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BD ecommerce-livestream-overlay-generator

电商直播贴片生成器 / 电商直播视觉包装工具。 适用于淘宝直播、抖音直播带货、拼多多直播、快手直播等电商直播场景。 输入品牌、产品、促销信息或产品照片，自动生成完整电商直播视觉包装： 背景、标题栏、主播形象、价格卡、产品货架、福利条、促销贴片。 支持绿幕抠图、自动合成预览、打包交付。 E-commerce livestream overlay generator for Taobao, Douyin, PDD, Kuaishou streaming. Generate background, title banner, host persona, pricing card, product shelf, benefits bar. Supports green screen removal, auto-composite preview, and packaged delivery.

ClawHub Agent Skills author: gaoyuji12138 v2.0.0 MIT-0 6 files body ≈ 2 560 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

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

GeneratorInfrastructuretype 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%
69
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: 6. 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 "homepage"

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. 31 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2560 tokens
  • 100Running it twice. No mutating operations
  • low 11 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
  • -229 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 397: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (17 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill mostly supports ecommerce image generation, but its privacy, local-file, review-avoidance, and capability-metadata mismatches should be reviewed before install.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026