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BC faceless-video-automation

AI无脸视频频道自动化系统。覆盖B站、YouTube、抖音等平台 的无脸/不露脸视频创作全流程：爆款选题挖掘→AI脚本生成→ AI配音（多种声线）→画面素材匹配（免费图库/AI生成）→ 字幕生成→多平台适配发布。支持批量生产，日更5-10条视频。 触发词：无脸视频、不露脸、AI视频、频道自动化、短视频批量。

ClawHub Agent Skills author: qq435912743 v1.0.0 MIT-0 4 files body ≈ 884 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

AI无脸视频频道自动化系统。覆盖B站、YouTube、抖音等平台 的无脸/不露脸视频创作全流程：爆款选题挖掘→AI脚本生成→ AI配音（多种声线）→画面素材匹配（免费图库/AI生成）→ 字幕生成→多平台适配发布。支持批量生产，日更5-10条视频。 触发词：无脸视频、不露脸、AI视频、频道自动化、短视频批量。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureYouTubeMedia 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
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "visibility"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 884 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (3 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
The skill’s video workflow is mostly coherent, but it also adds an automatic generic learning system that stores user preferences, error notes, and usage history locally and can steer future behavior.
LLM: suspicious (high) · 14 Aug 2026