Files
personamanagmentlayer__pcl/stdlib/api/api-design-expert/SKILL.md
ANGX 4e07a6a874 feat(stdlib)!: Agent Skills v1.0 conformance, resolver fix, and 17 new skills (#43)
* 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withdrawn.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.
2026-09-05 04:35:30 +02:00

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
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api-security
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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