AC ai-clipping
AI-powered video highlight extraction that identifies the most engaging moments and generates viral-ready video clips. Ideal for social media content creation, highlight reels, and long-to-short video repurposing. Best for broad requests like "highlights" or "best moments." Export clips with customizable aspect ratios, caption styles, and AI reframing. Supports both online URLs and local files.
As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Exfiltration
net-credential-useSKILL.md:92Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)> - If it has been more than 24 hours but less than 3 days, refresh the `export_link` by running: `curl -s -H "Authorization: Bearer $WAYIN_API_KEY" -H "x-wayinvideo-api-version: v2" "https://wayinvid
quoted
Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 85Steps. 15 steps, 1 vague phrases
- 100Failures and branches. 15 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2184 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (14 tags): a typed call is more reliable
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 397: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 15 items
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.