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.
git clone --depth 1 https://github.com/RasputinKaiser/Self-Improvement-PluginWrote 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/rasputinkaiser/self-improvement-plugin/teach)<a href="https://agentmods.dev/commands/rasputinkaiser/self-improvement-plugin/teach"><img src="https://agentmods.dev/badge/commands/rasputinkaiser/self-improvement-plugin/teach/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/rasputinkaiser/self-improvement-plugin/teach"><img src="https://agentmods.dev/badge/commands/rasputinkaiser/self-improvement-plugin/teach.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00013 | $0.00206 |
| Opus 5.5 | $0.00005 | $0.00082 |
| Sonnet 5.5 | $0.00003 | $0.00041 |
| Haiku 4.5 | $0.00001 | $0.00021 |
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 28d 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
Follow references/command-protocol.md.
Classify $ARGUMENTS as a user instruction, observation, hypothesis, or verified procedure. Manual authorship alone does not imply high confidence. Search for an existing scoped lesson before adding a duplicate. Retain contradictory evidence.
Use homebase_record or scripts/memory_fabric.py record. Include a concise
symptom, intervention, outcome, assumptions, invalidation conditions, and exact
evidence reference. Use provenance user_instruction for an actual user decision,
verified_command/source_document only for inspected supporting evidence, and
user_or_agent_observation otherwise. Hypotheses use --confidence unknown,
--status candidate, and --verify-before-use. Record missing evidence explicitly.
A reusable procedure goes through the adaptation artifact workflow with pinned source episodes and consumer/transfer evaluation. Report the record ID and status; do not promise that a lesson is effective or will always be retrieved.
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.
- 28d ago Changed · -7 tokens per session 863d7036236e
- 1mo 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 (8 stars, last pushed 13d ago), licensed MIT. It adds 13 tokens to every session and 206 once invoked, about $0.0001 per session on Opus 5.5. 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.
train-executor
Generate an executor onboarding playbook from a person's Freedom Path DELEGATE bucket. Produces SOPs in the person's voice, a training curriculum, and outdated-material flags. Non-technical-friendly — designed for Claude Desktop + Cowork usage.
sessions
Manage OpenAI Codex session history, aliases, and session metadata.
training-coach-trainer
Train-the-trainer (TtT) protocol for internal subject-matter-experts who will deliver a training program. Condensed adult-learning principles, facilitation skills, common SME-as-trainer failure modes, cohort facilitation practice schedule, and a TtT-readiness rubric. Refuses SME deployment without TtT.
training-measure-transfer
Kirkpatrick L3 (behavior) + L4 (results) measurement plan for a training program. Behavior observation protocol with sampling cadence, business-metric tracking, post-program reinforcement plan, redesign feedback loop. Refuses L1-only smile-sheet evaluation; refuses programs without baseline.