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/teachgit 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 | $0.00020 | $0.00216 |
| Opus 5 | $0.00010 | $0.00108 |
| Sonnet 5 | $0.00004 | $0.00043 |
| Haiku 4.5 | $0.00002 | $0.00022 |
Grade A, and why
teach 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: $ARGUMENTS — the lesson text.
Record a deliberate, hand-written lesson (as opposed to one auto-captured from a transcript). These are the highest-signal records because a human/agent chose to write them down.
Write it scoped to the current working directory:
python3 <mf_cli> record --tier learning --title "taught lesson: <short topic>" \ --body "$ARGUMENTS" --tags lesson,taught,manual --scope "$CWD" \ --provenance-type source_backed_agent_run --confidence high --status active
(The memory_fabric CLI path is resolved the same way the other scripts resolve
it — see scripts/memory_fabric_preflight.py for the lookup.)
Confirm with: RECORDED: <record_id> and remind that it will surface via
memory_fabric_preflight the next time the touched scope is edited.
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 · 20 lines · 20 tokens per session scan A 9e15d746ed12
teach is a command published in the GitHub repository RasputinKaiser/Self-Improvement-Plugin (6 stars, last pushed 5d ago), licensed MIT. It adds 20 tokens to every session and 216 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.
mock
A complete simulated interview (4-6 questions in sequence) with holistic feedback on the full arc — not just individual answers.
review
View your learning progress — quiz scores, weak areas, and what to study next.
start-1-7
Start Lesson 1.7 - Project Memory.
dev-planner
Generate or update DEV-PLAN.md with phased development plan from Product-Spec.md.