Use when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentation. Trigger terms: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python.
91
86%
Does it follow best practices?
Impact
94%
1.14xAverage score across 6 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that excels across all dimensions. It provides specific concrete actions, comprehensive trigger terms that match natural developer language, explicit 'Use when' and 'Invoke to' guidance, and a clearly defined technology niche. The description is well-structured and concise without unnecessary padding.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, generate OpenAPI documentation. | 3 / 3 |
Completeness | Clearly answers both 'what' (create REST endpoints, define Pydantic models, implement auth flows, etc.) and 'when' (explicit 'Use when building high-performance async Python APIs with FastAPI and Pydantic V2' plus 'Invoke to...' and 'Trigger terms' clauses). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: FastAPI, Pydantic, async Python, Python API, REST API Python, SQLAlchemy async, JWT authentication, OpenAPI, Swagger Python. These are terms developers would naturally use when requesting help with this stack. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche around FastAPI + Pydantic V2 + async Python. The specific technology stack (FastAPI, Pydantic V2, async SQLAlchemy, JWT) makes it very unlikely to conflict with generic Python or web framework skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, well-structured FastAPI skill with excellent actionability through complete, executable code examples and strong progressive disclosure via the reference table. The main weaknesses are moderate verbosity (some sections explain things Claude already knows) and workflow validation steps that could be more concrete with specific commands rather than general guidance.
Suggestions
Remove or trim sections that state things Claude already knows, such as 'don't store passwords in plain text', the Knowledge Reference keyword list, and the 'When to Use This Skill' section that duplicates the frontmatter description.
Make the workflow checkpoint more concrete with specific verification commands, e.g., 'Run `python -c "from app.schemas import UserCreate; UserCreate(email='test@test.com', password='12345678')"` to verify schema validation' rather than the generic 'confirm schemas validate correctly'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary sections like 'When to Use This Skill' (which largely duplicates the frontmatter description), the 'Knowledge Reference' list at the bottom (just keywords Claude already knows), and the 'Output Templates' section which is somewhat generic. The MUST DO/MUST NOT DO lists contain items Claude already knows (e.g., 'don't store passwords in plain text'). | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples across multiple files (schemas, routers, CRUD, security). The JWT authentication snippet, Pydantic V2 models, and async endpoint patterns are concrete and complete with proper imports, type hints, and error handling. | 3 / 3 |
Workflow Clarity | The core workflow lists 5 steps with a checkpoint callout, but the validation steps are somewhat vague ('confirm schemas validate correctly') rather than providing specific commands or checks. The checkpoint is a good addition but lacks concrete verification commands beyond 'run pytest' and 'check /docs'. For a skill involving database operations and authentication, more explicit validation/verification steps would be expected. | 2 / 3 |
Progressive Disclosure | Excellent use of a reference table with clear 'Load When' conditions for six different topics, each pointing to a single-level-deep reference file. The main skill provides a complete minimal example inline while deferring detailed guidance to well-organized reference files. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
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Table of Contents
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