BD video-editing
Automated video editing skill for talk/vlog/standup videos. Use when: cutting video, splitting video into sentences, merging video clips, extracting audio, transcribing speech, auto-editing oral presentation videos, combining selected sentence clips into a final video, generating video cover/thumbnail with title, B-roll cutaway editing, persistent video overlay/watermark, blinking REC indicator, ending title cards, multi-source audio mixing, generating voiceover videos with Remotion (audio-only to video with animated visuals/subtitles). Requires ffmpeg and whisper. Remotion workflow additionally requires Node.js and npm.
As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, failures and branches
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 29. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6069 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 47/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (video-editing) differs from the folder (auto-video-editor)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6069 tokens
- 100Steps. 188 steps
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -31 of 13 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 628: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 188 items
- +4Has examples (40 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.