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/escalategit 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 | $0.00018 | $0.00231 |
| Opus 5 | $0.00009 | $0.00115 |
| Sonnet 5 | $0.00004 | $0.00046 |
| Haiku 4.5 | $0.00002 | $0.00023 |
Grade A, and why
escalate 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: $ARGUMENTS — the bounded subtask to escalate.
Dispatch the escalate agent with this single, well-scoped task:
"$ARGUMENTS"
Before dispatching, confirm the task is genuinely bounded (one decision or one localized fix). If it is broad, decompose it first and escalate only the slice the main session is stuck on — escalation is a scalpel, not a session swap.
When the agent returns its DIFF: and LESSON: blocks:
- Apply the diff (the autonomy gate will snapshot first if it touches SIPS plugin source).
- Record the LESSON to Memory Fabric scoped to the touched file:
python3 <mf_cli> record --tier learning --title "escalation lesson: <topic>" \ --body "$LESSON" --tags lesson,escalation,frontier --scope <touched_file>so the workhorse recalls it next time and solves this class itself. - Run
/verify.
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 · 21 lines · 18 tokens per session scan A 553dbce133e3
escalate is a command published in the GitHub repository RasputinKaiser/Self-Improvement-Plugin (6 stars, last pushed 5d ago), licensed MIT. It adds 18 tokens to every session and 231 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
quiz
Generate and play an interactive study quiz from a KMS knowledge base, URL, file, or topic — and record the score back into the KMS.
quiz-result
Analyze quiz progress over time and the weak areas to study next, from a KMS scores log.
retro
Write this session's lessons into the project's lessons/ directory using the memory-discipline format.
cast
Choose, build, or synthesize the right harness for the current task.
research
Research a technical or product question.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.