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 skills/morodomi/dev-crew/learnnpx skills add morodomi/dev-crew --skill learngit clone --depth 1 https://github.com/morodomi/dev-crewWrote 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/skills/morodomi/dev-crew/learn)<a href="https://agentmods.dev/skills/morodomi/dev-crew/learn"><img src="https://agentmods.dev/badge/skills/morodomi/dev-crew/learn.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.00867 |
| Opus 5 | $0.00019 | $0.00434 |
| Sonnet 5 | $0.00008 | $0.00173 |
| Haiku 4.5 | $0.00004 | $0.00087 |
Grade A, and why
learn 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.
What it actually says
Learn - セッションパターン抽出
セッションの作業パターンを検出し、instinct (構造化パターン) として蓄積する。
前提
- プロジェクトディレクトリ内で実行すること
- Git 管理下であること (git log / git diff を使用)
実行手順
Step 1: 初回チェック (Empty State)
蓄積先ディレクトリを確認。存在しない場合は自動作成しオンボーディングメッセージを表示:
dev-crew learn へようこそ。
learn はセッションの作業パターンを記録し、evolve でスキルに進化させます。
初回セットアップを実行しました。
mkdir -p ${CLAUDE_PLUGIN_DATA}/instincts/
Step 2: 入力ソース収集 + TF-IDF サマリ計算
以下の情報を収集する:
| ソース | コマンド |
|---|---|
| Cycle doc | ls -t docs/cycles/*.md 2>/dev/null | head -1 |
| 変更履歴 | git log --oneline -20 |
| 変更ファイル | git diff --name-only HEAD~5..HEAD |
| observations | cat ${CLAUDE_PLUGIN_DATA}/observations/log.jsonl 2>/dev/null |
TF-IDF サマリ計算
scripts/tfidf-summary.sh で observations (log.jsonl) からセッション横断の TF-IDF スコアを算出し、tfidf_summary として observer に渡す。
TFIDF_JSON="$(bash scripts/tfidf-summary.sh)"
空配列 [] が返った場合 (ブートストラップ期間 or データなし)、observer に tfidf_summary を渡さない。
詳細: reference.md
Step 3: ユーザー補足情報
AskUserQuestion で確認:
- 「このセッションで気づいたパターンはありますか?」
- 選択肢: エラー解決 / ワークフロー改善 / ツール選好 / 特になし
Step 4: observer エージェントに委譲
Task tool で observer を起動し、収集した入力ソースを渡す:
Task(subagent_type: "dev-crew:observer", prompt: "...")
Step 5: 品質フィルタ + 保存
observer の出力から instinct を選別:
| confidence | 判定 |
|---|---|
| >= 0.5 | 保存 (${CLAUDE_PLUGIN_DATA}/instincts/ に JSONL 追記) |
| < 0.5 | 破棄 (理由付きで報告) |
confidence >= 0.8 の instinct は MEMORY.md 昇格候補としてユーザーに提示する。棲み分けルール詳細: reference.md
Step 6: 結果報告
N 件のパターンを保存しました。
現在の蓄積: X 件。evolve 可能まであと Y 件。
保存成功後、タイムスタンプを更新:
date -u +"%Y-%m-%dT%H:%M:%SZ" > ${CLAUDE_PLUGIN_DATA}/observations/.last-learn-timestamp
Reference
詳細: reference.md
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 95 lines · 38 tokens per session scan A 21f1eb120a3f
learn is a skill published in the GitHub repository morodomi/dev-crew (1 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 867 once invoked, about $0.0002 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 2026-09-03.
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