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/muji-j/jp-power-toolsWrote 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/muji-j/jp-power-tools/power-term)<a href="https://agentmods.dev/commands/muji-j/jp-power-tools/power-term"><img src="https://agentmods.dev/badge/commands/muji-j/jp-power-tools/power-term/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/muji-j/jp-power-tools/power-term"><img src="https://agentmods.dev/badge/commands/muji-j/jp-power-tools/power-term.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 | $0.00032 | $0.00432 |
| Opus 5 | $0.00016 | $0.00216 |
| Sonnet 5 | $0.00006 | $0.00086 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
power-term 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 8d 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
指定された用語を core スキルの用語集 skills/core/references/terms.md から調べ、簡潔に説明する。用語のオンボーディング・確認用。
手順
- 引数の用語（無ければ何を調べたいか尋ねる）を
terms.mdから検索する。表記ゆれ・英略称・関連語も考慮（例: 「デルタkW」→「ΔkW」、「FCR」→「一次調整力」）。 - 見つかれば: 正式な日本語表記とその意味を1〜数行で説明し、関連語や関連参照（市場ルールは
skills/markets/references/markets.md、計算はskills/ops/references/calc.md等）があれば案内する。 - 複数該当・部分一致なら候補を列挙。
terms.mdに無ければ、最も近い用語を提示し、必要に応じて markets.md / calc.md / sites.md も確認する。 - 制度・数値は改定されうる旨を添え、確定情報は公式一次ソース（必要なら
/power-verify）で確認するよう促す。
原則
- 公開・汎用の用語知識のみ。会社固有の用法・社内略語は扱わない。
- 公式用語の日本語表記を崩さない（社外資料・報告書でそのまま使えるように）。
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.
- 8d ago First seen · 17 lines · 32 tokens per session scan A 1b28ab574317
power-term is a command published in the GitHub repository muji-j/jp-power-tools (1 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 432 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 2026-08-31.
Other commands, from other repositories
design-tutorial
Interactive guided tour of Naksha — learn commands through real exercises, discover workflows, and get oriented in under 10 minutes.
alt
Import an Exam Radar (OPTIMETA Alt plugin) export and fold its lecture-emphasis exam signal into the course index — radar.md, a lecture-emphasis column on coverage.md, and a gold-zone weakmap.
pattern
Show solution pattern cards from course-index/patterns.md, filtered by topic or keyword.
ccc-compound
Post-task learning capture — extract patterns, corrections, and decisions to compound productivity.
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
paper-trail-resolve-textbooks
Resolve incomplete textbook references in the registry. Reads candidates via pipeline resolve-textbooks --list, invokes the textbook-resolver sub-agent to produce a decisions JSON (mergeinto / complete / blocked), and applies via pipeline resolve-textbooks --apply-from. Fully automated cleanup pass after INGEST.