AGENT SKILL

project-guidelines-example

Example project-specific skill template based on a real production application.

License 许可
Apache-2.0
最近更新
2026-11-11
Stars
★ 1.6k
Forks
474

01 前言 Front Matter

name
project-guidelines-example
origin
ECC
description
Example project-specific skill template based on a real production application.

02 资源文件 1

03 SKILL.md 正文

Project Guidelines Skill (Example)

This is an example of a project-specific skill. Use this as a template for your own projects.

Based on a real production application: Zenith - AI-powered customer discovery platform.

When to Use

Reference this skill when working on the specific project it's designed for. Project skills contain:

  • Architecture overview
  • File structure
  • Code patterns
  • Testing requirements
  • Deployment workflow

Architecture Overview

Tech Stack:

  • Frontend: Next.js 15 (App Router), TypeScript, React
  • Backend: FastAPI (Python), Pydantic models
  • Database: Supabase (PostgreSQL)
  • AI: Claude API with tool calling and structured output
  • Deployment: Google Cloud Run
  • Testing: Playwright (E2E), pytest (backend), React Testing Library

Services:

┌─────────────────────────────────────────────────────────────┐
│                         Frontend                            │
│  Next.js 15 + TypeScript + TailwindCSS                     │
│  Deployed: Vercel / Cloud Run                              │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                         Backend                             │
│  FastAPI + Python 3.11 + Pydantic                          │
│  Deployed: Cloud Run                                       │
└─────────────────────────────────────────────────────────────┘
                              │
              ┌───────────────┼───────────────┐
              ▼               ▼               ▼
        ┌──────────┐   ┌──────────┐   ┌──────────┐
        │ Supabase │   │  Claude  │   │  Redis   │
        │ Database │   │   API    │   │  Cache   │
        └──────────┘   └──────────┘   └──────────┘

File Structure

project/
├── frontend/
│   └── src/
│       ├── app/              # Next.js app router pages
│       │   ├── api/          # API routes
│       │   ├── (auth)/       # Auth-protected routes
│       │   └── workspace/    # Main app workspace
│       ├── components/       # React components
│       │   ├── ui/           # Base UI components
│       │   ├── forms/        # Form components
│       │   └── layouts/      # Layout components
│       ├── hooks/            # Custom React hooks
│       ├── lib/              # Utilities
│       ├── types/            # TypeScript definitions
│       └── config/           # Configuration
│
├── backend/
│   ├── routers/              # FastAPI route handlers
│   ├── models.py             # Pydantic models
│   ├── main.py               # FastAPI app entry
│   ├── auth_system.py        # Authentication
│   ├── database.py           # Database operations
│   ├── services/             # Business logic
│   └── tests/                # pytest tests
│
├── deploy/                   # Deployment configs
├── docs/                     # Documentation
└── scripts/                  # Utility scripts

Code Patterns

API Response Format (FastAPI)

from pydantic import BaseModel
from typing import Generic, TypeVar, Optional

T = TypeVar('T')

class ApiResponse(BaseModel, Generic[T]):
    success: bool
    data: Optional[T] = None
    error: Optional[str] = None

    @classmethod
    def ok(cls, data: T) -> "ApiResponse[T]":
        return cls(success=True, data=data)

    @classmethod
    def fail(cls, error: str) -> "ApiResponse[T]":
        return cls(success=False, error=error)

Frontend API Calls (TypeScript)

interface ApiResponse<T> {
  success: boolean
  data?: T
  error?: string
}

async function fetchApi<T>(
  endpoint: string,
  options?: RequestInit
): Promise<ApiResponse<T>> {
  try {
    const response = await fetch(`/api${endpoint}`, {
      ...options,
      headers: {
        'Content-Type': 'application/json',
        ...options?.headers,
      },
    })

    if (!response.ok) {
      return { success: false, error: `HTTP ${response.status}` }
    }

    return await response.json()
  } catch (error) {
    return { success: false, error: String(error) }
  }
}

Claude AI Integration (Structured Output)

from anthropic import Anthropic
from pydantic import BaseModel

class AnalysisResult(BaseModel):
    summary: str
    key_points: list[str]
    confidence: float

async def analyze_with_claude(content: str) -> AnalysisResult:
    client = Anthropic()

    response = client.messages.create(
        model="claude-sonnet-4-5-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": content}],
        tools=[{
            "name": "provide_analysis",
            "description": "Provide structured analysis",
            "input_schema": AnalysisResult.model_json_schema()
        }],
        tool_choice={"type": "tool", "name": "provide_analysis"}
    )

    # Extract tool use result
    tool_use = next(
        block for block in response.content
        if block.type == "tool_use"
    )

    return AnalysisResult(**tool_use.input)

Custom Hooks (React)

import { useState, useCallback } from 'react'

interface UseApiState<T> {
  data: T | null
  loading: boolean
  error: string | null
}

export function useApi<T>(
  fetchFn: () => Promise<ApiResponse<T>>
) {
  const [state, setState] = useState<UseApiState<T>>({
    data: null,
    loading: false,
    error: null,
  })

  const execute = useCallback(async () => {
    setState(prev => ({ ...prev, loading: true, error: null }))

    const result = await fetchFn()

    if (result.success) {
      setState({ data: result.data!, loading: false, error: null })
    } else {
      setState({ data: null, loading: false, error: result.error! })
    }
  }, [fetchFn])

  return { ...state, execute }
}

Testing Requirements

Backend (pytest)

# Run all tests
poetry run pytest tests/

# Run with coverage
poetry run pytest tests/ --cov=. --cov-report=html

# Run specific test file
poetry run pytest tests/test_auth.py -v

Test structure:

import pytest
from httpx import AsyncClient
from main import app

@pytest.fixture
async def client():
    async with AsyncClient(app=app, base_url="http://test") as ac:
        yield ac

@pytest.mark.asyncio
async def test_health_check(client: AsyncClient):
    response = await client.get("/health")
    assert response.status_code == 200
    assert response.json()["status"] == "healthy"

Frontend (React Testing Library)

# Run tests
npm run test

# Run with coverage
npm run test -- --coverage

# Run E2E tests
npm run test:e2e

Test structure:

import { render, screen, fireEvent } from '@testing-library/react'
import { WorkspacePanel } from './WorkspacePanel'

describe('WorkspacePanel', () => {
  it('renders workspace correctly', () => {
    render(<WorkspacePanel />)
    expect(screen.getByRole('main')).toBeInTheDocument()
  })

  it('handles session creation', async () => {
    render(<WorkspacePanel />)
    fireEvent.click(screen.getByText('New Session'))
    expect(await screen.findByText('Session created')).toBeInTheDocument()
  })
})

Deployment Workflow

Pre-Deployment Checklist

  • [ ] All tests passing locally
  • [ ] npm run build succeeds (frontend)
  • [ ] poetry run pytest passes (backend)
  • [ ] No hardcoded secrets
  • [ ] Environment variables documented
  • [ ] Database migrations ready

Deployment Commands

# Build and deploy frontend
cd frontend && npm run build
gcloud run deploy frontend --source .

# Build and deploy backend
cd backend
gcloud run deploy backend --source .

Environment Variables

# Frontend (.env.local)
NEXT_PUBLIC_API_URL=https://api.example.com
NEXT_PUBLIC_SUPABASE_URL=https://xxx.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=eyJ...

# Backend (.env)
DATABASE_URL=postgresql://...
ANTHROPIC_API_KEY=sk-ant-...
SUPABASE_URL=https://xxx.supabase.co
SUPABASE_KEY=eyJ...

Critical Rules

  1. No emojis in code, comments, or documentation
  2. Immutability - never mutate objects or arrays
  3. TDD - write tests before implementation
  4. 80% coverage minimum
  5. Many small files - 200-400 lines typical, 800 max
  6. No console.log in production code
  7. Proper error handling with try/catch
  8. Input validation with Pydantic/Zod

Related Skills

  • coding-standards.md - General coding best practices
  • backend-patterns.md - API and database patterns
  • frontend-patterns.md - React and Next.js patterns
  • tdd-workflow/ - Test-driven development methodology

04 同仓库更多 Skills

hashgraph-online
hashgraph-online/awesome-codex-plugins

migrate-to-e2e

Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for /migrate-to-e2e, "migrate our tests to e2e", "port these Maestro flows", "move our TesterArmy tests", or when a project has an e2e config and an older suite still running.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

e2e

Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

create-verification-skill

Generate a project-local verify-<app> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for /create-verification-skill, "make a verification skill for this repo", or when a project has no scripted way to prove UI behavior.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

deja-search

deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

deja-reflect

End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

revid

Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.

图像与多媒体Skill与智能体开发
★ 1.6k