Content
86%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A lean, highly actionable skill body organized into clean sections with executable commands and appropriate offloading of detail to a one-level-deep reference. The only weaknesses are minor conciseness redundancy and the absence of explicit verification guidance for batch frame extraction.
Suggestions
Drop the trailing 'Use the Read tool on frame image paths...' line since the same guidance is already in the embedded JSON 'note' field, or vice versa.
Add a brief verification cue for the batch operation, e.g. 'If frame_count is 0 in the output, re-run with -m interval' to give the workflow an explicit checkpoint.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Efficient, table-and-code driven body that assumes Claude's competence (no explaining what ffmpeg/video is); minor redundancy from the inline JSON example duplicating references/output-format.md and the 'Use the Read tool' line echoing the embedded 'note' field. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready commands covering the common cases (default, keyframe, interval, max-frames, whisper-model, no-transcribe, quiet, output) plus a complete CLI options table and a real referenced script. | 5 / 5 |
Workflow Clarity | The single-command workflow is unambiguous and the JSON output acts as an implicit checkpoint (frame_count, frames array), with a built-in scene→interval fallback noted; lacks explicit verification guidance for the batch frame-extraction step, so it stays just below 5. | 4 / 5 |
Progressive Disclosure | Clear overview with well-signaled one-level-deep references — the full output schema lives in references/output-format.md (a real bundle file) while only a compact example is inlined, and the referenced script path maps to scripts/understand_video.py. | 5 / 5 |
Total | 18 / 20 Passed |