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AB Video Analyzer Skill

Analyze any video by dropping a URL. Works with TikTok, YouTube, Instagram, Twitter/X, and 1000+ other sites. Transcribes the audio locally and answers any question about the content.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 415 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze any video by dropping a URL.

As a process B 66/100 · Nearly there — weak spots: result and completion, consistency, progress reporting

AnalyzerYouTubeMedia and videoMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

    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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 66/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (Video Analyzer Skill) differs from the folder (tiktok-video-analyzer)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 18 steps, 3 vague phrases
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 3 branches, has a failure section
    • 100Execution cost. Instruction body is 1415 tokens
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 183: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.