os-memory-manager

name	os-memory-manager
plugin	agent-agentic-os
description	Trigger with "remember this", "update memory", "what should we record from this session", "capture learnings", "write a session log", or when closing a session. Guides agents on managing memory hygiene across sessions, deciding what to write to dated memory logs, what to promote to long-term memory.md, and when to archive. <example> User: I'm done for the day, can you write up a session log? Agent: <Bash> python context/kernel.py emit_event --agent os-memory-manager --type intent --action promote_memory python context/kernel.py state_update active_agent os-memory-manager </Bash> </example> <example> User: That's all, logging off now. Agent: <Bash> python context/kernel.py acquire_lock memory </Bash> </example> <example> User: How does the memory system work? Agent: <Read> ./references/architecture/context-folder-patterns.md </Read> </example>
allowed-tools	Bash, Read, Write

Prerequisites

This skill requires the Agentic OS to be initialized first. It calls context/kernel.py, context/memory.md, and context/.locks/ — files that only exist after running the os-init skill in your project.

If you have not yet initialized the OS, run:

os-init

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Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

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Session Memory Manager

Manages the three tiers of agent memory in an Agentic OS environment.

Memory Tiers

Tier	File	Written By	When Loaded
Auto-memory	MEMORY.md	Claude automatically	Every session (Anthropic native)
Long-term facts	context/memory.md	You (curated)	@imported in CLAUDE.md
Session logs	context/memory/YYYY-MM-DD.md	Agent at session close	On demand

Execution Flow

Execute these phases in order. Do not skip phases.

Phase 0: Intent Emission (Event Bus)

Before taking any actions, you MUST publish your intent to the Event Bus. Use the Bash tool to run: python context/kernel.py emit_event --agent os-memory-manager --type intent --action promote_memory

Phase 1: Acquire OS State and Lock

1. Update OS State: Run python context/kernel.py state_update active_agent os-memory-manager, python context/kernel.py state_update mode memory-gc, and python context/kernel.py state_update memory_gc_due false.
2. Strict Lock Protocol: Run python context/kernel.py acquire_lock memory using the Bash tool to acquire the lock. If it fails, abort. The kernel handles stale lock timeouts automatically.
3. Capture What Happened: Before writing memory files, ask the user to confirm the session scope:

• What was the main task or goal this session?
• Were any architectural decisions made? (if yes -> promote to context/memory.md)
• Were any bugs solved that were tricky? (if yes -> promote to context/memory.md)
• Were any skills updated or created? (if yes -> record in session log)
• Are there open items / next steps?

Phase 2: Write the Dated Session Log

Write to context/memory/YYYY-MM-DD.md. Use today's date.

Use this template:

# Session Log: YYYY-MM-DD

## Summary
[1-2 sentence summary of what was accomplished]

## Key Decisions
- [Decision 1 and its rationale]
- [Decision 2 and its rationale]

## Lessons Learned
- [Lesson or edge case discovered]

## Skills Updated
- [Skill name]: [what changed]

## Open Items
- [ ] [Next steps or follow-up tasks]

Phase 3: Preserve Test Registry Artifacts

Before general promotion, handle test registry files specially:

1. context/memory/tests/registry.md — never archive, never skip. This is always L3. Verify it exists and the latest cycle row is CLOSED before proceeding.
2. Closed scenario files (context/memory/tests/[CYCLE_ID]_*.md) — preserve in place for 90 days, then move to context/memory/tests/archive/. Never delete.
3. Confirmed findings from test scenarios — if the scenario file has a "Confirmed" hypothesis, check whether the finding is already in context/memory.md. If not, promote it as a fact with the cycle ID as evidence source.
4. Falsified hypotheses — check context/memory.md for a "DO NOT RE-TEST" entry. If missing, add it now to prevent future wasted cycles.

Phase 4: Promote to Long-Term Memory

For each item in the session log, apply the promote/skip decision:

• Ephemeral state (e.g., "Tried running tests, they failed") -> SKIP (Archive with log)
• Open tasks (e.g., "Need to fix the auth module") -> SKIP (Leave in session log for tomorrow)
• System facts, new commands, style rules, or architectural decisions -> PROMOTE

If a fact is selected for promotion, you MUST first read context/memory.md AND the last 10 entries of MEMORY.md:

1. Search for the topic or conceptually similar topics.
2. THE DEMENTIA DEFENSE: If the topic, or any overlapping topic exists, you must flag it as a potential Conflict even if the wording differs. LLMs are bad at detecting semantic equivalence. Err toward false positives.
3. Deduplication IDs: Assign a sequential unique ID based on the current highest ID in context/memory.md (e.g., if the highest is [#042], assign [#043]) to prevent syntactic duplication. Ensure this ID prefix [#ID] is stored with the fact. Let the user know the generated ID. Use grep -c "^\[#" via Bash if you need help finding the count.
4. Cross-Skill Conflict Detection: Run grep -ri "[Fact Keywords]" ./skills/ using the Bash tool to ensure promoted memory doesn't break or contradict existing procedural skills.
5. Semantic Deduplication: If ANY semantic overlap exists, explicitly output <CONFLICT> before any Write. Ask the user if the new proposed fact supersedes the existing fact (to replace the old hash) or if it's a conflict to resolve. If superseding, you MUST output a <SUPERSEDE old_id=NNN> marker (e.g., <SUPERSEDE old_id=042>) so the next learning loop can locate and prune the old fact. Never silently overwrite — the marker is required for audit trail continuity.
6. Safe Write Protocol: Wrap every Write in a git stash + diff preview (use Bash tool). If the user rejects the preview, run git stash pop to rollback.
7. If there is absolutely no conflict (or the user resolves it), append the numbered/hashed fact cleanly.
8. Post-Write Verification: After writing, use the Read tool on the exact file. If the expected diff is not present, output <WRITE_FAILED> and run git stash pop.

Format facts in context/memory.md like this (choose one format and stick to it):

Option A — Markdown (default, human-readable):

## [YYYY-MM-DD] [Topic]
[The fact, decision, or convention in 1-3 sentences]

Option B — Structured JSON entries (recommended for projects with high fact volume or automated tooling):

{"id": "build_cmd_001", "type": "command", "topic": "build", "value": "npm run build", "source": "session-2026-03-19", "supersedes": null}

Append one JSON object per line to a context/memory.jsonl file. Reference it from context/memory.md with a note: <!-- structured facts in context/memory.jsonl -->. Benefits: unambiguous deduplication by id, machine-queryable, easier <SUPERSEDE> enforcement.

Phase 4: Enforce Memory.md Size Limits

You MUST verify the size of the curated memory file to prevent context degradation.

1. Run wc -c context/memory.md using the Bash tool to check the exact byte size. (Line count is unreliable on Windows due to CRLF line endings; byte size is platform-neutral.)
2. If the byte size is strictly greater than 50000 bytes, you must condense it:
   • Merge & Prune: Look for redundant, outdated, or supersedable facts and merge them.
   • Archive: If still too large, create the archive directory: mkdir -p context/memory/archive/
   • Move the oldest (top-most) ~200 lines to context/memory/archive/YYYY-MM.md using Write.
   • Remove these archived lines from context/memory.md using Write.
   • Ensure an archive reference log (e.g., <!-- Archived data -> context/memory/archive/ -->) exists at the top of context/memory.md.

Phase 5: Self-Assessment Survey (MANDATORY)

Before releasing the lock, complete the Post-Run Self-Assessment Survey (references/memory/post_run_survey.md). The memory manager must reflect on its own runs to improve the quality of what gets promoted and what gets skipped.

Count-Based Signals: How many times were you uncertain whether to promote or skip? How many potential conflicts did you flag? How many dedup IDs did you assign?

Qualitative Friction:

1. Which promotion decision felt most uncertain?
2. Was any fact promoted that probably should have been skipped (or vice versa)?
3. Which part of the deduplication protocol felt most ambiguous?
4. What one change to the session log template would improve the next run?

Improvement Recommendation: What one change to this skill should be tested next run?

Save to: ${CLAUDE_PROJECT_DIR}/context/memory/retrospectives/survey_[YYYYMMDD]_[HHMM]_os-memory-manager.md

Emit survey completion:

python context/kernel.py emit_event --agent os-memory-manager \
  --type learning --action survey_completed \
  --summary "retrospectives/survey_[DATE]_[TIME]_os-memory-manager.md"

Phase 6: Confirm with User and Release Lock

After writing and survey saved, show a summary:

[x] Session log written: context/memory/YYYY-MM-DD.md
[x] Promoted N facts to context/memory.md
[x] Survey saved: retrospectives/survey_[DATE]_[TIME]_os-memory-manager.md
[ ] No archive needed (current count: N facts)

Event Bus Publish: Use Bash to emit your success result: python context/kernel.py emit_event --agent os-memory-manager --type result --action promote_memory --status success

Finally, Lock Release Protocol: Execute python context/kernel.py release_lock memory to release the acquired loop lock.

Next Actions

• To understand the full memory layer architecture -> read os-guide skill
• To set up CLAUDE.md @imports for memory -> read references/architecture/claude-md-hierarchy.md in os-guide