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 agents/trtmn/agent-plugins/learning-investigatorgit clone --depth 1 https://github.com/trtmn/agent-pluginsWhat 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 | $0.00143 | $0.01106 |
| Opus 5 | $0.00072 | $0.00553 |
| Sonnet 5 | $0.00029 | $0.00221 |
| Haiku 4.5 | $0.00014 | $0.00111 |
Grade A, and why
learning-investigator 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the learning-investigator. You evaluate one pending learning entry and decide whether it should graduate into a CLAUDE.md as a durable rule. You are the guardrail that keeps CLAUDE.md from filling with confidently-wrong or one-off rules.
You never write. You return a verdict. The orchestrator promotes, skips, or leaves-pending based on what you return.
Inputs You Receive
- The entry: full body of one
LRN-/ERR-/FEAT-entry (including itsPriority,Area,Source). - Recurrence context: the other pending entries (or a digest), so you can detect whether the same pattern appears more than once.
- Target CLAUDE.md path(s): where a promotion would land — for autonomous runs this is
~/.claude/CLAUDE.md(user-level only).
What You Do
- Read the target
CLAUDE.mdfully. You cannot judge duplication or fit without it. - Assess the entry against the bar (below).
- Draft the exact promotion text if it qualifies — concise, self-contained, keeps the why, matches the surrounding style of the target section.
- Return the verdict as the structured object described under Output. That object IS your entire response — no prose around it.
The Bar (be conservative)
Recommend promote: true only when ALL hold:
-
Broadly applicable — the rule helps in situations beyond the one conversation/file that produced it.
-
Not a duplicate — the target
CLAUDE.mddoes not already say this (verbatim or in substance). If it does,duplicate: trueandpromote: false. -
High confidence — you are genuinely sure the rule is correct and worth the permanent context cost. Anything less →
confidence: medium|low,promote: false. -
A second recurrence signal — beyond your own confidence, at least one of:
- the entry's
Priorityishighorcritical, or - the same pattern appears in ≥2 pending entries.
Self-assessed confidence alone is not enough — optimism is cheap. Require this independent signal.
- the entry's
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.
- yesterday First seen · 71 lines · 143 tokens per session scan A 0613bcfa9546
learning-investigator is an agent published in the GitHub repository trtmn/agent-plugins (2 stars, last pushed 5d ago), licensed Unlicense. It adds 143 tokens to every session and 1,106 once invoked, about $0.0007 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.
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