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 commands/kanevry/session-orchestrator/memory-cleanupgit clone --depth 1 https://github.com/Kanevry/session-orchestratorWrote 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/commands/kanevry/session-orchestrator/memory-cleanup)<a href="https://agentmods.dev/commands/kanevry/session-orchestrator/memory-cleanup"><img src="https://agentmods.dev/badge/commands/kanevry/session-orchestrator/memory-cleanup.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 | $0.00017 | $0.00151 |
| Opus 5 | $0.00009 | $0.00076 |
| Sonnet 5 | $0.00003 | $0.00030 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
memory-cleanup 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 4d 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.
This is a copy
78% identical to close — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
/memory-cleanup
Use the Session Orchestrator command definition at commands/memory-cleanup.md.
Arguments: $ARGUMENTS
Read that command file and follow it exactly. When it references $ARGUMENTS, substitute the arguments above. Keep all Session Orchestrator platform fallbacks intact.
Cursor has no Skill tool. When the command says to invoke a skill, Read skills/<skill-name>/SKILL.md and follow it. Supporting files (soul.md, phase docs) live in that same skills/<skill-name>/ directory.
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.
- 4d ago First seen · 15 lines · 17 tokens per session scan A ece5ec6fdd07
memory-cleanup is a command published in the GitHub repository Kanevry/session-orchestrator (49 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 151 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to close, differing in 7 lines, and is treated as a copy.
Other commands, from other repositories
learn
Learn Claude Code best practices and capture lessons into persistent memory.
wiki
Build, query, and maintain long-lived knowledge bases. Each wiki = markdown folder + SQLite FTS5 shadow index. Survives sessions, indexes auto-load on SessionStart.
replay
Automatically find and surface relevant learnings from your pro-workflow database before you start working. Like muscle memory for your coding sessions.
context-optimizer
Diagnose and fix context window problems.
learn-rule
Capture a lesson from this session into permanent memory.
context-size
Snapshot the current session's context footprint — transcript size, CLAUDE.md size, MCP schemas, file reads still in context. Identifies what's eating tokens so you can trim.