Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/richfrem/agent-plugins-skills/os-memory-managernpx skills add richfrem/agent-plugins-skills --skill os-memory-managergit clone --depth 1 https://github.com/richfrem/agent-plugins-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/richfrem/agent-plugins-skills/os-memory-manager)<a href="https://agentmods.dev/skills/richfrem/agent-plugins-skills/os-memory-manager"><img src="https://agentmods.dev/badge/skills/richfrem/agent-plugins-skills/os-memory-manager.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00199 | $0.02564 |
| Opus 5 | $0.00100 | $0.01282 |
| Sonnet 5 | $0.00040 | $0.00513 |
| Haiku 4.5 | $0.00020 | $0.00256 |
Grade A, and why
os-memory-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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).
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
- Update OS State: Run
python context/kernel.py state_update active_agent os-memory-manager,python context/kernel.py state_update mode memory-gc, andpython context/kernel.py state_update memory_gc_due false. - Strict Lock Protocol: Run
python context/kernel.py acquire_lock memoryusing theBashtool to acquire the lock. If it fails, abort. The kernel handles stale lock timeouts automatically. - 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?
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- acceptance-criteria.md 1.6 KB
- evals/evals.json 1.7 KB
- evals/results.tsv 60 B
- kernel.py 28 B runs code
- references/acceptance-criteria.md 42 B
- references/architecture.md 35 B
- references/architecture/claude-md-hierarchy.md 58 B
- references/architecture/context-folder-patterns.md 62 B
- references/memory-promotion-guide.md 45 B
- references/memory/post_run_survey.md 48 B
- requirements.txt 22 B
- scripts/kernel.py 26 B runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 226 lines · 199 tokens per session scan A 9ce39f469830
os-memory-manager is a skill published in the GitHub repository richfrem/agent-plugins-skills (6 stars, last pushed yesterday), licensed MIT. It adds 199 tokens to every session and 2,564 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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