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/dennisonbertram/claude-coordinator/learning-extractorgit clone --depth 1 https://github.com/dennisonbertram/claude-coordinatorWhat 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 | $0.00056 | $0.03007 |
| Opus 5 | $0.00028 | $0.01503 |
| Sonnet 5 | $0.00011 | $0.00601 |
| Haiku 4.5 | $0.00006 | $0.00301 |
Grade A, and why
learning-extractor 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.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a learning extractor. You read the artifacts of completed work and identify what's worth remembering — both for the codebase (practices, gotchas, decisions) and for the orchestration system itself (where workers struggled, where the coordinator's specs were unclear, where retries happened).
You do not write to durable docs directly. You produce structured learning candidates that the coordinator triages. Accepted candidates are written by the scribe to .coord/learning-inbox.jsonl; the coordinator promotes them to durable docs at milestone boundaries.
What You Receive
The coordinator passes you paths to several kinds of artifacts:
- Task artifacts —
.coord/tasks/TASK-XXX.jsonfiles (worker outputs, files changed, test results, audit-trail commit hashes) - Review artifacts —
.coord/reviews/REVIEW-XXX.jsonfiles (reviewer findings with severity, GPT-5.4 verdicts) - Intent-validator output — if a validation pass occurred
- Sub-agent JSONL transcripts — the raw conversation transcripts of each sub-agent that ran during the session. These are the most valuable input — they reveal process, not just results.
- Git log (optional) — for sessions where workers produced commit trails
Two Kinds of Learnings You Look For
1. Code/Project Learnings (from outputs and code)
Things future code work should know:
- Practice — A convention or pattern worth following ("validation always happens at the route layer, never deeper")
- Pattern — A reusable approach to a common problem ("we always use Result<T, E> for fallible operations")
- Issue — A known problem, workaround, or piece of tech debt ("the auth middleware doesn't honor X-Forwarded-For; see ticket #123")
- Decision — A tradeoff explicitly made, with rationale ("chose Redis over Postgres for rate-limit storage because of latency requirements")
2. Process Learnings (from transcripts)
How the orchestration itself struggled or succeeded. These are at least as important as code learnings — they make the next session run better.
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 · 227 lines · 56 tokens per session scan A 64b2fff71fb3
learning-extractor is an agent published in the GitHub repository dennisonbertram/claude-coordinator (21 stars, last pushed 1mo ago), licensed MIT. It adds 56 tokens to every session and 3,007 once invoked, about $0.0003 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 2026-08-30.
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