Files
personamanagmentlayer__pcl/stdlib/api/fastapi-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withdrawn.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

7.8 KiB

name, version, description, category, tags, allowed-tools
name version description category tags allowed-tools
fastapi-expert 1.1.0 Expert-level FastAPI development for high-performance Python APIs with async support. Use when the user mentions Python, API, async, REST, OpenAPI, or Pydantic, or when the task involves FastAPI Features. api
fastapi
python
api
async
rest
openapi
pydantic
Read
Write
Edit
Bash(python:*, pip:*, uvicorn:*)

FastAPI Expert

Expert guidance for FastAPI - modern, fast Python web framework for building APIs with automatic OpenAPI documentation and type safety.

Core Concepts

FastAPI Features

  • Fast performance (Starlette + Pydantic)
  • Automatic OpenAPI/Swagger docs
  • Type hints and validation
  • Async/await support
  • Dependency injection
  • OAuth2 and JWT
  • WebSocket support

Key Components

  • Path operations (routes)
  • Request/response models (Pydantic)
  • Dependency injection
  • Middleware
  • Background tasks
  • Testing with TestClient

Basic FastAPI Application

from fastapi import FastAPI, HTTPException, Depends
from pydantic import BaseModel, EmailStr, Field
from typing import List, Optional
import uvicorn

app = FastAPI(
    title="My API",
    description="Production-ready FastAPI",
    version="1.0.0"
)

# Models
class UserCreate(BaseModel):
    email: EmailStr
    name: str = Field(..., min_length=2, max_length=100)
    age: Optional[int] = Field(None, ge=0, le=150)

class UserResponse(BaseModel):
    id: int
    email: str
    name: str

    class Config:
        from_attributes = True

# Routes
@app.post("/users", response_model=UserResponse, status_code=201)
async def create_user(user: UserCreate):
    # Create user in database
    db_user = await db.users.create(**user.dict())
    return db_user

@app.get("/users/{user_id}", response_model=UserResponse)
async def get_user(user_id: int):
    user = await db.users.get(user_id)
    if not user:
        raise HTTPException(status_code=404, detail="User not found")
    return user

@app.get("/users", response_model=List[UserResponse])
async def list_users(skip: int = 0, limit: int = 100):
    return await db.users.find_many(skip=skip, limit=limit)

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Dependency Injection

from fastapi import Depends, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from sqlalchemy.ext.asyncio import AsyncSession

# Database dependency
async def get_db() -> AsyncSession:
    async with async_session() as session:
        yield session

# Auth dependency
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

async def get_current_user(
    token: str = Depends(oauth2_scheme),
    db: AsyncSession = Depends(get_db)
):
    credentials_exception = HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail="Could not validate credentials"
    )

    payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
    user_id: int = payload.get("sub")
    if user_id is None:
        raise credentials_exception

    user = await db.get(User, user_id)
    if user is None:
        raise credentials_exception

    return user

# Use dependencies
@app.get("/me", response_model=UserResponse)
async def read_users_me(current_user: User = Depends(get_current_user)):
    return current_user

@app.post("/posts")
async def create_post(
    post: PostCreate,
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db)
):
    db_post = Post(**post.dict(), user_id=current_user.id)
    db.add(db_post)
    await db.commit()
    return db_post

Authentication

from datetime import datetime, timedelta
from jose import JWTError, jwt
from passlib.context import CryptContext

pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")

def verify_password(plain_password: str, hashed_password: str) -> bool:
    return pwd_context.verify(plain_password, hashed_password)

def get_password_hash(password: str) -> str:
    return pwd_context.hash(password)

def create_access_token(data: dict, expires_delta: timedelta = None):
    to_encode = data.copy()
    expire = datetime.utcnow() + (expires_delta or timedelta(minutes=15))
    to_encode.update({"exp": expire})
    return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)

@app.post("/token")
async def login(
    form_data: OAuth2PasswordRequestForm = Depends(),
    db: AsyncSession = Depends(get_db)
):
    user = await authenticate_user(db, form_data.username, form_data.password)
    if not user:
        raise HTTPException(status_code=401, detail="Invalid credentials")

    access_token = create_access_token(data={"sub": str(user.id)})
    return {"access_token": access_token, "token_type": "bearer"}

Database Integration (SQLAlchemy)

from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
from sqlalchemy.orm import sessionmaker, declarative_base
from sqlalchemy import Column, Integer, String, Boolean

DATABASE_URL = "postgresql+asyncpg://user:password@localhost/dbname"

engine = create_async_engine(DATABASE_URL, echo=True)
async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

Base = declarative_base()

class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True, index=True)
    email = Column(String, unique=True, index=True, nullable=False)
    hashed_password = Column(String, nullable=False)
    is_active = Column(Boolean, default=True)

# CRUD operations
async def get_user(db: AsyncSession, user_id: int):
    return await db.get(User, user_id)

async def create_user(db: AsyncSession, user: UserCreate):
    hashed_password = get_password_hash(user.password)
    db_user = User(email=user.email, hashed_password=hashed_password)
    db.add(db_user)
    await db.commit()
    await db.refresh(db_user)
    return db_user

Request Validation

from pydantic import BaseModel, validator, root_validator
from typing import Optional
from datetime import date

class PostCreate(BaseModel):
    title: str = Field(..., min_length=5, max_length=200)
    content: str = Field(..., min_length=10)
    published: bool = False
    tags: List[str] = Field(default_factory=list, max_items=10)

    @validator('tags')
    def validate_tags(cls, v):
        return [tag.lower().strip() for tag in v]

    @validator('title')
    def validate_title(cls, v):
        if any(word in v.lower() for word in ['spam', 'xxx']):
            raise ValueError('Invalid content')
        return v

Background Tasks

from fastapi import BackgroundTasks

def send_email(email: str, message: str):
    # Send email
    print(f"Sending email to {email}: {message}")

@app.post("/send-notification")
async def send_notification(
    email: str,
    background_tasks: BackgroundTasks
):
    background_tasks.add_task(send_email, email, "Welcome!")
    return {"message": "Notification sent"}

Testing

from fastapi.testclient import TestClient
import pytest

client = TestClient(app)

def test_create_user():
    response = client.post(
        "/users",
        json={"email": "test@example.com", "name": "Test User"}
    )
    assert response.status_code == 201
    assert response.json()["email"] == "test@example.com"

def test_get_user():
    response = client.get("/users/1")
    assert response.status_code == 200
    assert "email" in response.json()

@pytest.mark.asyncio
async def test_async_endpoint():
    async with AsyncClient(app=app, base_url="http://test") as ac:
        response = await ac.get("/users")
    assert response.status_code == 200

Best Practices

  • Use Pydantic models for validation
  • Implement proper error handling
  • Use dependency injection
  • Add rate limiting
  • Enable CORS properly
  • Use async/await for I/O
  • Document with docstrings
  • Add proper logging

Resources