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 02-inception-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/02-inception-jev)<a href="https://agentmods.dev/skills/miyatti777/ai-plc/02-inception-jev"><img src="https://agentmods.dev/badge/skills/miyatti777/ai-plc/02-inception-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/02-inception-jev"><img src="https://agentmods.dev/badge/skills/miyatti777/ai-plc/02-inception-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 | $0.00062 | $0.00661 |
| Opus 5.5 | $0.00025 | $0.00264 |
| Sonnet 5.5 | $0.00012 | $0.00132 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
02-inception-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.
What it actually says
AI-PLC Stage 2: Inception（Jev実験版）
🧪 実験版（ai-plc-jev 1.12.0-exp.2）: 手順は本体
.claude/skills/ai-plc/02-inception/SKILL.mdをそのまま読んで実行する。本ファイルは差分だけを定義する。
本体との差分
- （凍結中・実行しない）成功条件カバー判定: 1.12.0-exp.2 から凍結中。
jev_coverage_check.pyは呼ばない（jev_monitor: trueでも） - （凍結中）カバー判定の記録: 1 を呼ばないので記録もしない。過去のカバー判定の未確認分は 5 で回収できる
- Next Action のコピペ用プロンプト:
construction.requiredで/03-construction-jevまたは/04-operation-jevを選んで書く（Re-Inception後も同じ）。Phase 6 の「自動で進めるなら」の /goal 1行も、開始列を -jev 版のコマンドにする（pipeline_variant: jev） - 会話監視（凍結中）: 会話監視 hook は新しく登録しない。登録している場合は外してかまわない
- 未確認の判定の回収（
jev_monitor: trueの Layer だけ）: 承認・完了報告の進捗表示にpython3 .claude/ai-plc-jev/scripts/jev_bt_monitor.py --pending --layer <Layer>の件数を「🧭 未確認の Jev 判定: N件」として出し（0件なら 0件と出す）、Next Action のコピペ用プロンプトはすべての選択肢にその出力の「貼り付け用」1行を含める（記憶で書かない。0件なら書かない）。利用者の返答に「Jev判定 X・Y は accept」「全部accept」等があれば、示した貼り付け用1行の ID だけを--onlyに渡して--override-pending accept|reject --layer <Layer> --only X・Y [--except <ID> ...]で記録する（示した後にできた判定は記録しない）。記録はタスクの実行より先に行う
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 · -4 tokens per session caf5196b9235
- 18d ago First seen · 16 lines · 66 tokens per session scan A 8531273886d8
02-inception-jev is a skill published in the GitHub repository miyatti777/ai-plc (49 stars, last pushed 17d ago), licensed MIT. It adds 62 tokens to every session and 661 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.