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AB yt_transcript

Extract YouTube video transcripts from existing captions (manual or auto-generated) using yt-dlp, with optional timestamps and local SQLite caching. Use when the user asks for a YouTube transcript, captions, subtitles, or wants to turn a YouTube link into text for summarization/search.

ClawHub Agent Skills author: mpbshhx v1.1.0 MIT-0 3 files body ≈ 516 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 69/100 · Nearly there — weak spots: consistency, progress reporting

GeneratorYouTubeMedia 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
B
69/100
Nearly there
Progress reporting w 2
0
Consistency w 8
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-password-literal scripts/yt_transcript.py:705
      Hard-coded password / key literal (may be an example)
      api_key = m2.group(1) if m2 else None

    Files scanned: 3. 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 69/100

    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (yt_transcript) differs from the folder (yt-transcript-yt-dlp)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 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
    • 100Steps. 6 steps
    • 100Execution cost. Instruction body is 516 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 286: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 6 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: 85.

    External checks

    ClawHub: clean
    This skill appears to be a coherent YouTube transcript helper, with disclosed local caching and optional YouTube cookie use.
    LLM: benign (high) · VirusTotal: · 29 May 2026