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BD huo15-influencer-video-skill

通过火山方舟Ark API调用Seedance 2.0生成第一人称带货短视频，v2 新增剧本驱动的配音（edge-tts/火山TTS）、背景音乐自动混音、字幕烧录，内置 8 套人设模板（传统女、时尚主播、老中医、厨房主妇、美妆博主、健身教练、户外探店、数码博主）。触发词：生成视频、带货视频、产品视频、拍视频、剧本拍视频。

ClawHub Agent Skills author: Job Zhao v2.0.0 MIT-0 10 files body ≈ 1 952 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationMedia and videoInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
46/100
Unfinished process
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: 10. 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 "displayName"
  • note frontmatter-key unknown frontmatter key "aliases"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1952 tokens
  • 100Running it twice. No mutating operations
  • low 14 top-level sections: this looks like several domains in one skill

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
  • -221 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 161: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (9 code blocks)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This skill appears to do what it advertises: generate short product videos using disclosed external AI/TTS services and local media tools.
LLM: benign (high) · VirusTotal: · 29 May 2026