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 essentially a placeholder that repeats the skill name without providing any meaningful information about what the skill does or when it should be used. It lacks concrete actions, natural trigger terms, and explicit usage guidance, making it nearly useless for skill selection among multiple options.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates Flask-based REST APIs for serving machine learning models, generates prediction endpoints, handles model loading and inference pipelines, and scaffolds deployment configurations.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user wants to deploy an ML model as a Flask API, create prediction endpoints, serve a trained model, build an inference REST service, or wrap a scikit-learn/PyTorch/TensorFlow model in a web API.'
Replace the duplicated trigger term with diverse natural keywords users would actually say, such as 'deploy model', 'serve predictions', 'model API', 'Flask endpoint', 'inference service', 'REST API for ML'.
| 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 clearly answer 'what does this do' beyond the name itself, and there is no 'when should Claude use it' clause. The 'Triggers on' line just repeats the skill name and provides no meaningful guidance. | 1 / 3 |
Trigger Term Quality | The trigger terms are just the skill name repeated twice ('flask ml api creator'). There are no natural user keywords like 'deploy model', 'REST API', 'serve predictions', 'Flask endpoint', 'model serving', or 'inference API'. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Flask' and 'ML API' provides some specificity that distinguishes it from generic coding or deployment skills, but the lack of concrete actions or detailed triggers means it could still overlap with other ML deployment or API creation skills. | 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 examples, or workflows for creating Flask ML APIs. 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 integration, endpoint creation, testing, and deployment with validation checkpoints at each stage.
Remove all meta-description sections ('When to Use', 'Example Triggers', 'Capabilities') and replace with actual technical content like production configuration examples, error handling patterns, and model serialization approaches.
Add references to supplementary files for advanced topics (e.g., MONITORING.md for production monitoring, SCALING.md for scaling strategies) rather than just listing 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 merely lists vague capabilities like 'generates production-ready code' without showing any actual process. | 1 / 3 |
Progressive Disclosure | No references to external files, no structured content hierarchy, and no meaningful organization. The sections present are all meta-descriptions about the skill rather than actual instructional 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
3076d78
- Reviewed
Table of Contents
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