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AB scraping-youtube-channel-info

Extracts YouTube channel metadata, subscriber counts, video counts, and descriptions using apidojo's YouTube Channel Scraper on Apify. Triggers when the user asks to: get YouTube channel information, scrape YouTube channel stats, fetch subscriber count for a YouTube channel, export channel metadata in bulk, find YouTube channels by keyword, get channel descriptions and tags, check if a YouTube channel is verified, or research YouTube channels by niche. Returns channel name, subscriber count, video count, description, verification status, and keywords per channel. Ideal for influencer researchers, competitive analysts, and YouTube marketers.

ClawHub Agent Skills author: API Dojo v1.0.0 MIT-0 2 files body ≈ 707 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Extracts YouTube channel metadata, subscriber counts, video counts, and descriptions using apidojo's YouTube Channel Scraper on Apify.

As a process B 78/100 · Nearly there — weak spots: progress reporting

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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Failures and branches w 10
50
Result and completion w 14
60
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 78/100

    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 4 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 707 tokens
    • 100Running it twice. Mutating operations check current state

    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 648: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 4 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This skill is a disclosed Apify integration for collecting public YouTube channel metadata, with no hidden local code, persistence, or destructive behavior found.
    LLM: benign (high) · VirusTotal: · 3 Sept 2026