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 skills add miyatti777/ai-plc --skill 01-collection-jevgit clone --depth 1 https://github.com/miyatti777/ai-plcWrote 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/miyatti777/ai-plc/01-collection-jev)<a href="https://agentmods.dev/skills/miyatti777/ai-plc/01-collection-jev"><img src="https://agentmods.dev/badge/skills/miyatti777/ai-plc/01-collection-jev/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/skills/miyatti777/ai-plc/01-collection-jev"><img src="https://agentmods.dev/badge/skills/miyatti777/ai-plc/01-collection-jev.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 | $0.00057 | $0.03808 |
| Opus 5.5 | $0.00023 | $0.01523 |
| Sonnet 5.5 | $0.00011 | $0.00762 |
| Haiku 4.5 | $0.00006 | $0.00381 |
Grade A, and why
01-collection-jev 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 16d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-PLC Stage 1: Collection（Jev実験版）
🧪 実験版（ai-plc-jev 1.12.0-exp.2）: 公開 core の
.claude/skills/ai-plc/01-collection/SKILL.md（v2.2）の代わりに/01-collection-jevで使う試行用のスキル。本体の SKILL・rules は書き換えない。 公開 core 2.2 との違いは Jev 監視（opt-in）に関わる箇所だけ（intent.yaml のjev_monitor欄のコメント、Phase 6.5 の opt-in 判定〔自動完走中も自動で承認しない〕、Phase 7 の Next Action と 4. の /goal 1行を-jevに）。詳細は.claude/skills/ai-plc-jev/README.md。
パイプライン（Collection → Inception → Construction → Operation）の初期化ステージ。Goal と Mode を受け取り、Execution Context（Scope）を確立し、Context を収集・構造化する。
共通規約: 命名は RUL_plc_system §6 / 完了報告は RUL_plc_session §7（4パート）/ Phase遷移通知は §8 / Mob CP出力は §9 に従う。
自動完走モード: セッション中に RUL_plc_session §10 を指す /goal があるときは §10 に従い、Phase 7 も待たずに進める（無ければ本 SKILL のとおり止まる）。Phase 1 で RUL_plc_adaptive §0 の①に当たった場合と、complex・platform_builder と判定した場合は停止する（§10 の停止）。
入力
| 入力 | 必須 | 説明 |
|---|---|---|
| goal | ✅ | 達成すべき目標の自然言語記述 |
| mode | ⭕ | direct / platform_builder（デフォルト: direct） |
| owner / deadline | ⭕ | デフォルト: 現在のユーザー / +30d |
| parent_scope | ⭕ | 親ScopeのURL/パス。Sub-Agent Scope作成時に指定 |
| scope_name | ⭕ | デフォルト: Goalから自動生成 |
実行フロー
Phase 0: Scope判定
- parent_scope なし & 既存Scope指定なし →
pipeline_init（新規パイプライン） - parent_scope あり →
sub_agent_scope（親配下のsublayers/に作成、親Context継承） - 既存Scope指定あり →
scope_reinit（構造・成果物を保持してIntent/Manifestを更新）
Phase 1: Adaptive Workflow深度判定
RUL_plc_adaptive §1 に従い simple / standard / complex を判定し（§0 の表の深度の目安と食い違えば、その理由も workflow_depth_reason の同じ行に書く）、結果と根拠をユーザーに報告する（変更指示があれば従う。停止はしない。§0 の①〔Layer を作らない〕に当たれば、その旨を報告し、続けるかをユーザーに聞く）。深度と、その理由を intent.yaml に記録する: workflow_depth_reason（1行）と、参考として depth_axes（判定の条件にはまだ使わない。各軸を S〔軽い〕か H〔重い＝問いに「はい」。V だけは「いいえ」が H〕で書く）— R 受け手「成果物を自分以外が読んで何かを決めるか」／U やり直し「AI 自身が行う操作で、間違えたとき元に戻すのに他人や外部が絡むか（公開・送信・本体のルールやスキル・外部 DB・削除・お金）」／V 確かめ方「完了の条件を人の判断なしに ○/× できるか（できなければ H）」／C 固有の文脈「このリポジトリや組織の資料を読まないと書けないか」。
Phase 2: ディレクトリ構造生成
Flow/[YYYYMM]/[YYYY-MM-DD]/[Scope名]/ に作成（既存Flowを使用。日付フォルダは当日分を使用、なければ作成）:
[Scope名]/
├── intent.yaml / context.yaml / backlog.yaml（空で初期生成）
├── Context/ （Context Store）
├── Agents/ （Stage 3 を通したときだけ生成）
├── sublayers/ （Stage 2で生成）
└── Documents/ （Stage 4で生成）
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.
- 16d ago Changed · +5 lines ed4a26717652
- 18d ago First seen · 139 lines · 57 tokens per session scan A 8f7c8c032f84
01-collection-jev is a skill published in the GitHub repository miyatti777/ai-plc (49 stars, last pushed 17d ago), licensed MIT. It adds 57 tokens to every session and 3,808 once invoked, about $0.0002 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 2026-09-30.
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