Agent skill

Project Guidelines Example

by vibeeval in vibeeval/vibecosystem

Example template for project-specific skill files covering architecture, patterns, testing, and deployment.

MITAuto-check: notesAI & LLM Engineering

Install Project Guidelines Example

skills CLI
$ npx skills add vibeeval/vibecosystem --skill project-guidelines-example -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vibeeval/vibecosystem project-guidelines-example --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/project-guidelines-example .claude/skills/project-guidelines-example && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
project-guidelines-example
GitHub stars
531
Used in
3 other repos
Token cost
~2.2k tokens
SKILL.md length
238 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

Example template for project-specific skill files covering architecture, patterns, testing, and deployment.

  • Works in 8 steps: No emojis in code, comments, or… → Immutability - never mutate objects or… → TDD - write tests before implementation → …
  • Tasks that involve Software architecture
  • SKILL.md covers When to Use, Architecture Overview, File Structure and Code Patterns, plus 4 more sections
  • Calls npm, poetry and gcloud; reaches xxx.supabase.co; needs NEXT_PUBLIC_SUPABASE_ANON_KEY and ANTHROPIC_API_KEY

What it does

Project Guidelines Example is an agent skill from vibeeval/vibecosystem. Example template for project-specific skill files covering architecture, patterns, testing, and deployment.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Software architecture and Structured output and tool calling. The repository describes itself as: AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. The licence is MIT.

When your agent uses it

  • Tasks that involve Software architecture
  • Tasks that involve Structured output and tool calling

Example prompts

  • “/project-guidelines-example”

Requirements

  • Python 3
  • A credential in NEXT_PUBLIC_SUPABASE_ANON_KEY
  • A credential in ANTHROPIC_API_KEY

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • poetry
    • gcloud

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • xxx.supabase.co

    Also links to:

    • zenith.chat

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • NEXT_PUBLIC_SUPABASE_ANON_KEY
    • ANTHROPIC_API_KEY
    • SUPABASE_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Project Guidelines Example loads about 2.2k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 238 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:318
    # Frontend (.env.local)
  • NoteMentions a .env fileSKILL.md:323
    # Backend (.env)

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 238 words, ~2,213 tokens.

Download SKILL.mdSave it as .claude/skills/project-guidelines-example/SKILL.md (or your agent's skills folder).
name
project-guidelines-example
description
Example template for project-specific skill files covering architecture, patterns, testing, and deployment.

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)
python
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)
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)
python
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)
typescript
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)
bash
# 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:

python
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)
bash
# Run tests
npm run test

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

# Run E2E tests
npm run test:e2e

Test structure:

typescript
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
bash
# 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
bash
# 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

  • 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

© vibeeval, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/project-guidelines-example of vibeeval/vibecosystem.

Open the folder on GitHubat commit 3b763b1

Used in 3 other repositories

We found 7 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in vibeeval/vibecosystem, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Testing LLMyonatangross/orchestkit289—~2.6kAutomated safety check: PassMIT
Planning With Filesjarrodwatts/claude-code-config1.1k5 repos~967Automated safety check: PassNone

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Questions about Project Guidelines Example

What does Project Guidelines Example do?

Example template for project-specific skill files covering architecture, patterns, testing, and deployment. Project Guidelines Example is an agent skill from vibeeval/vibecosystem. Example template for project-specific skill files covering architecture, patterns, testing, and deployment.

When should I use Project Guidelines Example?

Project Guidelines Example fits situations like: tasks that involve Software architecture; tasks that involve Structured output and tool calling.

How do I install Project Guidelines Example in Claude Code?

Run `npx skills add vibeeval/vibecosystem --skill project-guidelines-example -a claude-code`. Or copy the skill folder (skills/project-guidelines-example in vibeeval/vibecosystem) into .claude/skills/project-guidelines-example in your project. Claude Code loads it when a task matches its description.

How do I install Project Guidelines Example in Codex?

Run `npx skills add vibeeval/vibecosystem --skill project-guidelines-example -a codex`. Or copy the skill folder (skills/project-guidelines-example in vibeeval/vibecosystem) into .agents/skills/project-guidelines-example in your project. Codex loads it when a task matches its description.

Can I use Project Guidelines Example in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vibeeval/vibecosystem --skill project-guidelines-example -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/project-guidelines-example, .gemini/skills/project-guidelines-example, .github/skills/project-guidelines-example and .opencode/skills/project-guidelines-example in your project.

What does Project Guidelines Example need to run?

Going by SKILL.md and its folder, Project Guidelines Example needs the command-line tools its instructions call (npm, poetry and gcloud) and credentials named NEXT_PUBLIC_SUPABASE_ANON_KEY, ANTHROPIC_API_KEY and SUPABASE_KEY. Our summary lists: Python 3; A credential in NEXT_PUBLIC_SUPABASE_ANON_KEY; A credential in ANTHROPIC_API_KEY.

Does Project Guidelines Example access the network?

SKILL.md names 2 domains. In commands or code: xxx.supabase.co; the agent is likely to contact it when it follows the instructions. As links in the text: zenith.chat. This is read from the text; nothing was executed.

Is Project Guidelines Example safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Project Guidelines Example use?

Project Guidelines Example is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Project Guidelines Example use?

About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Project Guidelines Example?

Skills that share tags, products or a category with Project Guidelines Example: Cc Skill Project Guidelines Example (davila7/claude-code-templates, 32k stars), Add AI Chat Tool (ryokun6/ryos, 1.3k stars), Agent Interface Design (Neeeophytee/finding-unknowns-skills, 343 stars) and Testing LLM (yonatangross/orchestkit, 289 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Project Guidelines Example?

vibeeval (a GitHub user) maintains it in vibeeval/vibecosystem, which has 531 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on August 8, 2026.

Source: vibeeval/vibecosystem on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.