SKILLEMALL.ai
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AC video-analyzer

Download, transcribe, and analyze videos from YouTube, X/Twitter, and TikTok with local Whisper processing. Perfect for extracting TL;DRs, timestamps, and actionable insights. Use when asked to transcribe a video, summarize a YouTube video, extract key points from a podcast or talk, analyze what someone said in a video, get timestamps from a long video, or when the user shares any YouTube, X/Twitter, or TikTok video URL and wants to know what's in it.

ClawHub Agent Skills author: minilozio v1.0.1 4 files body ≈ 575 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerYouTubeMedia and videotype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (video-analyzer) differs from the folder (video-analyzer-skill)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 9 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Execution cost. Instruction body is 575 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 455: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.

    External checks

    ClawHub: suspicious
    This video skill mostly does what it claims, but its script can build unsafe shell commands from user-provided values, which could let a crafted request run unintended local commands.
    LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026