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AF wjs-dubbing-video

Use when the user has a video + a target-language SRT and wants the video to actually speak that language — generates a time-aligned TTS voice dub. Routes by voice ID — Volcano (豆包) TTS for Chinese, edge-tts neural for any language. Defaults to one voice (single-speaker); opt-in multi-speaker via visual diarization. Outputs `*_<lang>_dub.mp4` with the dub audio in place of the original. Final mixing (audio bed + burn-in) is handed off to `/wjs-burning-subtitles`. Triggers — "配音", "中文配音", "Chinese dub", "voice over this", "dub the video", "TTS this SRT", "different voice for each speaker".

ClawHub Agent Skills author: Jian Shuo Wang v0.1.0 MIT-0 4 files body ≈ 5 127 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Routes by voice ID — Volcano (豆包) TTS for Chinese, edge-tts neural for any language.

As a process F 51/100 · Will not run — References files that are not bundled: scripts/sample_voices.py

GeneratorMedia 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
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
Run on models
none yet
Process rating
F
51/100
Will not run
References files that are not bundled: scripts/sample_voices.py
Tools and files w 18
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. The text references files that are not there: add them or drop the references.
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 Exfiltration read-dotenv SKILL.md:57
    Reads a .env file
    set -a; source ~/code/.env; set +a

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5127 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/sample_voices.py

Process rating: all ten parameters 51/100

Will not run. References files that are not bundled: scripts/sample_voices.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/sample_voices.py
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 3 mutating operations with no state check
  • 60Steps. 51 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 5127 tokens
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 14 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 595: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 51 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
The skill’s video dubbing purpose is coherent, but it uses broad local secret loading and sends subtitle text to a remote TTS service without clear user-facing consent or scoping.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026