Content
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a lean but generic scaffold: it sequences a workflow yet provides no executable ffmpeg guidance and never points to the actual composite/normalize scripts that hold the real instructions. It reads as a refactored template rather than skill-specific content.
Suggestions
Replace vague workflow lines with concrete ffmpeg guidance and reference the existing scripts by path (e.g., scripts/composite.py, scripts/normalize_clip.sh) with usage examples.
Add an explicit validation checkpoint with a runnable command (e.g., ffprobe the output, verify dimensions/duration) for the batch compositing operation.
Move the 'Implementation status / Sources / Extraction notes' meta-information out of the body since it does not help execution.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lines are short with no concept over-explanation, but 'Implementation status', 'Sources', and 'Extraction notes' meta-scaffolding plus generic boilerplate sections add tokens that don't aid execution. | 2 / 3 |
Actionability | No concrete ffmpeg commands, code, or examples appear in the body — 'Run the provider, script, or planning workflow described by this skill' describes rather than instructs. | 1 / 3 |
Workflow Clarity | Steps 1–5 are sequenced and Quality Checks exist, but for a batch video-compositing operation the validation is generic ('paths in the manifest are valid') with no real executable checkpoints or feedback loops. | 2 / 3 |
Progressive Disclosure | Sections are organized and bundle scripts exist under scripts/, but the body never signals or links to those files, so navigation to the real executable material is missing. | 2 / 3 |
Total | 7 / 12 Passed |