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 samqin123/Claude_skill_pool --skill ai-specgit clone --depth 1 https://github.com/samqin123/Claude_skill_poolWrote 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/samqin123/claude_skill_pool/ai-spec)<a href="https://agentmods.dev/skills/samqin123/claude_skill_pool/ai-spec"><img src="https://agentmods.dev/badge/skills/samqin123/claude_skill_pool/ai-spec.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 5.1 | $0.00045 | $0.01310 |
| Opus 5 | $0.00023 | $0.00655 |
| Sonnet 5 | $0.00009 | $0.00262 |
| Haiku 4.5 | $0.00005 | $0.00131 |
Grade A, and why
ai-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 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Spec
Overview
把用户需求翻译成结构化技术规格与可执行的“实现指令”，强调安全、测试、性能与可维护性。
Workflow
1. 需求审计
- 提取核心功能与非功能需求（性能、安全、可靠性、可维护性、部署约束）。
- 标出缺失信息，优先询问：技术栈/版本、数据规模、并发指标、部署环境、合规要求。
2. 架构与技术栈决策
- 给出 2-3 种实现路径并简要对比（性能、成本、生态、团队技能）。
- 选择最优方案并记录权衡（ADR）。
3. 规格与实现约束
- 生成目录结构、核心数据模型、关键流程说明。
- 明确错误处理、测试策略、安全防护、性能基线。
- 明确文档交付：前端变更的用户说明书、后端/环境变更的开发与部署文档。
4. AI 执行指令
- 输出可直接交给编码工具的分阶段任务清单（初始化 → 核心模型 → 业务逻辑 → 接口层 → 测试/文档）。
- 指令必须可执行、避免含糊措辞。
- Checkfix 闭环（必选）：生成的执行指令中必须包含「每阶段/每次代码变更后按技术栈执行自动检查」的步骤（见下方「技术栈与推荐检查」），形成基础开发工作流：实现 → 检查 → 修正 → 再验收。
5. 文档与部署策略（必选）
- 前端功能更新必须包含
docs/用户说明书更新任务，默认面向零基础用户，步骤写到可直接照做。 - 后端/API/环境迭代必须包含开发与部署文档更新任务，要求“新开发者可按文档独立完成部署与验证”。
- 每次功能或环境变更都要显式检查既有部署指导文档是否需要更新（如
docs/DEPLOYMENT.md、docs/README.md）。 - Python 部署优先级固定：
uv（注意是uv，不是uvicorn）> 直接部署 >conda。 - 涉及 PyTorch 且目标环境有 NVIDIA GPU 时，默认优先给出 CUDA 版本安装命令（含官方 CUDA 索引链接），并附 CPU 回退命令。
技术栈与推荐检查（须写入生成的 AI 指令）
| 技术栈/类型 | 推荐检查 | 说明 |
|---|---|---|
| Python | 优先 uv venv + uv sync（或 uv pip install -r requirements.txt），并执行 ruff check .、ruff format --check . 或 black --check . |
部署优先级：uv（非 uvicorn）> 直接部署 > conda |
| 前端 (Node/npm) | npm install（依赖变更时）、npm run lint 或 npx eslint .，可选 npm run build |
优先用 package.json scripts |
| PyTorch (GPU) | uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124（按目标 CUDA 版本调整） |
有 NVIDIA GPU 时优先 CUDA 包，并给 CPU 回退命令 |
| Rust | cargo check 或 cargo clippy |
编译与 Clippy |
| Go | go build ./...、gofmt -l . 或 golangci-lint run |
编译与格式/静态检查 |
| Java/Kotlin (Maven) | mvn compile 或 mvn verify |
编译与测试 |
| Java/Kotlin (Gradle) | ./gradlew compileJava 或 ./gradlew check |
同上 |
| C# / .NET | dotnet build、dotnet format --verify-no-changes |
编译与格式 |
| 通用 | 项目内已配置的 lint/format/check 脚本 | 优先执行项目既有脚本 |
Output Format (required)
# [项目名称]: 技术规范与 AI 指令
## 1. 需求审计总结（含缺失信息）
## 2. 架构决策记录（含备选与权衡）
## 3. 系统设计（目录结构 / 数据模型 / 关键流程）
## 4. 详细实现要求（错误处理 / 测试 / 安全 / 性能）
## 5. AI 执行指令（分阶段任务清单）
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.
- 8d ago First seen · 73 lines · 45 tokens per session scan A 3e4ed5dbeb6d
ai-spec is a skill published in the GitHub repository samqin123/Claude_skill_pool (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,310 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.
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