jbvc/fastapi-pro
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.
55
55%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description effectively identifies its technology stack and includes an explicit 'Use when' clause with relevant trigger scenarios. However, it relies on buzzword-heavy language ('Master', 'high-performance', 'modern') rather than listing concrete actions, and some terms like 'API architecture' and 'microservices' are broad enough to risk overlap with other skills. Replacing vague verbs with specific capabilities would strengthen it.
Suggestions
Replace vague terms like 'Master' and 'modern Python async patterns' with concrete actions such as 'define route handlers', 'configure dependency injection', 'set up async database sessions', 'implement background tasks'.
Narrow the broader trigger terms: instead of 'API architecture' or 'microservices' generically, specify 'FastAPI microservices' or 'FastAPI project structure' to reduce conflict risk with other API/architecture skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (FastAPI, async APIs) and some technologies (SQLAlchemy 2.0, Pydantic V2, WebSockets, microservices), but uses vague terms like 'Master' and 'modern Python async patterns' rather than listing concrete actions like 'create endpoints', 'define schemas', 'configure middleware'. | 2 / 3 |
Completeness | Answers both 'what' (build async APIs with FastAPI/SQLAlchemy/Pydantic, microservices, WebSockets, async patterns) and 'when' with an explicit trigger clause ('Use PROACTIVELY for FastAPI development, async optimization, or API architecture'). | 3 / 3 |
Trigger Term Quality | Includes strong natural keywords users would say: 'FastAPI', 'async', 'APIs', 'SQLAlchemy', 'Pydantic', 'microservices', 'WebSockets', 'API architecture'. These cover a good range of terms a developer would naturally use when seeking help with FastAPI development. | 3 / 3 |
Distinctiveness Conflict Risk | FastAPI is a fairly specific niche, but terms like 'async optimization', 'API architecture', and 'microservices' are broad enough to potentially overlap with general Python skills, Django/Flask API skills, or generic architecture skills. The FastAPI-specific terms help but the broader terms create some conflict risk. | 2 / 3 |
Total | 10 / 12 Passed |
Implementation
7%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads as a persona/capability description rather than an actionable skill file. It consists almost entirely of bullet-point lists enumerating topics Claude already knows about (FastAPI features, testing tools, deployment strategies) without providing any concrete code, commands, or specific instructions. The content would be far more effective if it were reduced to a concise overview with executable examples and clear references to detailed resources.
Suggestions
Replace the extensive capability lists with 2-3 concrete, executable code examples showing key patterns (e.g., async endpoint with SQLAlchemy 2.0, JWT auth setup, WebSocket handler).
Remove the 'Capabilities', 'Behavioral Traits', 'Knowledge Base', and 'Example Interactions' sections entirely — these describe things Claude already knows and waste token budget.
Add a concrete workflow with validation steps for common tasks like 'Setting up a new FastAPI project' or 'Adding a new endpoint', including specific commands and verification checks.
Ensure the referenced 'resources/implementation-playbook.md' exists and is clearly described, and move any essential patterns/code into the main skill file as a quick-start section.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive lists of capabilities, behavioral traits, knowledge bases, and example interactions that Claude already knows. The content reads like a persona description or resume rather than actionable instructions. Most of the bullet points describe concepts Claude is already familiar with (e.g., 'RESTful API design principles', 'pytest with pytest-asyncio for async tests'). | 1 / 3 |
Actionability | Contains zero executable code, no concrete commands, no specific examples, and no copy-paste ready snippets. The entire skill is abstract descriptions and capability lists. The only actionable reference is 'open resources/implementation-playbook.md' but the skill itself provides no concrete guidance. | 1 / 3 |
Workflow Clarity | The 'Response Approach' section lists 8 high-level steps but they are vague and lack any validation checkpoints, specific commands, or feedback loops. Steps like 'Analyze requirements for async opportunities' and 'Consider deployment and scaling strategies' are abstract directives, not actionable workflow steps. | 1 / 3 |
Progressive Disclosure | There is a reference to 'resources/implementation-playbook.md' for detailed examples, which is good progressive disclosure. However, the main file itself is a monolithic wall of bullet-pointed lists that could be dramatically condensed, and it's unclear what the referenced file actually contains or how it's organized. | 2 / 3 |
Total | 5 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
Reviewed
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