Conciseness

The body is lean — a commands block, two compact tables, and short usage notes with no explanations of concepts Claude already knows. It falls short of anchor 5 because the 17-line inline JSON output example (lines 72-88) duplicates content already covered by 'references/output-format.md', which the same section points to and could be trimmed to a minimal skeleton.

Actionability

Every command is copy-paste ready and covers the common cases — default run, all three extraction modes, frame limits, whisper model size, skipping transcription, quiet mode, and file output — plus a complete CLI options table and prerequisites with install commands. This matches anchor 5's 'fully executable; specific examples cover the common cases'.

Workflow Clarity

This is a simple, single-action skill (run the script) where the single action is unambiguous, meeting the simple-skill exception in the scoring notes. The flow is complete: prerequisites with install commands, mode selection with best-for guidance, an automatic scene-to-interval fallback documented, and an explicit post-run step ('Use the Read tool on frame image paths to visually inspect extracted frames').

Progressive Disclosure

The body is a clear overview with one well-signaled, one-level-deep reference ('references/output-format.md', which exists and contains no nested references), and the implementation script lives in scripts/. Content is appropriately split — the full output schema is in the reference while only a sample sits inline — making navigation easy, matching anchor 5.

Specificity

The description names concrete actions with specific tools — 'using ffmpeg frame extraction and Whisper transcription', 'Extracting key frames for visual analysis', 'Transcribing video audio locally' — going beyond the anchor-3 example of 1-2 actions. It falls short of anchor 5 because it doesn't comprehensively cover capabilities like scene detection or different extraction modes, leaving minor gaps in coverage.

Completeness

It explicitly answers both questions: 'what' via 'Understand video content locally using ffmpeg frame extraction and Whisper transcription' and 'when' via a numbered 'Use when:' list with four concrete trigger phrases. This matches the anchor-5 good example's structure of clear what plus explicit when clauses, and clearly exceeds anchor 4 (where the 'when' could be more explicit).

Trigger Term Quality

Natural trigger phrases like 'Understanding what a video contains', 'Transcribing video audio', and 'key frames' would match how users phrase these requests. It falls short of anchor 5's comprehensive coverage because it omits common synonyms and file extensions (e.g., .mp4, .mov, 'captions', 'video-to-text').

Distinctiveness Conflict Risk

Video understanding via ffmpeg/Whisper is a clear niche with distinct triggers ('video content', 'video audio', 'key frames'), occupying minimal overlap with adjacent skills like image or audio-only processing. The 'No API keys needed' framing further distinguishes it from cloud-based transcription skills.