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AB yt-assemblyai-monitor

YouTube channel monitor and video transcription using AssemblyAI cloud API. Pure Python + requests only — no ffmpeg, no Whisper, no extra tools needed. Monitors YouTube channels for new videos, extracts audio URLs via innertube API, submits to AssemblyAI for cloud transcription, and returns text + AI summary. Works on Mac, Linux, Windows. Only dependency: requests (usually pre-installed). Use when: user asks to monitor YouTube channels, transcribe YouTube videos, summarize video content, or set up YouTube content monitoring.

ClawHub Agent Skills author: 大佬的鼠DonRat v1.0.1 MIT-0 4 files body ≈ 530 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 75/100 · Nearly there — weak spots: when it triggers, progress reporting

IntegrationYouTubeMedia 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
92/100
safety, quality, tests
Safety 60%
94
Quality 40%
90
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
55
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Secrets in code medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.

For the author

The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.

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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • medium Secrets in code secret-google-key scripts/monitor.py:74
      Google API key (quoted — discussed, not commanded)
      key = config.get("INNERTUBE_API_KEY", "AIza…cW8")
      quoted
    • low Secrets in code secret-high-entropy-token scripts/monitor.py:74
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      key = config.get("INNERTUBE_API_KEY", "AIza…cW8")
      quoted

    Files scanned: 4. 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 75/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 530 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 530: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 13 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This skill does what it says: it monitors or transcribes video content by sending audio URLs to AssemblyAI and saving results locally.
    LLM: benign (high) · VirusTotal: · 29 May 2026