fastapi-ml-endpoint
Fastapi Ml Endpoint - Auto-activating skill for ML Deployment. Triggers on: fastapi ml endpoint, fastapi ml endpoint Part of the ML Deployment skill category.
39
Quality
11%
Does it follow best practices?
Impact
88%
1.02xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/08-ml-deployment/fastapi-ml-endpoint/SKILL.mdQuality
Discovery
22%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 description is severely underdeveloped, functioning more as a label than a useful skill description. It lacks any concrete actions, meaningful trigger terms beyond the skill name itself, and provides no guidance on when Claude should use it. The duplicate trigger terms and boilerplate structure suggest this was auto-generated without customization.
Suggestions
Add specific actions the skill performs, e.g., 'Creates FastAPI endpoints for serving ML models, configures request/response schemas, handles model loading and inference'
Add a 'Use when...' clause with natural trigger terms like 'deploy model as API', 'serve predictions', 'create inference endpoint', 'REST API for machine learning'
Include common file types or frameworks users might mention, such as '.py', 'uvicorn', 'pydantic', 'scikit-learn', 'pytorch', 'tensorflow'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only mentions 'fastapi ml endpoint' without describing any concrete actions. There are no verbs or specific capabilities listed - it doesn't explain what the skill actually does (e.g., create endpoints, deploy models, configure routes). | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming the domain, and there is no explicit 'Use when...' clause or equivalent guidance for when Claude should select this skill. Only mentions it's 'auto-activating' without explaining the triggers meaningfully. | 1 / 3 |
Trigger Term Quality | Contains 'fastapi ml endpoint' which is a relevant technical term, but the trigger terms are duplicated and missing common variations users might say like 'deploy model', 'API endpoint', 'serve model', 'REST API', or 'machine learning API'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'fastapi' and 'ml endpoint' provides some specificity that distinguishes it from generic API or ML skills, but without concrete actions described, it could still overlap with general FastAPI skills or other ML deployment tools. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is a hollow template with no actual instructional content. It describes what a skill should do without providing any concrete guidance, code examples, or actionable steps for creating FastAPI ML endpoints. The entire content could be replaced with actual implementation details.
Suggestions
Add executable Python code showing a minimal FastAPI ML endpoint with model loading and prediction route
Include a concrete workflow: 1) Load model, 2) Define Pydantic schemas for input/output, 3) Create prediction endpoint, 4) Add health checks
Provide specific examples of request/response formats and error handling patterns for ML inference
Replace generic capability descriptions with actual best practices like async inference, batching, or model versioning
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing specific about FastAPI ML endpoints. Phrases like 'provides automated assistance' and 'follows industry best practices' are filler that Claude already understands. | 1 / 3 |
Actionability | No concrete code, commands, or specific guidance is provided. The skill describes what it does abstractly ('provides step-by-step guidance') but never actually provides any guidance, examples, or executable content. | 1 / 3 |
Workflow Clarity | No workflow, steps, or process is defined. The content only describes trigger conditions and vague capabilities without any actual sequence of actions for implementing a FastAPI ML endpoint. | 1 / 3 |
Progressive Disclosure | No structure for progressive disclosure exists. There are no references to detailed documentation, no links to examples, and no organization beyond generic section headers with placeholder content. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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