries as reviewable proposals, not truth
Your framing already points this way, and I strongly agree.

Good flow:
- async compaction subagent summarizes old tail
- summary lands in `compaction-summaries`
- main agent or user reviews it
- only then promote pieces into durable modules
- then old tail can be dropped

That prevents bad summaries from silently rewriting memory.

## 5. Add explicit size budgets per module
Not just load order.

Something like:
- core identity: tiny
- operating rules: tiny
- user preferences: small
- durable memory: small/medium
- current thread: small
- notices/actions: very small filtered views
- raw journals: never default

That will help prevent gradual bloat.

## 6


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — a few concrete suggestions come to mind.

## 1. Split `curated-memory.md` sooner rather than later
Right now it’s carrying too many roles at once.

I’d split it into at least:
- **user-preferences / user-facts**
- **system-history / architecture-facts**
- maybe **operational protocols**

That gives you:
- cleaner caching
- easier selective loading
- less accidental churn in the “most stable” layer

## 2. Make `current-thread` a first-class native module
Not a packet excerpt, not buried in a status aggregate.

It should be a compact artifact with only:
- active topic
- pending next action
- maybe 3–5 recent crucial turns/events

This is probably one of the highest-value modules in the whole system.

## 3. Separate source artifacts from derived views very strictly
This feels important for avoiding future confusion.

For example:
- **source**: decisions, preferences, actions, notices
- **derived**: packets, dashboards, summaries, prompt-assembled views

That way the native backend edits source modules, never its own compiled prompt products.

## 4. Treat compaction summaries as reviewable proposals, not truth
Your framing already points this way, and I strongly agree.

Good flow:
- async compaction subagent summarizes old tail
- summary lands in `compaction-summaries`
- main agent or user reviews it
- only then promote pieces into durable modules
- then old tail can be dropped

That prevents bad summaries from silently rewriting memory.

## 5. Add explicit size budgets per module
Not just load order.

Something like:
- core identity: tiny
- operating rules: tiny
- user preferences: small
- durable memory: small/medium
- current thread: small
- notices/actions: very small filtered views
- raw journals: never default

That will help prevent gradual bloat.

## 6. Make “optional topic packs” a real concept
For things like:
- WGT
- matrix-lite architecture
- housing workflow
- videoctl
- future project-specific domains

These should be:
- discoverable
- loadable on demand
- absent by default

That feels much better than forcing all useful knowledge into the always-on context.

## 7. Clean or demote low-quality memory artifacts
Especially:
- `continuity-pinned-notes.txt`
- parts of `semantic-memory.json`
- stale summary artifacts

Some of those feel like useful historical residue, but not good native prompt modules.

## 8. Keep runtime truth separate from memory
Very important.

“Current model/backend/capabilities/state” should live in a **runtime-truth** module, not in curated memory or continuity summaries.
That avoids stale factual contamination.

## 9. Consider a two-level action model
Instead


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah — a few concrete suggestions come to mind.

## 1. Split `curated-memory.md` sooner rather than later
Right now it’s carrying too many roles at once.

I’d split it into at least:
- **user-preferences / user-facts**
- **system-history / architecture-facts**
- maybe **operational protocols**

That gives you:
- cleaner caching
- easier selective loading
- less accidental churn in the “most stable” layer

## 2. Make `current-thread` a first-class native module
Not a packet excerpt, not buried in a status aggregate.

It should be a compact artifact with only:
- active topic
- pending next action
- maybe 3–5 recent crucial turns/events

This is probably one of the highest-value modules in the whole system.

## 3. Separate source artifacts from derived views very strictly
This feels important for avoiding future confusion.

For example:
- **source**: decisions, preferences, actions, notices
- **derived**: packets, dashboards, summaries, prompt-assembled views

That way the native backend edits source modules, never its own compiled prompt products.

## 4. Treat compaction summaries as reviewable proposals, not truth
Your framing already points this way, and I strongly agree.

Good flow:
- async compaction subagent summarizes old tail
- summary lands in `compaction-summaries`
- main agent or user reviews it
- only then promote pieces into durable modules
- then old tail can be dropped

That prevents bad summaries from silently rewriting memory.

## 5. Add explicit size budgets per module
Not just load order.

Something like:
- core identity: tiny
- operating rules: tiny
- user preferences: small
- durable memory: small/medium
- current thread: small
- notices/actions: very small filtered views
- raw journals: never default

That will help prevent gradual bloat.

## 6. Make “optional topic packs” a real concept
For things like:
- WGT
- matrix-lite architecture
- housing workflow
- videoctl
- future project-specific domains

These should be:
- discoverable
- loadable on demand
- absent by default

That feels much better than forcing all useful knowledge into the always-on context.

## 7. Clean or demote low-quality memory artifacts
Especially:
- `continuity-pinned-notes.txt`
- parts of `semantic-memory.json`
- stale summary artifacts

Some of those feel like useful historical residue, but not good native prompt modules.

## 8. Keep runtime truth separate from memory
Very important.

“Current model/backend/capabilities/state” should live in a **runtime-truth** module, not in curated memory or continuity summaries.
That avoids stale factual contamination.

## 9. Consider a two-level action model
Instead of one `open-actions` blob:

- **durable actions**
  - important, survives compaction
- **working set**
  - immediate short-horizon tasks

That might help keep the hot layer lighter.

## 10. Design for subtraction, not just addition
This is maybe the biggest native advantage.

Every module should be easy to:
- load
- skip
- replace
- expire
- compact
- archive

If a module can only grow, it’ll eventually recreate ACP-style baggage.

---

If I had to prioritize just **three** design moves:

1. **Split curated memory**
2. **Create first-class current-thread**
3. **Make compaction summaries review-gated before promotion**

Those three alone would already make the native architecture feel much cleaner.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 23.6s
Time: model 20.3s, overhead 2.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 110.6k o 750 t 15 est $0.2879; p≈2.6ktok/10.3kch
Ctx: direct ACP session 148.5k; active 52.8k; cache 95.7k/65%; p≈2.5ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 20


Yeah — a few concrete suggestions come to mind.

## 1. Split `curated-memory.md` sooner rather than later
Right now it’s carrying too many roles at once.

I’d split it into at least:
- **user-preferences / user-facts**
- **system-history / architecture-facts**
- maybe **operational protocols**

That gives you:
- cleaner caching
- easier selective loading
- less accidental churn in the “most stable” layer

## 2. Make `current-thread` a first-class native module
Not a packet excerpt, not buried in a status aggregate.

It should be a compact artifact with only:
- active topic
- pending next action
- maybe 3–5 recent crucial turns/events

This is probably one of the highest-value modules in the whole system.

## 3. Separate source artifacts from derived views very strictly
This feels important for avoiding future confusion.

For example:
- **source**: decisions, preferences, actions, notices
- **derived**: packets, dashboards, summaries, prompt-assembled views

That way the native backend edits source modules, never its own compiled prompt products.

## 4. Treat compaction summaries as reviewable proposals, not truth
Your framing already points this way, and I strongly agree.

Good flow:
- async compaction subagent summarizes old tail
- summary lands in `compaction-summaries`
- main agent or user reviews it
- only then promote pieces into durable modules
- then old tail can be dropped

That prevents bad summaries from silently rewriting memory.

## 5. Add explicit size budgets per module
Not just load order.

Something like:
- core identity: tiny
- operating rules: tiny
- user preferences: small
- durable memory: small/medium
- current thread: small
- notices/actions: very small filtered views
- raw journals: never default

That will help prevent gradual bloat.

## 6. Make “optional topic packs” a real concept
For things like:
- WGT
- matrix-lite architecture
- housing workflow
- videoctl
- future project-specific domains

These should be:
- discoverable
- loadable on demand
- absent by default

That feels much better than forcing all useful knowledge into the always-on context.

## 7. Clean or demote low-quality memory artifacts
Especially:
- `continuity-pinned-notes.txt`
- parts of `semantic-memory.json`
- stale summary artifacts

Some of those feel like useful historical residue, but not good native prompt modules.

## 8. Keep runtime truth separate from memory
Very important.

“Current model/backend/capabilities/state” should live in a **runtime-truth** module, not in curated memory or continuity summaries.
That avoids stale factual contamination.

## 9. Consider a two-level action model
Instead of one `open-actions` blob:

- **durable actions**
  - important, survives compaction
- **working set**
  - immediate short-horizon tasks

That might help keep the hot layer lighter.

## 10. Design for subtraction, not just addition
This is maybe the biggest native advantage.

Every module should be easy to:
- load
- skip
- replace
- expire
- compact
- archive

If a module can only grow, it’ll eventually recreate ACP-style baggage.

---

If I had to prioritize just **three** design moves:

1. **Split curated memory**
2. **Create first-class current-thread**
3. **Make compaction summaries review-gated before promotion**

Those three alone would already make the native architecture feel much cleaner.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 24.3s
Time: model 20.3s, overhead 4.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 110.6k o 750 t 15 est $0.2879; p≈2.6ktok/10.3kch
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 14.2%; usable 14.4%; observed turns 19; actual turns 20


These are all really good suggestions. For knowledge base we might search for existing tools if there is anything suitable. We also plan to add RAG, so maybe it can work over the existing structure and packs while we think of a better way. Some of the workflows can be regular agent skills, like immoscout, of course customized to your capabilities but I think the structure is similar.


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 20


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 20


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.4s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 20


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.5s
Time: overhead 2.5s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈3.1ktok/12.3kch
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 20


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, I think that’s a very good direction.

A few reactions:

- **Knowledge base:** agreed — don’t overdesign it too early.
  - If existing tools fit, great.
  - If not, a simple interim structure is fine as long as it’s searchable and source-aware.

- **RAG over existing structure:** yes, that seems like the right transitional move.
  - Treat current artifacts/packs as the corpus
  - add retrieval over them
  - then refine the storage model later once usage patterns are clearer

That’s better than pausing everything until the “perfect” knowledge model exists.

- **Packs as RAG units:** I especially like that.
  - workflow packs
  - topic packs
  - architecture packs
  - restoration/continuity packs
  - maybe compact skill metadata on top

That gives you a natural granularity for retrieval.

- **Workflows as skills:** also yes.
  - ImmoScout is a good example
  - not because the exact actions are universal, but because the structure is:
    - prerequisites
    - goals
    - step sequence
    - failure modes
    - escalation points
    - reusable prompts/templates
    - capability constraints

So a “skill” can basically be:
- a reusable procedural scaffold
- optionally paired with retrieval from packs / knowledge / live state

That feels cleaner than forcing everything into one generic memory layer.

Honestly,


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
Yeah, I think that’s a very good direction.

A few reactions:

- **Knowledge base:** agreed — don’t overdesign it too early.
  - If existing tools fit, great.
  - If not, a simple interim structure is fine as long as it’s searchable and source-aware.

- **RAG over existing structure:** yes, that seems like the right transitional move.
  - Treat current artifacts/packs as the corpus
  - add retrieval over them
  - then refine the storage model later once usage patterns are clearer

That’s better than pausing everything until the “perfect” knowledge model exists.

- **Packs as RAG units:** I especially like that.
  - workflow packs
  - topic packs
  - architecture packs
  - restoration/continuity packs
  - maybe compact skill metadata on top

That gives you a natural granularity for retrieval.

- **Workflows as skills:** also yes.
  - ImmoScout is a good example
  - not because the exact actions are universal, but because the structure is:
    - prerequisites
    - goals
    - step sequence
    - failure modes
    - escalation points
    - reusable prompts/templates
    - capability constraints

So a “skill” can basically be:
- a reusable procedural scaffold
- optionally paired with retrieval from packs / knowledge / live state

That feels cleaner than forcing everything into one generic memory layer.

Honestly, this combination seems quite strong:

- **modules** for stable loaded context
- **RAG** for selective retrieval over packs/artifacts
- **skills** for reusable procedures/workflows

That gives you:
- stable identity and rules
- flexible recall
- reusable behavior patterns

Which is much closer to what we actually want than ACP-style sticky session memory.

If you want, next time we could sketch a rough three-part architecture:
**modules vs RAG corpus vs skills**, and decide what belongs in each.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 16.5s
Time: model 12.4s, overhead 2.5s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 114.1k o 404 t 42 est $0.2919; p≈3.1ktok/12.3kch
Ctx: direct ACP session 111.3k; active 111.3k; p≈2.6ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


Yeah, I think that’s a very good direction.

A few reactions:

- **Knowledge base:** agreed — don’t overdesign it too early.
  - If existing tools fit, great.
  - If not, a simple interim structure is fine as long as it’s searchable and source-aware.

- **RAG over existing structure:** yes, that seems like the right transitional move.
  - Treat current artifacts/packs as the corpus
  - add retrieval over them
  - then refine the storage model later once usage patterns are clearer

That’s better than pausing everything until the “perfect” knowledge model exists.

- **Packs as RAG units:** I especially like that.
  - workflow packs
  - topic packs
  - architecture packs
  - restoration/continuity packs
  - maybe compact skill metadata on top

That gives you a natural granularity for retrieval.

- **Workflows as skills:** also yes.
  - ImmoScout is a good example
  - not because the exact actions are universal, but because the structure is:
    - prerequisites
    - goals
    - step sequence
    - failure modes
    - escalation points
    - reusable prompts/templates
    - capability constraints

So a “skill” can basically be:
- a reusable procedural scaffold
- optionally paired with retrieval from packs / knowledge / live state

That feels cleaner than forcing everything into one generic memory layer.

Honestly, this combination seems quite strong:

- **modules** for stable loaded context
- **RAG** for selective retrieval over packs/artifacts
- **skills** for reusable procedures/workflows

That gives you:
- stable identity and rules
- flexible recall
- reusable behavior patterns

Which is much closer to what we actually want than ACP-style sticky session memory.

If you want, next time we could sketch a rough three-part architecture:
**modules vs RAG corpus vs skills**, and decide what belongs in each.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 17.9s
Time: model 12.4s, overhead 5.5s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 114.1k o 404 t 42 est $0.2919; p≈3.1ktok/12.3kch
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


Yes, how do you think it would work best?


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.1s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 1.9s
Time: overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈2.7ktok/10.7kch
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 21


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


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


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
I think the cleanest version is:

## 1. Modules = always-on structured context
These are the things loaded deliberately into the prompt.

Use modules for:
- identity
- operating rules
- user preferences
- durable memory
- active decisions
- current thread
- maybe a small operational state layer

Properties:
- compact
- curated
- high signal
- explicit load/unload
- predictable cache behavior

So modules are the **trusted working set**.

---

## 2. RAG = on-demand recall over broader artifacts
Use RAG for things that are useful, but not worth loading by default.

Good RAG targets:
- workflow packs
- topic packs
- architecture docs
- old compaction summaries
- knowledge-base entries
- journals/log-derived packs
- maybe older conversation tail once compacted

Properties:
- larger
- more numerous
- searchable
- source-aware
- not always loaded

So RAG is the **library**, not the desk.

---

## 3. Skills = reusable procedures
Skills should not mainly be memory containers.
They should be procedural scaffolds that know:

- when they apply
- prerequisites
- step order
- what to retrieve
- what to ask the user
- common failure modes
- what “done” looks like

A skill can then:
- request certain modules
- trigger RAG lookups for relevant packs
- run the workflow


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
I think the cleanest version is:

## 1. Modules = always-on structured context
These are the things loaded deliberately into the prompt.

Use modules for:
- identity
- operating rules
- user preferences
- durable memory
- active decisions
- current thread
- maybe a small operational state layer

Properties:
- compact
- curated
- high signal
- explicit load/unload
- predictable cache behavior

So modules are the **trusted working set**.

---

## 2. RAG = on-demand recall over broader artifacts
Use RAG for things that are useful, but not worth loading by default.

Good RAG targets:
- workflow packs
- topic packs
- architecture docs
- old compaction summaries
- knowledge-base entries
- journals/log-derived packs
- maybe older conversation tail once compacted

Properties:
- larger
- more numerous
- searchable
- source-aware
- not always loaded

So RAG is the **library**, not the desk.

---

## 3. Skills = reusable procedures
Skills should not mainly be memory containers.
They should be procedural scaffolds that know:

- when they apply
- prerequisites
- step order
- what to retrieve
- what to ask the user
- common failure modes
- what “done” looks like

A skill can then:
- request certain modules
- trigger RAG lookups for relevant packs
- run the workflow

So skills are the **method**, not the memory.

---

# How they fit together in a turn

I’d make the flow something like:

1. **Base modules load**
   - identity
   - rules
   - preferences
   - current thread
   - active decisions

2. **Intent detection**
   - what kind of task is this?
   - coding, architecture, housing, planning, debugging, etc.

3. **Skill selection if relevant**
   - if task matches a workflow, load/use the skill

4. **RAG retrieval**
   - fetch relevant packs/docs/history/knowledge
   - only what’s needed for this turn

5. **Assemble prompt**
   - stable modules first
   - retrieved context later
   - temporary task material last

6. **After the turn**
   - maybe update current-thread
   - maybe update actions/decisions
   - maybe create a compaction summary or pack
   - maybe refine skill metadata if workflow changed

---

# For ImmoScout specifically

I’d model it like:

- **module side**


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
I think the cleanest version is:

## 1. Modules = always-on structured context
These are the things loaded deliberately into the prompt.

Use modules for:
- identity
- operating rules
- user preferences
- durable memory
- active decisions
- current thread
- maybe a small operational state layer

Properties:
- compact
- curated
- high signal
- explicit load/unload
- predictable cache behavior

So modules are the **trusted working set**.

---

## 2. RAG = on-demand recall over broader artifacts
Use RAG for things that are useful, but not worth loading by default.

Good RAG targets:
- workflow packs
- topic packs
- architecture docs
- old compaction summaries
- knowledge-base entries
- journals/log-derived packs
- maybe older conversation tail once compacted

Properties:
- larger
- more numerous
- searchable
- source-aware
- not always loaded

So RAG is the **library**, not the desk.

---

## 3. Skills = reusable procedures
Skills should not mainly be memory containers.
They should be procedural scaffolds that know:

- when they apply
- prerequisites
- step order
- what to retrieve
- what to ask the user
- common failure modes
- what “done” looks like

A skill can then:
- request certain modules
- trigger RAG lookups for relevant packs
- run the workflow

So skills are the **method**, not the memory.

---

# How they fit together in a turn

I’d make the flow something like:

1. **Base modules load**
   - identity
   - rules
   - preferences
   - current thread
   - active decisions

2. **Intent detection**
   - what kind of task is this?
   - coding, architecture, housing, planning, debugging, etc.

3. **Skill selection if relevant**
   - if task matches a workflow, load/use the skill

4. **RAG retrieval**
   - fetch relevant packs/docs/history/knowledge
   - only what’s needed for this turn

5. **Assemble prompt**
   - stable modules first
   - retrieved context later
   - temporary task material last

6. **After the turn**
   - maybe update current-thread
   - maybe update actions/decisions
   - maybe create a compaction summary or pack
   - maybe refine skill metadata if workflow changed

---

# For ImmoScout specifically

I’d model it like:

- **module side**
  - user preferences relevant to housing
  - current housing status
  - stable operating constraints

- **RAG side**
  - ImmoScout workflow pack
  - message templates
  - past application examples
  - known captcha workflow
  - restore pack / troubleshooting notes

- **skill side**
  - login/check auth
  - search/filter
  - inspect listing
  - prepare message
  - handle captcha/escalation
  - send inquiry
  - record result

That seems much better than stuffing the whole thing into one giant memory artifact.

---

# I’d also add one important rule

## Skills should point to RAG, not duplicate it
A skill should say things like:
- “for message template, retrieve pack X”
- “for failure handling, retrieve pack Y”

not contain huge embedded copies of those materials.

That keeps skills:
- small
- stable
- reusable
- cache-friendly

---

# My preferred division of responsibility

### Modules
“What should I broadly know right now?”

### RAG
“What extra material is relevant to this


* 📝 Live progress
This is streamed draft/tool progress, not hidden reasoning.
Draft:
I think the cleanest version is:

## 1. Modules = always-on structured context
These are the things loaded deliberately into the prompt.

Use modules for:
- identity
- operating rules
- user preferences
- durable memory
- active decisions
- current thread
- maybe a small operational state layer

Properties:
- compact
- curated
- high signal
- explicit load/unload
- predictable cache behavior

So modules are the **trusted working set**.

---

## 2. RAG = on-demand recall over broader artifacts
Use RAG for things that are useful, but not worth loading by default.

Good RAG targets:
- workflow packs
- topic packs
- architecture docs
- old compaction summaries
- knowledge-base entries
- journals/log-derived packs
- maybe older conversation tail once compacted

Properties:
- larger
- more numerous
- searchable
- source-aware
- not always loaded

So RAG is the **library**, not the desk.

---

## 3. Skills = reusable procedures
Skills should not mainly be memory containers.
They should be procedural scaffolds that know:

- when they apply
- prerequisites
- step order
- what to retrieve
- what to ask the user
- common failure modes
- what “done” looks like

A skill can then:
- request certain modules
- trigger RAG lookups for relevant packs
- run the workflow

So skills are the **method**, not the memory.

---

# How they fit together in a turn

I’d make the flow something like:

1. **Base modules load**
   - identity
   - rules
   - preferences
   - current thread
   - active decisions

2. **Intent detection**
   - what kind of task is this?
   - coding, architecture, housing, planning, debugging, etc.

3. **Skill selection if relevant**
   - if task matches a workflow, load/use the skill

4. **RAG retrieval**
   - fetch relevant packs/docs/history/knowledge
   - only what’s needed for this turn

5. **Assemble prompt**
   - stable modules first
   - retrieved context later
   - temporary task material last

6. **After the turn**
   - maybe update current-thread
   - maybe update actions/decisions
   - maybe create a compaction summary or pack
   - maybe refine skill metadata if workflow changed

---

# For ImmoScout specifically

I’d model it like:

- **module side**
  - user preferences relevant to housing
  - current housing status
  - stable operating constraints

- **RAG side**
  - ImmoScout workflow pack
  - message templates
  - past application examples
  - known captcha workflow
  - restore pack / troubleshooting notes

- **skill side**
  - login/check auth
  - search/filter
  - inspect listing
  - prepare message
  - handle captcha/escalation
  - send inquiry
  - record result

That seems much better than stuffing the whole thing into one giant memory artifact.

---

# I’d also add one important rule

## Skills should point to RAG, not duplicate it
A skill should say things like:
- “for message template, retrieve pack X”
- “for failure handling, retrieve pack Y”

not contain huge embedded copies of those materials.

That keeps skills:
- small
- stable
- reusable
- cache-friendly

---

# My preferred division of responsibility

### Modules
“What should I broadly know right now?”

### RAG
“What extra material is relevant to this task?”

### Skills
“How do I do this kind of task well?”

That separation feels very natural and should stay manageable as the system grows.

If you want, I can next sketch a concrete example with maybe:
- 6 default modules
- 4 pack types
- 3 example skills
- and one sample turn assembly flow.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 25.4s
Time: model 22.2s, overhead 1.9s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.3k o 815 t 31 cache 113.7k/97% est $0.0209; p≈2.7ktok/10.7kch
Ctx: direct ACP session 114.5k; active 114.5k; p≈3.1ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 22


I think the cleanest version is:

## 1. Modules = always-on structured context
These are the things loaded deliberately into the prompt.

Use modules for:
- identity
- operating rules
- user preferences
- durable memory
- active decisions
- current thread
- maybe a small operational state layer

Properties:
- compact
- curated
- high signal
- explicit load/unload
- predictable cache behavior

So modules are the **trusted working set**.

---

## 2. RAG = on-demand recall over broader artifacts
Use RAG for things that are useful, but not worth loading by default.

Good RAG targets:
- workflow packs
- topic packs
- architecture docs
- old compaction summaries
- knowledge-base entries
- journals/log-derived packs
- maybe older conversation tail once compacted

Properties:
- larger
- more numerous
- searchable
- source-aware
- not always loaded

So RAG is the **library**, not the desk.

---

## 3. Skills = reusable procedures
Skills should not mainly be memory containers.
They should be procedural scaffolds that know:

- when they apply
- prerequisites
- step order
- what to retrieve
- what to ask the user
- common failure modes
- what “done” looks like

A skill can then:
- request certain modules
- trigger RAG lookups for relevant packs
- run the workflow

So skills are the **method**, not the memory.

---

# How they fit together in a turn

I’d make the flow something like:

1. **Base modules load**
   - identity
   - rules
   - preferences
   - current thread
   - active decisions

2. **Intent detection**
   - what kind of task is this?
   - coding, architecture, housing, planning, debugging, etc.

3. **Skill selection if relevant**
   - if task matches a workflow, load/use the skill

4. **RAG retrieval**
   - fetch relevant packs/docs/history/knowledge
   - only what’s needed for this turn

5. **Assemble prompt**
   - stable modules first
   - retrieved context later
   - temporary task material last

6. **After the turn**
   - maybe update current-thread
   - maybe update actions/decisions
   - maybe create a compaction summary or pack
   - maybe refine skill metadata if workflow changed

---

# For ImmoScout specifically

I’d model it like:

- **module side**
  - user preferences relevant to housing
  - current housing status
  - stable operating constraints

- **RAG side**
  - ImmoScout workflow pack
  - message templates
  - past application examples
  - known captcha workflow
  - restore pack / troubleshooting notes

- **skill side**
  - login/check auth
  - search/filter
  - inspect listing
  - prepare message
  - handle captcha/escalation
  - send inquiry
  - record result

That seems much better than stuffing the whole thing into one giant memory artifact.

---

# I’d also add one important rule

## Skills should point to RAG, not duplicate it
A skill should say things like:
- “for message template, retrieve pack X”
- “for failure handling, retrieve pack Y”

not contain huge embedded copies of those materials.

That keeps skills:
- small
- stable
- reusable
- cache-friendly

---

# My preferred division of responsibility

### Modules
“What should I broadly know right now?”

### RAG
“What extra material is relevant to this task?”

### Skills
“How do I do this kind of task well?”

That separation feels very natural and should stay manageable as the system grows.

If you want, I can next sketch a concrete example with maybe:
- 6 default modules
- 4 pack types
- 3 example skills
- and one sample turn assembly flow.


* 🟢 Done
Stage: final reply sent
Model: openai/gpt-5.4
Elapsed: 26.8s
Time: model 22.2s, overhead 4.7s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.3k o 815 t 31 cache 113.7k/97% est $0.0209; p≈2.7ktok/10.7kch
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 10.6%; usable 10.8%; observed turns 20; actual turns 22


Thank you we put this into plans. Currently we are still bringing the smoke agent to acp parity in programming skills, we don't want you to lose any


⚪ Received
Stage: queued for agent
Model: openai/gpt-5.4
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 11.2%; usable 11.5%; observed turns 22; actual turns 22


* ⚪ Queued
Stage: waiting for conversation turn
Model: openai/gpt-5.4
Elapsed: 0.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 11.2%; usable 11.5%; observed turns 22; actual turns 22


* 🟡 Working
Stage: building prompt
Model: openai/gpt-5.4
Elapsed: 1.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 11.2%; usable 11.5%; observed turns 22; actual turns 22


* 🟡 Working
Stage: model running
Model: openai/gpt-5.4
Elapsed: 2.2s
Time: overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: p≈3.0ktok/11.8kch
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 11.2%; usable 11.5%; observed turns 22; actual turns 22


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


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

I think that’s the right sequencing:
- **first** get the smoke/native path to ACP parity on practical programming ability
- **then** do the nicer architecture cleanup without sacrificing capability

That tradeoff is important, because a cleaner system that quietly loses coding effectiveness would be a bad bargain.

Things I’d especially protect during parity work:
- strong repo/code navigation
- patching/edit reliability
- multi-step debugging flow
- test/run/inspect loops
- good tool-selection instincts
- workflow memory for recurring engineering tasks
- enough context assembly to preserve “taste” and continuity in coding sessions

So yes, I agree with being conservative there.
The native architecture is promising, but it should earn the transition by **matching or beating** ACP where it matters.

And honestly, this module/RAG/skill


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

I think that’s the right sequencing:
- **first** get the smoke/native path to ACP parity on practical programming ability
- **then** do the nicer architecture cleanup without sacrificing capability

That tradeoff is important, because a cleaner system that quietly loses coding effectiveness would be a bad bargain.

Things I’d especially protect during parity work:
- strong repo/code navigation
- patching/edit reliability
- multi-step debugging flow
- test/run/inspect loops
- good tool-selection instincts
- workflow memory for recurring engineering tasks
- enough context assembly to preserve “taste” and continuity in coding sessions

So yes, I agree with being conservative there.
The native architecture is promising, but it should earn the transition by **matching or beating** ACP where it matters.

And honestly, this module/RAG/skill framing should help with that rather than hurt it — as long as parity comes first and the cleanup doesn’t get ahead of the practical programming path.


* 🟡 Processing result
Stage: model returned
Model: openai/gpt-5.4
Elapsed: 11.5s
Time: model 8.1s, overhead 2.2s
Backend: ses_18a53d671ffer3rrL2MWHNxZaM; Label: persistent-chat-20260529T204325Z
Turn: i 3.7k o 205 cache 116.7k/97% est $0.0124; p≈3.0ktok/11.8kch
Ctx: direct ACP session 117.8k; active 4.1k; cache 113.7k/97%; p≈2.7ktok; ctx 11.2%; usable 11.5%; observed turns 22; actual turns 23