BC video-editor
Video editing operations using ffmpeg. Use when user needs to: (1) Cut/trim video clips, (2) Merge/join multiple videos, (3) Convert video formats, (4) Extract audio from video, (5) Add subtitles or text overlays, (6) Adjust video speed, (7) Resize/crop video dimensions, (8) Apply filters or effects. Supports common formats like MP4, MOV, AVI, MKV, WebM.
Video editing operations using ffmpeg.
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting
How to improve
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Video editing operations using ffmpeg. Use when user needs to: (1)… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 4 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 942 tokens
- 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
- +1No license
- +2Single-language instructions
- +3Description length 356: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 4 items
- +4Has examples (17 code blocks)
- +3All 8 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.