How do I install the Os Memory Manager skill?

Run npx skillmds@latest add richfrem/os-memory-manager in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Os Memory Manager safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Os Memory Manager?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Os Memory Manager free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Os Memory Manager?

richfrem (@richfrem) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Os Memory Manager by richfrem — Agent Skill

Name: Os Memory Manager
Rating: 4.5 (4 reviews)
Author: richfrem

Os Memory Manager

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.

richfrem 7a6d0cf 15 files · 7.6 KB Updated Sep 10, 2026

File contents

Session Memory Manager

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

Prerequisites, dependencies, and trigger examples are in references/detailed-reference.md — this skill requires the Agentic OS to be initialized first (os-init).

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. Full detail (templates, dedup protocol, size-limit enforcement, survey questions) for each phase is in references/detailed-reference.md.

Phase 0: Intent Emission (Event Bus)

Before taking any actions, publish your intent: python context/kernel.py emit_event --agent os-memory-manager --type intent --action promote_memory

Phase 1: Acquire OS State and Lock

Update OS state: active_agent os-memory-manager, mode memory-gc, memory_gc_due false.
Acquire the lock: python context/kernel.py acquire_lock memory. If it fails, abort.
Ask the user to confirm session scope: main task/goal, architectural decisions, tricky bugs solved, skills updated, open next steps.

Phase 2: Write the Dated Session Log

Write to context/memory/YYYY-MM-DD.md using today's date, per the template in references/detailed-reference.md.

Phase 3: Preserve Test Registry Artifacts

Never archive/skip context/memory/tests/registry.md; preserve closed scenario files for 90 days before archiving; promote confirmed test findings not already in context/memory.md; add "DO NOT RE-TEST" entries for falsified hypotheses. Full protocol in references/detailed-reference.md.

Phase 4: Promote to Long-Term Memory

Apply the promote/skip decision (ephemeral state and open tasks → skip; system facts, commands, style rules, architectural decisions → promote). Before promoting, read context/memory.md and the last 10 MEMORY.md entries, run dedup/conflict detection, and follow the Safe Write Protocol (git stash + diff preview + post-write verification). Full dedup/conflict/ID protocol and both fact-format options are in references/detailed-reference.md.

Phase 4b: Enforce Memory.md Size Limits

Check wc -c context/memory.md; if over 50000 bytes, merge/prune redundant facts or archive the oldest ~200 lines to context/memory/archive/YYYY-MM.md. Full steps in references/detailed-reference.md.

Phase 5: Self-Assessment Survey (MANDATORY)

Before releasing the lock, complete the Post-Run Self-Assessment Survey (references/memory/post_run_survey.md) and save to context/memory/retrospectives/survey_[YYYYMMDD]_[HHMM]_os-memory-manager.md. Emit --type learning --action survey_completed on completion. Full survey questions in references/detailed-reference.md.

Phase 6: Confirm with User and Release Lock

Show a completion summary, emit the success result event, then run python context/kernel.py release_lock memory. Full format in references/detailed-reference.md.

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

---name: os-memory-managerdescription: 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.---# Session Memory ManagerManages the three tiers of agent memory in an Agentic OS environment.Prerequisites, dependencies, and trigger examples are in `references/detailed-reference.md` —this skill requires the Agentic OS to be initialized first (`os-init`).## 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 FlowExecute these phases in order. Do not skip phases. Full detail (templates, dedup protocol,size-limit enforcement, survey questions) for each phase is in `references/detailed-reference.md`.### Phase 0: Intent Emission (Event Bus)Before taking any actions, publish your intent:`python context/kernel.py emit_event --agent os-memory-manager --type intent --action promote_memory`### Phase 1: Acquire OS State and Lock1. Update OS state: `active_agent os-memory-manager`, `mode memory-gc`, `memory_gc_due false`.2. Acquire the lock: `python context/kernel.py acquire_lock memory`. If it fails, abort.3. Ask the user to confirm session scope: main task/goal, architectural decisions, tricky bugs solved, skills updated, open next steps.### Phase 2: Write the Dated Session LogWrite to `context/memory/YYYY-MM-DD.md` using today's date, per the template in`references/detailed-reference.md`.### Phase 3: Preserve Test Registry ArtifactsNever archive/skip `context/memory/tests/registry.md`; preserve closed scenario files for 90days before archiving; promote confirmed test findings not already in `context/memory.md`; add"DO NOT RE-TEST" entries for falsified hypotheses. Full protocol in `references/detailed-reference.md`.### Phase 4: Promote to Long-Term MemoryApply the promote/skip decision (ephemeral state and open tasks → skip; system facts, commands,style rules, architectural decisions → promote). Before promoting, read `context/memory.md` andthe last 10 `MEMORY.md` entries, run dedup/conflict detection, and follow the Safe Write Protocol(git stash + diff preview + post-write verification). Full dedup/conflict/ID protocol and bothfact-format options are in `references/detailed-reference.md`.### Phase 4b: Enforce Memory.md Size LimitsCheck `wc -c context/memory.md`; if over 50000 bytes, merge/prune redundant facts or archive theoldest ~200 lines to `context/memory/archive/YYYY-MM.md`. Full steps in `references/detailed-reference.md`.### Phase 5: Self-Assessment Survey (MANDATORY)Before releasing the lock, complete the Post-Run Self-Assessment Survey(`references/memory/post_run_survey.md`) and save to`context/memory/retrospectives/survey_[YYYYMMDD]_[HHMM]_os-memory-manager.md`. Emit`--type learning --action survey_completed` on completion. Full survey questions in`references/detailed-reference.md`.### Phase 6: Confirm with User and Release LockShow a completion summary, emit the success result event, then run`python context/kernel.py release_lock memory`. Full format in `references/detailed-reference.md`.## 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`

Frequently asked questions