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/rasputinkaiser/self-improvement-plugin/recallgit clone --depth 1 https://github.com/RasputinKaiser/Self-Improvement-PluginWhat 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 | $0.00015 | $0.00189 |
| Opus 5 | $0.00008 | $0.00095 |
| Sonnet 5 | $0.00003 | $0.00038 |
| Haiku 4.5 | $0.00002 | $0.00019 |
Grade A, and why
recall 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 2d 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.
What it actually says
Argument: $ARGUMENTS (optional query; defaults to the current scope/recent objective).
Run the tier-aware recall:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/recall_ranker.py --query "$ARGUMENTS" --json
Read the result and present it as a ranked list:
- rank, tier, confidence, title, one-line body excerpt.
- If any record is tagged
failure, surface it FIRST with a⚠ prior failuremarker and the matching last-successful approach beneath it.
Do not act on the recalled lessons automatically — they are advisory. Confirm
relevance to the current task before relying on them. If the active tier is
workhorse, you may also spawn a repo-scout agent to map where the recalled
file/topic sits in the current repo.
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.
- 2d ago First seen · 18 lines · 15 tokens per session scan A 25850d75409a
recall is a command published in the GitHub repository RasputinKaiser/Self-Improvement-Plugin (6 stars, last pushed 5d ago), licensed MIT. It adds 15 tokens to every session and 189 once invoked, about $0.0001 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.
Other commands, from other repositories
feature
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spec
Turn an objective into a spec and implementable tasks.
research
Research a technical or product question.
fic-create-plan
Act as a Senior XP Developer creating detailed implementation plans through interactive, iterative collaboration.
reqforge-greenkeeper
Diagnose and fix ReqForge repository release gate failures.
evolution-engine
Scan feedback and generate evolution proposals for rule/skill upgrades.