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 careerchain-ys/stdd --skill reverse-engineering-common-specgit clone --depth 1 https://github.com/careerchain-ys/stddWrote 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/careerchain-ys/stdd/reverse-engineering-common-spec)<a href="https://agentmods.dev/skills/careerchain-ys/stdd/reverse-engineering-common-spec"><img src="https://agentmods.dev/badge/skills/careerchain-ys/stdd/reverse-engineering-common-spec/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/careerchain-ys/stdd/reverse-engineering-common-spec"><img src="https://agentmods.dev/badge/skills/careerchain-ys/stdd/reverse-engineering-common-spec.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 | $0.00167 | $0.02608 |
| Opus 5.5 | $0.00067 | $0.01043 |
| Sonnet 5.5 | $0.00033 | $0.00522 |
| Haiku 4.5 | $0.00017 | $0.00261 |
Grade A, and why
reverse-engineering-common-spec 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 2mo 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
common spec のリバースエンジニアリング
既存（稼働中）プロジェクトに STDD を導入する際、コードベース全体を input にして common 階層の Spec を作成する。
| 出力 | 内容 |
|---|---|
docs/common/REQUIREMENTS.md |
サービス概要・登場アクター・アプリ構成（プロジェクト全体のビジネス要件） |
docs/common/ARCHITECTURE.md |
システム概要（システム構成・リポジトリ構成・レイヤ規約・スタック・連携・セキュリティ・インフラ。データモデル/API は持たない） |
docs/common/TABLE_DEFINITION.md |
全テーブル定義の SSoT（カード形式・ER図なし） |
docs/common/API_SPEC.md |
API 契約の SSoT（OpenAPI 風 Markdown。API がある場合） |
このうち ARCHITECTURE.md / TABLE_DEFINITION.md / API_SPEC.md は技術 spec (tech_specs) の common 分にあたる（→ stdd-methodology.md §3）。テンプレートは ../documenting-requirements/templates/requirements-common.md（REQUIREMENTS）/ ../documenting-tech-specs/templates/architecture-common.md（ARCHITECTURE。TABLE_DEFINITION / API_SPEC も同ディレクトリ）を参照する。
位置づけ — 導入時に一度だけ
common 階層はプロジェクト全体で 1 組しか存在しない SSoT であり、本スキルは STDD 導入時に一度だけ実行する想定。
作成後の更新（アーキテクチャ変更時など）は本スキルではなく、通常の Spec 更新として documenting-requirements（要件）／ documenting-tech-specs（ARCHITECTURE 等の技術設計）で扱う。
STDD 導入フロー（既存プロジェクト）
1. reverse-engineering-common-spec ← 本スキル（一度だけ）
↓ common 階層が揃う
2. reverse-engineering-feature-spec ← 機能ごとに繰り返す
↓
3. auto-implement ← 以降の新機能は順行 STDD
順序の理由: 先に common 階層（レイヤ規約・共有ドメインモデル・テーブル定義・API 契約）を固定しておくと、後続の機能単位リバース（reverse-engineering-feature-spec）の精度と速度が上がる。
最重要原則 — 実装・設定が真実
common 階層でも 実装が真実（Source of Truth） である。推測や理想論で書かず、必ず一次情報を確認してから書く。 feature 階層と違い、確認する一次情報は UI 文言ではなく構成・設定・型定義 である。
| 記述する内容 | 確認元（一次情報） | よくある間違い |
|---|---|---|
| アプリ構成・責務 | トップレベルディレクトリ / README | 想像でアプリ名・責務を書く |
| パッケージ分割 | package.json の workspaces / 依存定義 |
モジュール境界を推測で書く |
| レイヤ規約・依存方向 | domain/ 配下の実構成・lint ルール |
一般論の Clean Architecture を書く |
| 外部サービス連携 | 環境変数・SDK の import 箇所 | 使っていないサービスを書く／使用中を漏らす |
| テーブル一覧（TABLE_DEFINITION） | 生成された DB 型定義（database.types.ts 等） |
テーブル名・カラム名を想像で書く |
| デプロイ・環境 | CI/CD 設定（.github/workflows 等） |
ブランチ→環境マッピングを推測で書く |
読む順序とチェックリスト
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.
- 2mo ago First seen · 150 lines · 167 tokens per session scan A 793d17d2c077
reverse-engineering-common-spec is a skill published in the GitHub repository careerchain-ys/stdd (2 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 167 tokens to every session and 2,608 once invoked, about $0.0007 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 2026-08-31.
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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…