Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计通过

project-onboarding项目入职

Agent Skill

project-onboarding 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。

总安装

1,693

周安装

72

GitHub Stars

25

下载量

593
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:project-onboarding(项目入职)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/project-onboarding
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill project-onboarding
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/oimiragieo/agent-studio --skill project-onboarding

简介

project-onboarding 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于项目初始化、信息整合与协作准备等场景。
  • 通过 npx skills add 命令从 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态及是否触发联网或文件操作。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

When to Use

Invoke this skill when:

  • Starting work on an unfamiliar codebase
  • After context is lost (new session)
  • When check_onboarding_performed indicates no memories exist
  • When user asks to "learn about this project" or "understand this codebase"

Onboarding Workflow

Step 1: Check Existing Knowledge

First, check if onboarding was already performed:

List files in: .claude/context/memory/
Look for: project-structure.md, build-commands.md, test-commands.md

If memories exist, read them and skip to Step 6 (Validation).

Step 2: Project Discovery

First, classify the project:

Greenfield vs Brownfield Detection

IndicatorPresent?Classification
.git directory with historyYesBrownfield
Package manifest (package.json, requirements.txt, etc.)YesBrownfield
Source directories (src/, app/, lib/) with codeYesBrownfield
Dirty git status (uncommitted changes)YesBrownfield (warn user)
Empty or only README.mdNone of aboveGreenfield

For Brownfield Projects:

  1. Respect Ignore Files: Check .gitignore and .claudeignore BEFORE scanning
  2. Efficient File Triage:

- Use git ls-files to list tracked files (respects.gitignore) - For large files (>1MB): Read only head/tail (first and last 20 lines) - Skip binary files, node_modules, build artifacts

  1. Infer Tech Stack: Analyze manifests before asking questions
  2. Context-Aware Questions: Base questions on discovered patterns
# Efficient file listing (respects .gitignore)
git ls-files --exclude-standard -co | head -100

# For non-git projects with manual ignores
find . -type f \
  -not -path '*/node_modules/*' \
  -not -path '*/.git/*' \
  -not -path '*/dist/*' \
  -not -path '*/build/*' \
  | head -100

For Greenfield Projects:

  • Create fresh context artifacts
  • Use interactive-requirements-gathering skill for setup

Analyze the project root to identify:

  1. Package Manager & Language:

- package.json - Node.js/JavaScript/TypeScript - pyproject.toml, requirements.txt - Python - Cargo.toml - Rust - go.mod - Go - pom.xml, build.gradle - Java - composer.json - PHP

  1. Project Type:

- Frontend, Backend, Fullstack, Library, CLI, Mobile, Monorepo

  1. Framework Detection:

- Parse dependencies for frameworks (React, Next.js, FastAPI, etc.)

Step 3: Build System Analysis

Identify how to build/run the project:

  1. Check package.json scripts (Node.js): {"scripts": {"dev": "...", "build": "...", "start": "...", "test": "..."}}
  2. Check Makefiles (Python, Go, Rust): build: test: lint:
  3. Check pyproject.toml (Python): [tool.poetry.scripts] [tool.poe.tasks]
  4. Document discovered commands:

- Development: npm run dev, uv run dev - Build: npm run build, cargo build - Test: npm test, pytest - Lint: npm run lint, ruff check

Step 4: Directory Structure Mapping

Map key directories:

DirectoryPurpose
src/Source code
lib/Library code
test/, tests/, __tests__/Test files
docs/Documentation
scripts/Utility scripts
config/Configuration files

Identify:

  • Entry points (index.ts, main.py, app.py)
  • Component directories
  • API routes
  • Database models

Step 5: Create Onboarding Memories

Save discovered information to persistent memories:

Memory: project-structure.md

# Project Structure

## Overview

- Project Type: [fullstack/backend/frontend/library]
- Primary Language: [TypeScript/Python/Go/Rust]
- Framework: [Next.js/FastAPI/Express/etc.]

## Key Directories

- Source: `src/`
- Tests: `test/`
- Config: `.claude/`

## Entry Points

- Main: `src/index.ts`
- API: `src/api/`

## Important Files

- Configuration: `package.json`, `tsconfig.json`
- Environment: `.env.example`

Memory: build-commands.md

# Build Commands

## Development

- Start dev server: `npm run dev`
- Watch mode: `npm run watch`

## Build

- Production build: `npm run build`
- Type check: `npm run typecheck`

## Clean

- Clean build: `npm run clean`

Memory: test-commands.md

# Test Commands

## Unit Tests

- Run all: `npm test`
- Watch mode: `npm test -- --watch`
- Coverage: `npm test -- --coverage`

## E2E Tests

- Run: `npm run test:e2e`

## Linting

- Lint: `npm run lint`
- Fix: `npm run lint:fix`

Step 6: Validation

Validate discovered information:

  1. Test Commands (if safe):

- Run npm --version or equivalent to verify package manager - Run npm run --silent to list available scripts - Do NOT run build or test without user permission

  1. Verify Paths:

- Confirm key directories exist - Verify entry points are correct

Step 7: Report Summary

Output a concise summary:

## Onboarding Complete

**Project**: [name]
**Type**: [fullstack/backend/etc.]
**Framework**: [Next.js/FastAPI/etc.]

**Quick Commands**:
- Dev: `npm run dev`
- Test: `npm test`
- Build: `npm run build`

**Key Locations**:
- Source: `src/`
- Tests: `test/`
- API: `src/api/`

**Memories Created**:
- .claude/context/memory/project-structure.md
- .claude/context/memory/build-commands.md
- .claude/context/memory/test-commands.md

Agent Actions:

  1. Check for existing memories in .claude/context/memory/
  2. If no memories, run project discovery
  3. Analyze package.json, directory structure
  4. Create memory files
  5. Report summary

Output:

## Onboarding Complete

**Project**: agent-studio
**Type**: Multi-agent orchestration framework
**Framework**: Claude Code + Custom agents

**Quick Commands**:
- Validate: `node .claude/tools/cli/validate-agents.mjs`
- Test hooks: `node .claude/hooks/routing/router-enforcer.cjs` (uses `.claude/lib/routing/routing-table.cjs`)

**Key Locations**:
- Agents: `.claude/agents/`
- Skills: `.claude/skills/`
- Memory: `.claude/context/memory/`

**Memories Created**: 3 files

</usage_example>

Iron Laws

  1. ALWAYS check for existing onboarding memories before running discovery — duplicate onboarding creates contradictory knowledge entries that mislead future agents.
  2. NEVER assume standard conventions without verification — run commands to confirm what actually works in this specific project environment.
  3. ALWAYS write discovered commands and paths to persistent memory files — session context resets, but named memory files survive indefinitely.
  4. ALWAYS verify discovered commands with a safe test step before documenting — incorrect commands in onboarding memories mislead every agent that reads them.
  5. NEVER complete onboarding without revisiting memories when the project evolves — stale onboarding artifacts are more dangerous than no onboarding.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Assuming standard conventions without checkingEvery project has unique build/test/lint commands; wrong assumptions cause silent failuresRead package.json, Makefile, or pyproject.toml and run --version to confirm
Skipping verification of discovered commandsDocumented-but-wrong commands mislead every future agent sessionRun each command with a safe no-op or --help flag to confirm it works
Storing onboarding only in session contextContext resets on every new conversation; discoveries are permanently lostWrite all findings to named memory files in .claude/context/memory/named/
Treating onboarding as a one-time eventProjects evolve; stale commands fail silently and waste agent timeUpdate onboarding memories after any significant project structure change
Over-documenting without prioritizing key commandsLong files with low-priority info bury the critical build/test commandsStructure memories with Quick Start commands at the top, details below

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

  • New pattern discovered -> .claude/context/memory/learnings.md
  • Issue encountered -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.48%
按下载量换算222

Claude

32.72%
按下载量换算194

Cursor

17.72%
按下载量换算105

Gemini CLI

9.32%
按下载量换算55

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills