AC video-editor
Edits existing videos using ffmpeg and Python. Use ALWAYS when the user wants to edit a video, cut a video, join videos, add subtitles, add music, remove audio, resize a video, convert format, compress a video, extract audio, extract frames, add a watermark, make a timelapse, slow motion, speed up, reverse, add a transition, crop, rotate, adjust brightness/contrast, color grading, generate a GIF, generate a thumbnail, or any manipulation of an existing video file. Also activates when the user mentions: ffmpeg, moviepy, cutting a clip, joining clips, captioning a video, or processing a video.
As a process C 51/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, consistency
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 51/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (video-editor) differs from the folder (eb-video-editor)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 85Steps. 25 steps, 1 vague phrases
- 100Execution cost. Instruction body is 3973 tokens
- low 12 top-level sections: this looks like several domains in one skill
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)
- +1No license
- +2Single-language instructions
- +3Description length 598: enough signal without eating the budget
- +4Structure: 50 headings
- +3Step-by-step instructions: 25 items
- +3Output format is stated explicitly
- +4Has examples (37 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.