flask-ml-api-creator
Flask Ml Api Creator - Auto-activating skill for ML Deployment. Triggers on: flask ml api creator, flask ml api creator Part of the ML Deployment skill category.
35
3%
Does it follow best practices?
Impact
94%
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/flask-ml-api-creator/SKILL.mdQuality
Discovery
7%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 extremely weak, consisting essentially of just the skill name repeated as trigger terms with a category label. It provides no concrete actions, no natural trigger keywords users would say, and no guidance on when Claude should select this skill. It reads like auto-generated boilerplate rather than a useful skill description.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates Flask REST API endpoints for serving ML model predictions, generates model loading code, sets up request/response schemas, and configures deployment configurations.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user wants to deploy a machine learning model as an API, create a Flask endpoint for predictions, serve an ML model, or build a REST API for inference.'
Include natural keyword variations users might say, such as 'deploy model', 'model serving', 'prediction API', 'Flask API', 'REST endpoint', 'inference endpoint', 'ML microservice'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names a domain ('ML Deployment') and mentions 'Flask Ml Api Creator' but does not describe any concrete actions. There are no specific capabilities listed like 'creates REST endpoints', 'deploys models', 'generates Flask boilerplate', etc. | 1 / 3 |
Completeness | The description fails to answer both 'what does this do' and 'when should Claude use it'. There is no explanation of capabilities and no explicit 'Use when...' clause with trigger guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('flask ml api creator, flask ml api creator'). There are no natural user keywords like 'deploy model', 'REST API', 'serve predictions', 'Flask endpoint', 'model serving', or 'ML API'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Flask' and 'ML API' provides some specificity that distinguishes it from generic coding skills, but the lack of concrete actions means it could overlap with any Flask or ML deployment related skill. | 2 / 3 |
Total | 5 / 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 an empty shell with no actual instructional content. It consists entirely of auto-generated boilerplate that describes what the skill would do without providing any concrete guidance, code, commands, or workflows for creating a Flask ML API. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable Flask code showing how to create an ML API endpoint (e.g., model loading, prediction route, request/response handling with actual Python code).
Include a clear multi-step workflow: project setup, model serialization, Flask app creation, endpoint definition, testing, and deployment—with validation checkpoints at each stage.
Remove all meta-description sections (Purpose, When to Use, Example Triggers, Capabilities) and replace with actionable content like a quick-start example and production considerations (error handling, input validation, health checks).
Add references to separate files for advanced topics like Docker deployment, model versioning, monitoring setup, and load testing rather than listing them as vague tags.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and boilerplate. It explains nothing Claude doesn't already know, repeats 'flask ml api creator' excessively, and provides zero actual technical content. Every token is wasted. | 1 / 3 |
Actionability | There is no concrete code, no commands, no executable guidance whatsoever. The skill describes what it could do in abstract terms ('provides step-by-step guidance') without actually providing any guidance. | 1 / 3 |
Workflow Clarity | No workflow, no steps, no sequence, no validation checkpoints. The content contains no actual process for creating a Flask ML API—just vague claims about capabilities. | 1 / 3 |
Progressive Disclosure | No references to external files, no structured content hierarchy, and no meaningful organization. The sections that exist (Purpose, When to Use, Capabilities, Example Triggers) are all meta-descriptions with no substantive 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 | |
- Commit
87f14eb
- Reviewed
Table of Contents
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