BF bilibili-video-extractor
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As a process F 35/100 · Will not run — References files that are not bundled: references/core_workflow.md
ProcedureInfrastructureMedia and videotype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
For the model run — optional
- 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: 12. 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: Implicit map keys need to be followed by map values at line 4, column 1: version:1.0.0 ^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: references/core_workflow.md
Process rating: all ten parameters 35/100
Will not run. References files that are not bundled: references/core_workflow.md
- 0Tools and files. 1 referenced file(s) missing: references/core_workflow.md
- 0Result and completion. Does not say what the result is
- 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
- 20When it triggers. No condition that starts the skill
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 160 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
- +4No input/output examples
- -213 emoji in the instructions: noise for the model
- -45 reference files, but SKILL.md never points to them: the model will not open them
- -33 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 250: enough signal without eating the budget
- +4Structure: 4 headings
- +3Step-by-step instructions: 8 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 43.
External checks
ClawHub: suspicious
The skill mostly matches its Bilibili video-analysis purpose, but it ships and preserves raw unmoderated social-media text that may include unsafe content and lacks clear moderation or retention controls.
LLM: suspicious (high) · VirusTotal: · 5 Jun 2026