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AB youtube-cn-subtitle-burnin

Create Simplified Chinese or Chinese-English bilingual hard-subtitled videos from YouTube links or existing video/subtitle files. Use when the user asks to add/burn Chinese subtitles, translate YouTube videos, produce Chinese or bilingual SRT/ASS/MP4 outputs, download or prepare YouTube thumbnails/covers, extract or translate YouTube descriptions, add Chinese-subtitle labels or author information to covers, review subtitle readability, fix subtitle timing/splitting/size/occlusion problems, or incorporate user subtitle feedback into the workflow.

ClawHub Agent Skills author: woodenxyz v0.1.0 MIT-0 28 files · 22 scripts body ≈ 3 067 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Create Simplified Chinese or Chinese-English bilingual hard-subtitled videos from YouTube links or existing video/subtitle files.

As a process B 78/100 · Nearly there — weak spots: inputs and preconditions, 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
78/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
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: 28. 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

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 54 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3067 tokens
    • 100Running it twice. Mutating operations check current state
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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 551: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 54 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 22 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    The skill mostly matches its YouTube subtitle purpose, but it can write raw user feedback into its own future instructions and repackage itself, which needs human review before installation.
    LLM: suspicious (high) · VirusTotal: · 16 Sept 2026