songsee
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
86
Quality
83%
Does it follow best practices?
Impact
96%
19.20xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
67%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 effectively communicates specific capabilities with a named tool, making it distinctive and clear about what it does. However, it lacks explicit trigger guidance ('Use when...') and could benefit from additional natural keywords users might use when requesting audio visualization tasks.
Suggestions
Add a 'Use when...' clause with trigger scenarios like 'Use when the user wants to visualize audio, create spectrograms, or analyze sound frequencies'
Include additional natural trigger terms users might say: 'audio analysis', 'frequency visualization', 'waveform', 'sound analysis', '.wav', '.mp3'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists specific concrete actions: 'Generate spectrograms and feature-panel visualizations from audio' - these are distinct, technical but understandable capabilities with a named tool (songsee CLI). | 3 / 3 |
Completeness | Clearly answers 'what' (generate spectrograms and visualizations from audio using songsee CLI), but lacks an explicit 'Use when...' clause to guide when Claude should select this skill. | 2 / 3 |
Trigger Term Quality | Includes relevant terms like 'spectrograms', 'audio', and 'visualizations', but misses common variations users might say like 'audio analysis', 'waveform', 'frequency plot', or 'sound visualization'. | 2 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with specific domain (audio spectrograms), specific tool (songsee CLI), and specific outputs (spectrograms, feature-panel visualizations) - unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Excellent skill content that exemplifies token efficiency and actionability. The quick start examples cover the main use cases with executable commands, the flag reference is comprehensive yet concise, and the notes section adds only essential context about format support. This is a model example of a well-written simple skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean and efficient. No unnecessary explanations of what spectrograms are or how audio processing works. Every line provides actionable information Claude needs. | 3 / 3 |
Actionability | Provides concrete, copy-paste ready commands covering common use cases (basic, multi-panel, time slice, stdin). Flag documentation is specific and complete. | 3 / 3 |
Workflow Clarity | This is a simple, single-purpose CLI tool skill. The commands are unambiguous and self-contained. No multi-step workflows requiring validation checkpoints. | 3 / 3 |
Progressive Disclosure | For a simple CLI skill under 50 lines, the organization is appropriate. Quick start examples first, then flags reference, then notes. No need for external file references. | 3 / 3 |
Total | 12 / 12 Passed |
Validation
72%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 8 / 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 |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 8 / 11 Passed | |
- Repository
- trpc-group/trpc-agent-go
- Commit
50ef2f3
- Reviewed
Table of Contents
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