developing-genkit-python
Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.
71
88%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
89%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 solid description with an explicit 'Use when' clause and good trigger term coverage spanning both development and debugging scenarios. Its main weakness is that the 'what' portion is somewhat high-level—it could benefit from listing more specific concrete actions (e.g., creating flows, defining tools, configuring model plugins) rather than just 'develop AI-powered applications'.
Suggestions
Add more specific concrete actions to the 'what' portion, e.g., 'Create flows, define tools, configure model plugins, and build AI agents using Genkit in Python' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (AI-powered applications using Genkit in Python) and a few actions/concepts (agents, flows, tools), but doesn't list multiple concrete actions like 'create flows', 'define tools', 'configure models', or 'debug deployment issues'. | 2 / 3 |
Completeness | Clearly answers both 'what' (develop AI-powered applications using Genkit in Python) and 'when' (explicit 'Use when' clause covering asking about Genkit, AI agents, flows, tools, errors, import issues, or API problems). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would actually say: 'Genkit', 'AI agents', 'flows', 'tools in Python', 'Genkit errors', 'import issues', 'API problems'. These cover both feature-building and troubleshooting scenarios well. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of 'Genkit' and 'Python' creates a very distinct niche. Terms like 'Genkit errors' and 'Genkit flows' are highly specific and unlikely to conflict with other AI framework skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill that is concise, actionable, and well-organized. It provides a complete executable example, avoids explaining concepts Claude already knows, and uses progressive disclosure effectively with clearly signaled references. The main weakness is the development workflow lacking explicit validation checkpoints between steps, though the error-handling guidance partially compensates.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It avoids explaining what Genkit is, what AI agents are, or how Python async works. Every section earns its place — prerequisites, a minimal hello world, critical warnings, workflow steps, and reference links. No padding or unnecessary context. | 3 / 3 |
Actionability | The Hello World example is fully executable and copy-paste ready. Prerequisites give specific commands (`npm install -g genkit-cli`, `uv`). The development workflow provides concrete model ID formats, specific environment variable names, and exact entry point patterns. Guidance is specific rather than abstract. | 3 / 3 |
Workflow Clarity | The Development Workflow section provides a numbered sequence with clear steps, but it lacks explicit validation checkpoints or feedback loops. Step 5 says 'on errors, check Common Errors' but there's no validate-then-proceed pattern. For a workflow involving code generation and server startup, a more explicit 'verify it works before proceeding' checkpoint would strengthen this. | 2 / 3 |
Progressive Disclosure | Excellent structure: a concise overview with a hello world example, then well-signaled one-level-deep references to setup, examples, common errors, FastAPI, dotprompt, evals, and dev workflow. References are clearly labeled with descriptive summaries. Navigation is easy and intuitive. | 3 / 3 |
Total | 11 / 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 |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
metadata_field | 'metadata' should map string keys to string values | Warning |
Total | 9 / 11 Passed | |
- Repository
- firebase/agent-skills
- Commit
c3bb9d5
- Reviewed
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