AC video-metadata-analyzer
Video content analysis pipeline — extract frames, transcribe audio, run LLM visual+audio analysis, synthesize structured Bilibili publish metadata (title, intro, tags, category, cover suggestion). Use when user says '生成投稿元数据', '视频元数据分析', or explicitly requests this skill. ⚠️ API modes send video frames and audio to external LLM providers — see Privacy section.
As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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
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low Secrets in code
secret-high-entropy-tokenREADME.md:11High-entropy token-like string (may be an id, hash or a credential)[](https://clawhub.ai/CyberKurry/video-metadata-analyzer) [ - note
frontmatter-keyunknown frontmatter key "privacy_notice" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 59/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 60Consistency. The Hermes dialect needs category and tags
- 100Steps. 18 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 1728 tokens
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)
- +2Single-language instructions
- +3Description length 362: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 18 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 5 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.