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4e07a6a874
* chore(stdlib): add conformance tooling for the skills library
The library had no gate that could express what a valid skill is, so
malformed frontmatter, over-budget files and credential literals went
unnoticed. These scripts define and enforce that contract.
- validate-skills.py: extend with Agent Skills v1.0 conformance -
naming rules and directory agreement, an explicit activation trigger
in the description, canonical layout, the 500-line SKILL.md budget,
relative-link integrity, and two security rules (no unrestricted
Bash(*) grant, no credential literals). Structural checks now read a
skill as a whole so material moved into references/ still counts.
- split-skill-references.py: move the heaviest sections of an oversized
SKILL.md into references/. Verifies line by line that every source
line lands in exactly one output and refuses to write otherwise.
- generate-skill-inventory.py: derive SKILLS_INVENTORY.md and
DIRECTORY_STRUCTURE.md from the tree so they cannot drift.
- generate-skill-catalog.py: index the canonical layout only, count
references/ toward a skill's size, and stop truncating descriptions
at 200 characters - the catalog is the discovery surface and must
carry the activation trigger.
- .gitignore: Python bytecode from these scripts, and local .pcl state.
Reviewed-by: Security Analyst, Compiler Engineer, DevX Engineer
* fix(stdlib): bring all 174 skills to Agent Skills v1.0 conformance
No skill in the library was conformant. 41 of 175 failed the existing
validator outright, and none satisfied the spec. This pass fixes the
tree and records the result. See stdlib/CHANGELOG.md for the per-skill
versioning and migration notes.
BREAKING CHANGE: one skill removed, five moved, seven with a capability
withdrawn. Library version 2.0.0 -> 3.0.0.
Structure
- Convert 71 skills stored as flat files to <category>/<name>/SKILL.md,
the layout the spec requires and the only one able to carry companion
resources.
- Rename 5 directories that disagreed with their declared name:, so
resolution by path and by name stop giving different answers. The
repository docs already referenced the corrected paths.
- Merge the duplicate legal-technology skills into legaltech-expert and
delete legal-tech-expert, carrying over everything the survivor
lacked.
Metadata
- Repair 20 unparseable frontmatters: an H1 preceded the --- block and
the key was skill_id: instead of name:, so any parser reading
frontmatter at offset 0 saw no metadata at all. The objectives,
prerequisites and outcome they carried become real body sections.
- Add the missing version: to 21 skills, fold keywords into tags, move
expertise_level under metadata, and align 10 category: values with
their directory.
Discoverability
- Give all 174 descriptions an explicit "Use when ..." trigger, built
from each skill's own tags and section headings. 152 previously said
only what the skill was about, leaving an agent nothing to match on.
Progressive disclosure
- Split 127 oversized skills, moving bulk material into references/ read
on demand and leaving an index behind. A SKILL.md now averages 277
lines instead of 644 and none exceeds 500. Verified lossless line by
line against the previous revision.
Security
- Withdraw the unrestricted Bash(*) grant from 7 skills; none of the six
non-financial ones contained a single shell command. finance-expert
documents code that charges cards and reads bank accounts, so it now
grants no shell at all.
- Remove credential literals: Stripe keys, two JWT signing keys, an
Express session secret, a Redis password and WiFi credentials, all
inlined in examples. They load from the environment now.
- finance-expert: idempotency keys were documented but absent, so a
retry double-charged; webhook signature failures were not caught, so a
forged event raised uncaught instead of being rejected;
int(amount * 100) truncated fractions of a cent. All fixed, plus a
money-movement guardrails section.
- trading-expert: place_order routed without any pre-trade risk check
although a RiskManager sat unused in the next section. Risk validation
now runs before routing and raises rather than returning a boolean a
caller can ignore; live routing is opt-in.
- Correct an Ansible Vault example that showed plaintext under an
"encrypted" label.
Also fixes 8 broken documentation links and a prematurely closed code
fence in skill-creator-expert that leaked template content into prose.
Per the repository file-organization policy, four stale progress reports
(SKILLS_MANIFEST, COMPLETE_SKILLS_LIBRARY, COMPLETE_LIBRARY_MANIFEST,
PHASE_3_DATA_ANALYTICS) move out of stdlib/ into the gitignored
.roadmap/status/stdlib/; they claimed 31 of 100 skills complete. They
appear here as deletions because .roadmap/ is not tracked.
Reviewed-by: Security Analyst, Language Designer, DevX Engineer,
Documentation Specialist
* fix(skills): resolve standard library skills from their real layout
No stdlib skill was resolvable. SkillResolver looked for
`<stdlibDir>/<name>.md` with stdlibDir defaulting to `stdlib/skills`, a
directory that does not exist, and the path ignored the category level
the library actually uses. The two LSP providers had the same wrong
directory and scanned only one level deep, so neither completion nor
go-to-definition could see a skill either.
- skill-discovery.ts: enumerate skill entry points across the shapes in
use - a flat `<name>.md`, `<name>/SKILL.md`, and
`<category>/<name>/SKILL.md` - while skipping references/, scripts/
and assets/, which hold supporting material rather than entry points.
- skill-resolver.ts: point stdlibDir at `stdlib`, and look the name up
as a directory under any category as well as the two legacy shapes, so
existing custom stdlib directories keep working. The name is used as a
single path segment and rejected if it contains a separator or
traversal, so a reference cannot escape the stdlib root.
- Rewire both LSP providers onto the shared discovery helper.
Tests: 13 new cases covering the layouts, ordering, the references/
exclusion, path-traversal rejection, and an integration test that
resolves real shipped skills by bare name.
Reviewed-by: Compiler Engineer, Runtime Architect, Security Analyst
* feat(stdlib): add 17 skills covering the library's largest gaps
Selection was driven by cross-referencing coverage against observed usage
rather than by intuition: Stack Overflow and GitHub Octoverse data for
which technologies matter, and install rankings on public agent-skill
registries for the shape of skills people actually use.
That comparison found a mismatch of form, not only of coverage. The
most-installed skills in the ecosystem are procedures - tdd, refactoring,
frontend design, browser automation - while all 174 existing skills were
domain knowledge shaped as <domain>-expert. The new workflows/ category
closes that gap and introduces the <activity>-workflow convention.
New category workflows/ (4)
- tdd-workflow, refactoring-workflow, debugging-workflow,
code-review-workflow. code-review-workflow complements rather than
replaces code-review-expert: procedure versus judgement. Merge them if
the split proves confusing in use.
Applied AI (3) - the largest coverage gap
- llm-engineering-expert, rag-expert, agent-engineering-expert.
The library referenced LLMs in tags but covered none of it: pgvector
appeared zero times across 174 skills, LangChain once, MCP twice
despite the repository shipping an MCP integration guide.
Capabilities (2)
- document-processing-expert (PDF, DOCX, XLSX, PPTX in one skill),
browser-automation-expert (a client against third-party sites,
distinct from playwright-expert which tests your own application).
Data (3)
- mysql-expert, the second most used database and previously absent
while rarer stores had skills; analytical-databases-expert;
stream-processing-expert.
Security (3)
- secrets-management-expert, supply-chain-security-expert (OWASP A03
2025), identity-access-expert (OWASP A01).
Interface and meta (2)
- accessibility-expert: design/ held only design-expert, which despite
its name covers software architecture, so WCAG had no home.
- skill-router: find and compose skills across 191, with a
disambiguation table for overlapping pairs.
Grouping by decision context rather than by technology keeps the growth
to 17 skills for roughly 50 topics. Five vector stores live inside
rag-expert; four document formats inside document-processing-expert.
Per-tool depth sits in references/, which the progressive-disclosure work
in 3.0.0 made cheap.
Four further gaps are better served by extending existing skills -
PyTorch and TensorFlow in ml-expert, OpenTelemetry in monitoring-expert,
Pulumi and Crossplane in terraform-expert - and are not in this change.
Library 3.0.0 -> 3.1.0, additive only. Adds the workflows label to the
inventory generator; catalogue, inventory, structure and CHANGELOG
regenerated.
Reviewed-by: Product Strategist, Security Analyst, DevX Engineer,
Documentation Specialist
* fix(test): create test directories with mkdtempSync
CodeQL flagged js/insecure-temporary-file (CWE-377) on both suites.
`join(tmpdir(), <predictable name>)` followed by mkdirSync creates a
world-readable directory at a guessable path, so another local user can
pre-create it or plant a symlink between the name being computed and the
directory being created.
mkdtempSync creates the directory atomically with a random suffix and
0700 permissions, which closes both the disclosure and the race.
One occurrence is new in this branch (skill-discovery); the other
pre-dates it in skill-resolver and is fixed in the same pass rather than
left as a known finding in a file this branch already touches.
13 KiB
13 KiB
name, version, description, category, tags, allowed-tools
| name | version | description | category | tags | allowed-tools | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| api-design-expert | 1.1.0 | Expert-level API design principles, REST, GraphQL, versioning, and API best practices. Use when the user mentions REST, GraphQL, API versioning, or API security, or when the task involves API Design Principles, API Styles, or Documentation. | api |
|
|
API Design Expert
Expert guidance for API design, RESTful principles, GraphQL, versioning strategies, and API best practices.
Core Concepts
API Design Principles
- RESTful architecture
- Resource-oriented design
- Uniform interface
- Statelessness
- Cacheability
- Layered system
API Styles
- REST (Representational State Transfer)
- GraphQL
- RPC (Remote Procedure Call)
- WebSocket
- Server-Sent Events (SSE)
- gRPC
Key Considerations
- Versioning strategies
- Authentication and authorization
- Rate limiting
- Error handling
- Documentation
- Backward compatibility
REST API Design
from fastapi import FastAPI, HTTPException, Query, Path, Header
from pydantic import BaseModel, Field
from typing import List, Optional
from datetime import datetime
from enum import Enum
app = FastAPI(
title="User Management API",
version="1.0.0",
description="RESTful API for user management"
)
# Models
class UserRole(str, Enum):
ADMIN = "admin"
USER = "user"
GUEST = "guest"
class UserCreate(BaseModel):
email: str = Field(..., example="user@example.com")
name: str = Field(..., min_length=1, max_length=100)
role: UserRole = UserRole.USER
class UserResponse(BaseModel):
id: str
email: str
name: str
role: UserRole
created_at: datetime
updated_at: datetime
class Config:
schema_extra = {
"example": {
"id": "123e4567-e89b-12d3-a456-426614174000",
"email": "user@example.com",
"name": "John Doe",
"role": "user",
"created_at": "2024-01-01T00:00:00Z",
"updated_at": "2024-01-01T00:00:00Z"
}
}
class UserUpdate(BaseModel):
name: Optional[str] = Field(None, min_length=1, max_length=100)
role: Optional[UserRole] = None
# REST Endpoints following best practices
@app.get("/api/v1/users",
response_model=List[UserResponse],
summary="List all users",
tags=["Users"])
async def list_users(
page: int = Query(1, ge=1, description="Page number"),
page_size: int = Query(20, ge=1, le=100, description="Items per page"),
sort: str = Query("created_at", description="Sort field"),
order: str = Query("desc", regex="^(asc|desc)$")
):
"""
Retrieve a paginated list of users.
- **page**: Page number (starts at 1)
- **page_size**: Number of items per page (1-100)
- **sort**: Field to sort by
- **order**: Sort order (asc or desc)
"""
# Implementation
return []
@app.get("/api/v1/users/{user_id}",
response_model=UserResponse,
summary="Get user by ID",
tags=["Users"])
async def get_user(
user_id: str = Path(..., description="User ID")
):
"""Retrieve a specific user by ID."""
# Implementation
raise HTTPException(status_code=404, detail="User not found")
@app.post("/api/v1/users",
response_model=UserResponse,
status_code=201,
summary="Create new user",
tags=["Users"])
async def create_user(user: UserCreate):
"""Create a new user."""
# Implementation
return UserResponse(
id="123e4567-e89b-12d3-a456-426614174000",
email=user.email,
name=user.name,
role=user.role,
created_at=datetime.now(),
updated_at=datetime.now()
)
@app.patch("/api/v1/users/{user_id}",
response_model=UserResponse,
summary="Update user",
tags=["Users"])
async def update_user(
user_id: str = Path(..., description="User ID"),
user: UserUpdate = None
):
"""Partially update a user."""
# Implementation
pass
@app.delete("/api/v1/users/{user_id}",
status_code=204,
summary="Delete user",
tags=["Users"])
async def delete_user(
user_id: str = Path(..., description="User ID")
):
"""Delete a user."""
# Implementation
pass
# Nested resources
@app.get("/api/v1/users/{user_id}/posts",
summary="Get user posts",
tags=["Users", "Posts"])
async def get_user_posts(user_id: str):
"""Retrieve all posts for a specific user."""
return []
API Versioning
from fastapi import APIRouter, Request
# URL Path Versioning (Recommended)
v1_router = APIRouter(prefix="/api/v1")
v2_router = APIRouter(prefix="/api/v2")
@v1_router.get("/users")
async def get_users_v1():
"""Version 1: Returns basic user info"""
return [{"id": 1, "name": "John"}]
@v2_router.get("/users")
async def get_users_v2():
"""Version 2: Returns enhanced user info"""
return [{"id": 1, "name": "John", "email": "john@example.com"}]
# Header Versioning
async def version_from_header(request: Request):
version = request.headers.get("API-Version", "1")
return version
@app.get("/api/users")
async def get_users(version: str = Depends(version_from_header)):
if version == "2":
return get_users_v2()
return get_users_v1()
# Content Negotiation Versioning
@app.get("/api/users")
async def get_users_content_negotiation(
accept: str = Header("application/vnd.api+json; version=1")
):
if "version=2" in accept:
return get_users_v2()
return get_users_v1()
Error Handling
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel, ValidationError
class ErrorResponse(BaseModel):
error: str
message: str
details: Optional[dict] = None
timestamp: datetime
path: str
@app.exception_handler(HTTPException)
async def http_exception_handler(request: Request, exc: HTTPException):
return JSONResponse(
status_code=exc.status_code,
content=ErrorResponse(
error=exc.status_code,
message=exc.detail,
timestamp=datetime.now(),
path=request.url.path
).dict()
)
@app.exception_handler(ValidationError)
async def validation_exception_handler(request: Request, exc: ValidationError):
return JSONResponse(
status_code=422,
content=ErrorResponse(
error="validation_error",
message="Request validation failed",
details=exc.errors(),
timestamp=datetime.now(),
path=request.url.path
).dict()
)
# Custom business logic errors
class BusinessError(Exception):
def __init__(self, message: str, code: str):
self.message = message
self.code = code
@app.exception_handler(BusinessError)
async def business_error_handler(request: Request, exc: BusinessError):
return JSONResponse(
status_code=400,
content=ErrorResponse(
error=exc.code,
message=exc.message,
timestamp=datetime.now(),
path=request.url.path
).dict()
)
Rate Limiting
from fastapi import Request, HTTPException
from datetime import datetime, timedelta
import redis
from typing import Dict
class RateLimiter:
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
async def check_rate_limit(self,
key: str,
max_requests: int,
window_seconds: int) -> Dict:
"""
Token bucket algorithm for rate limiting
"""
now = datetime.now().timestamp()
window_key = f"rate_limit:{key}:{int(now // window_seconds)}"
pipe = self.redis.pipeline()
pipe.incr(window_key)
pipe.expire(window_key, window_seconds)
result = pipe.execute()
request_count = result[0]
if request_count > max_requests:
reset_time = (int(now // window_seconds) + 1) * window_seconds
raise HTTPException(
status_code=429,
detail="Rate limit exceeded",
headers={
"X-RateLimit-Limit": str(max_requests),
"X-RateLimit-Remaining": "0",
"X-RateLimit-Reset": str(reset_time)
}
)
return {
"limit": max_requests,
"remaining": max_requests - request_count,
"reset": (int(now // window_seconds) + 1) * window_seconds
}
# Middleware for rate limiting
@app.middleware("http")
async def rate_limit_middleware(request: Request, call_next):
limiter = RateLimiter(redis_client)
# Get client identifier (IP, API key, etc.)
client_id = request.client.host
try:
rate_info = await limiter.check_rate_limit(
client_id,
max_requests=100,
window_seconds=60
)
response = await call_next(request)
# Add rate limit headers
response.headers["X-RateLimit-Limit"] = str(rate_info["limit"])
response.headers["X-RateLimit-Remaining"] = str(rate_info["remaining"])
response.headers["X-RateLimit-Reset"] = str(rate_info["reset"])
return response
except HTTPException as e:
return JSONResponse(
status_code=e.status_code,
content={"error": e.detail},
headers=e.headers
)
HATEOAS Implementation
from typing import Dict, List
class HATEOASResponse(BaseModel):
data: dict
links: Dict[str, str]
def create_links(resource_id: str, resource_type: str) -> Dict[str, str]:
"""Create HATEOAS links for a resource"""
return {
"self": f"/api/v1/{resource_type}/{resource_id}",
"update": f"/api/v1/{resource_type}/{resource_id}",
"delete": f"/api/v1/{resource_type}/{resource_id}",
"collection": f"/api/v1/{resource_type}"
}
@app.get("/api/v1/users/{user_id}", response_model=HATEOASResponse)
async def get_user_with_links(user_id: str):
user = get_user_from_db(user_id)
return HATEOASResponse(
data=user,
links={
"self": f"/api/v1/users/{user_id}",
"posts": f"/api/v1/users/{user_id}/posts",
"profile": f"/api/v1/users/{user_id}/profile",
"update": f"/api/v1/users/{user_id}",
"delete": f"/api/v1/users/{user_id}"
}
)
API Security
import os
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from jose import JWTError, jwt
from passlib.context import CryptContext
security = HTTPBearer()
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
# The JWT signing key. A literal here means every deployment that copies
# this file shares a forgeable key - load it, and fail closed if absent.
SECRET_KEY = os.environ["JWT_SECRET_KEY"]
ALGORITHM = "HS256"
async def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
"""Verify JWT token"""
try:
payload = jwt.decode(
credentials.credentials,
SECRET_KEY,
algorithms=[ALGORITHM]
)
return payload
except JWTError:
raise HTTPException(
status_code=401,
detail="Invalid authentication credentials"
)
@app.get("/api/v1/protected")
async def protected_route(token_payload: dict = Depends(verify_token)):
return {"message": "Access granted", "user": token_payload}
# API Key Authentication
async def verify_api_key(api_key: str = Header(...)):
"""Verify API key"""
if api_key not in valid_api_keys:
raise HTTPException(status_code=403, detail="Invalid API key")
return api_key
Best Practices
Design
- Use nouns for resources, not verbs
- Use plural names for collections
- Use HTTP methods correctly (GET, POST, PUT, PATCH, DELETE)
- Return appropriate status codes
- Support filtering, sorting, and pagination
- Use consistent naming conventions
- Version APIs from the start
Documentation
- Use OpenAPI/Swagger
- Provide example requests and responses
- Document error codes and messages
- Include authentication requirements
- Keep documentation up-to-date
- Provide SDKs when possible
Performance
- Implement caching (ETags, Cache-Control)
- Support compression (gzip)
- Paginate large result sets
- Use async operations for long tasks
- Implement rate limiting
- Monitor API performance
Security
- Use HTTPS always
- Implement authentication and authorization
- Validate all inputs
- Rate limit to prevent abuse
- Log security events
- Use API keys or OAuth 2.0
- Implement CORS properly
Anti-Patterns
❌ Using GET for state-changing operations ❌ Returning inconsistent response formats ❌ No versioning strategy ❌ Poor error messages ❌ No rate limiting ❌ Exposing internal implementation details ❌ Breaking changes without versioning
Resources
- REST API Design Best Practices: https://restfulapi.net/
- OpenAPI Specification: https://swagger.io/specification/
- API Design Guide: https://cloud.google.com/apis/design
- Microsoft REST API Guidelines: https://github.com/microsoft/api-guidelines
- FastAPI: https://fastapi.tiangolo.com/