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Agent Skill · 研究检索

ai-repo-setup(ai 仓库设置)

ai-repo-setup 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ai-repo-setup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 49,433。

总安装 652周安装 28GitHub Stars 3下载 228核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-repo-setup(ai 仓库设置)
来源仓库:https://github.com/fellipeutaka/leon
仓库路径:skills/ai-repo-setup
安装命令:
npx skills add https://github.com/fellipeutaka/leon --skill ai-repo-setup
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/fellipeutaka/leon --skill ai-repo-setup

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于提升 AI 代理理解项目上下文的效率。聚焦代码即文档原则减少冗余说明。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。可自动生成最小可行协作规范与验收标准。需提前提供项目描述与业务规则等非功能性约束。

ai-repo-setup 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ai-repo-setup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/fellipeutaka/leon --skill ai-repo-setup;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

ai-repo-setup 怎么安装?

在终端执行 npx skills add https://github.com/fellipeutaka/leon --skill ai-repo-setup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-repo-setup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ai-repo-setup 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

ai-repo-setup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ai-repo-setup 用的人多吗?

公开数据是累计安装 652 次,近一周安装 28 次,下载 228 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ai-repo-setup 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
ai-repo-setup

ai 仓库设置 · 本页

6523调用外部服务未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

AI Repo Setup

Prepare a repository so AI coding agents can navigate, implement, and verify changes with minimal friction.

Core philosophy: AI agents are new starters with no memory. Every session starts fresh. The codebase itself — not documentation — is the primary context. Only document what is undiscoverable and globally relevant.

Inputs to gather (if missing)

  • One-sentence project description (what does this project do?)
  • Functional requirements (what the system should do)
  • Non-functional requirements (performance, security, scalability constraints)
  • Business rules (domain logic, validation rules, constraints)
  • External issue tracker (Linear, Jira, GitHub Issues, ClickUp, etc.) — if used by the team
  • Behavioral preferences for agents (see step 4 for what to ask)

Workflow

1. Analyze existing repo

  • Read package.json, config files, directory structure
  • Identify tech stack, package manager, existing scripts
  • Check for existing AGENTS.md, CLAUDE.md, docs/
  • Note what's already discoverable from source (don't re-document it)

2. Create or convert docs/REQUIREMENTS.md and docs/BUSINESS-RULES.md

If these files already exist, ask the user whether to convert them to the status-based format. Preserve all existing content — only restructure the format and add status fields. If creating from scratch, interview user or extract from existing code.

Choose the correct template based on whether the team uses an external tracker:

Solo mode (no external tracker)

Status lives in the doc. Single source of truth = docs.

# Requirements

## Status Reference

| Status | Meaning |
|--------|---------|
| `draft` | Written but not yet reviewed — may be vague or incomplete |
| `refined` | Reviewed and clarified by user, ready to implement |
| `in-progress` | Actively being implemented by the agent |
| `implemented` | Code written by agent, awaiting user review |
| `verified` | User reviewed and approved — only the user sets this |
| `deferred` | Intentionally postponed, not abandoned |
| `cancelled` | No longer relevant, kept for historical context |

## Functional Requirements

### [Feature Area]

#### FR-001: [Requirement title]

- **Status**: `draft`
- **Description**: [What the system should do]
Team mode (external tracker: Linear, Jira, GitHub Issues, etc.)

No **Status**: field in docs — status lives in the tracker. Single source of truth = tracker. The **Issue**: field links to the tracker item.

# Requirements

## Functional Requirements

### [Feature Area]

#### FR-001: [Requirement title]

- **Issue**: (none yet)
- **Description**: [What the system should do]

When **Issue**: is (none yet), the agent creates the issue in the tracker and writes back the ID (e.g., LINEAR-123, PROJ-42, #123).

Structure for docs/BUSINESS-RULES.md (same pattern in both modes):

# Business Rules

## [Domain Area]

### BR-001: [Rule name]

- **Status**: `draft`          ← solo mode
- **Issue**: (none yet)        ← team mode (use one, not both)
- **When**: [Trigger condition]
- **Then**: [Expected behavior]
- **Rationale**: [Why this rule exists]

Keep requirements specific, testable, and numbered for traceability.

Agent Workflow for Requirements & Business Rules

Solo mode lifecycle:

  1. Draft — user or agent adds a new item with status: draft
  2. Refine — agent clarifies until specific and testable; user confirms; status → refined
  3. Implement — user asks agent to implement a specific ID; agent implements it and updates status → implemented
  4. Verify — user reviews; if approved, status → verified; if rejected, status → in-progress with a note

The agent must never set status to verified — only the user does. The agent must update status to implemented before closing a session.

Team mode lifecycle (tracker integration):

When bulk-creating issues from requirements, follow the pre-flight checklist and patterns in references/tracker-issue-patterns.md.

When asked to implement a requirement:

  1. Check `Issue:` field

- If (none yet) → create an issue in the tracker via MCP (title = requirement title, description = requirement body), write back the ID - If an ID exists → query the tracker for current status before proceeding

  1. Check tracker status

- If in-progress (or equivalent) → warn the user and stop. Do not proceed silently. The user decides whether to continue, reassign, or skip. - Otherwise → transition to in-progress in the tracker, then implement

  1. After implementation → transition tracker status to the equivalent of implemented/done (discover available statuses via MCP; infer the mapping — do not hardcode it)
  2. Never query all requirements at session start — query only the specific item being worked on (lazy query)

3. Create context docs per deployment layer (if multi-layer project)

For projects with multiple independent layers (backend, frontend, mobile, etc.), create per-layer context docs that track implementation status separately.

See references/multi-layer-guide.md for the full guide: directory structures, context doc rules, monorepo vs separate repo layouts, and agent workflow with layered docs.

4. Generate minimal AGENTS.md

See references/agents-md-guide.md for full principles.

The file must be as small as possible. Only include:

  1. One-sentence project description
  2. Pointers to docs/ with the sync rule (mandatory — without this, the status lifecycle is dead on arrival since agents start fresh every session)
  3. Behavioral instructions (agent workflow preferences that are undiscoverable from source). Ask the user about each of these individually — do not collapse them into a single vague question:

- Plan mode: "How should I present plans? Concise or detailed? Should I list unresolved questions at the end?" - Communication style: "Any preferences for brevity, formality, or language?" - Docs lookup: "Should I consult Context7 (or similar) for up-to-date library docs before writing code, instead of relying on training data?" - Tracker integration: "Does the team use an external issue tracker (Linear, Jira, GitHub Issues)? Should I create/sync issues automatically?" - Workflow habits: "Any recurring instructions you find yourself repeating across sessions? (e.g., commit style, test expectations)"

Example (single-layer project):

# Project Name

SaaS platform for team retrospectives with real-time collaboration.

## Docs

- `docs/REQUIREMENTS.md` — functional and non-functional requirements
- `docs/BUSINESS-RULES.md` — domain rules and constraints

When implementing features or fixing bugs, update the relevant requirement/rule
status in these docs to keep them synced with the codebase.

## Docs Lookup

When implementing or fixing bugs involving external libraries, always consult
Context7 for up-to-date documentation before writing code. Never rely on
training data for library APIs.

## Plan Mode

- Make the plan extremely concise. Sacrifice grammar for the sake of concision.
- At the end of each plan, give me a list of unresolved questions to answer, if any.

For multi-layer projects, see the multi-layer guide for an AGENTS.md example.

Team mode addition — if the team uses an external tracker, add a Tracker section to AGENTS.md instead of a Docs sync rule:

## Docs

- `docs/REQUIREMENTS.md` — functional and non-functional requirements
- `docs/BUSINESS-RULES.md` — domain rules and constraints

## Tracker

Issue tracker: Linear (MCP available in this session).

When implementing a requirement:
- If `**Issue**:` is `(none yet)`, create an issue and write back the ID.
- If an ID exists, query the tracker for current status before proceeding.
- If status is in-progress, warn the user and stop — do not proceed silently.
- Discover available statuses via MCP; infer the mapping — do not hardcode it.
- Never query all requirements at session start — query only what you're about to work on.

When bulk-creating issues from requirements, follow the pre-flight checklist in
`references/tracker-issue-patterns.md` before creating anything.

Replace Linear with the actual tracker. Remove the "MCP available" note if the team doesn't use an MCP integration for that tracker.

Do NOT include: package manager (discoverable from lock files, packageManager field in package.json, or enforcement hooks), commands (discoverable from package.json scripts), architecture descriptions, file listings, framework/library explanations, implementation patterns. These are all discoverable from source.

5. Create CLAUDE.md symlink

ln -s AGENTS.md CLAUDE.md

This keeps Claude Code and other tools reading the same instructions.

6. Set up feedback loops (optional)

See references/feedback-loops.md for details.

Ask user which feedback loops to set up:

  • TypeScript type-check script in package.json
  • Test runner (vitest, jest, bun test)
  • E2E tests (playwright, cypress) — for frontend projects
  • Pre-commit hooks: Lefthook (recommended) or Husky + lint-staged
  • Code quality: Oxlint + Oxfmt (recommended), Biome, or ESLint + Prettier

Pre-commit hooks are the most powerful feedback loop for AI agents — they get error messages on failed commits and retry automatically.

7. Set up deterministic enforcement (optional)

See references/deterministic-enforcement.md for details.

Ask user which agent tools they use and set up enforcement for each:

  • Claude Code: PreToolUse hooks in .claude/settings.json (bash scripts)
  • OpenCode: plugins in .opencode/plugins/ (TypeScript/JavaScript modules)

Ask about both tools — users may use one or both. Set up enforcement for every tool the user opts into.

Common enforcement rules:

  • Enforce correct package manager (block npm, yarn, bun, deno if using pnpm)
  • Block dangerous git commands (git push --force, git reset --hard)
  • Block specific CLI patterns
  • Protect sensitive files (.env, credentials)

Enforcement saves instruction budget and is deterministic — rules cannot be ignored by the agent. It also makes the package manager discoverable by agents (they see the error message when blocked), which is why it doesn't need to be in AGENTS.md.

Deliverables

  • docs/REQUIREMENTS.md — numbered functional + non-functional requirements (definitions only)
  • docs/BUSINESS-RULES.md — numbered business rules with triggers/behavior (definitions only)
  • docs/[layer]/REQUIREMENTS.md — per-layer status + implementation scope (if multi-layer)
  • docs/[layer]/BUSINESS-RULES.md — per-layer enforcement status (if multi-layer)
  • AGENTS.md — minimal, hand-crafted, globally relevant only
  • CLAUDE.md — symlink to AGENTS.md
  • Feedback loops configured (if opted in)
  • Deterministic enforcement configured (if opted in)

Anti-patterns to avoid

  • Bloated AGENTS.md — every line costs tokens on every session
  • Documenting the discoverable — agents read package.json scripts, lock files (pnpm-lock.yaml, yarn.lock, etc.), packageManager field, config files, and imports. Don't repeat what they'll find in seconds.
  • Listing package manager or commands — discoverable from lock files, packageManager field in package.json, and enforcement hooks
  • File path references — paths change; describe capabilities instead
  • Auto-generated init files — stale immediately, actively mislead agents
  • Global rules for local concerns — use progressive disclosure or skills instead
  • Missing docs sync rule — without telling agents to update requirement/rule statuses, the entire lifecycle system is unused
  • Layer status in root docs — when using layered docs, agents must not update the root doc status to reflect only their own layer's progress. Root status is aggregated (all layers done → root promoted). Updating it prematurely misleads other layers into thinking the feature is complete across the whole system.
  • Dual source of truth for status — in team mode, never put **Status**: in docs alongside **Issue**:. The tracker is the single source of truth. Keeping both leads to divergence and confusion about which one is authoritative.

平台分布

Codex

36.31%
按下载量换算83

Claude

28.9%
按下载量换算66

Cursor

16.57%
按下载量换算38

Gemini CLI

9.45%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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