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AA scraping-youtube-trending-videos

Extracts YouTube trending videos by category and country using apidojo's YouTube Trending Scraper on Apify. Triggers when the user asks to: get trending YouTube videos, scrape what's trending on YouTube today, fetch YouTube trending videos in a specific country, get trending gaming or music videos on YouTube, export the YouTube trending page data, find viral YouTube videos right now, or collect top trending YouTube content by category. Returns video title, URL, view count, like count, channel info, description, and thumbnail per video. Ideal for content researchers, trend analysts, and YouTube marketers.

ClawHub Agent Skills author: API Dojo v1.0.0 MIT-0 2 files body ≈ 666 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Extracts YouTube trending videos by category and country using apidojo's YouTube Trending Scraper on Apify.

As a process A 81/100 · Runs to the end — 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
A
81/100
Runs to the end
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 81/100

    • 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
    • 100Tools and files. No external tools needed
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 666 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)
    • +2Single-language instructions
    • +3Description length 611: 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 straightforward YouTube trending-data scraper that clearly uses Apify and does not show hidden or destructive behavior.
    LLM: benign (high) · VirusTotal: · 3 Sept 2026