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BD visual-bug-hunter

视觉 Bug 定位与修复 Skill。当用户描述 GUI App 或 Web App 存在视觉 Bug（按钮点不了、元素重叠、布局错位、样式异常），或 AI 反复修 Bug 但无法自检验证效果，或需要对应用进行 UI 自动化测试时触发。通过截图感知 + UI 交互测试 + 控制台捕获 + 代码精准定位的闭环流程，让 AI 像人一样"眼睛看 + 手点击"来发现和修复 Bug，并以省 token 的最小 Diff 方式生成代码修复方案。不适用于纯后端逻辑 Bug、数据库问题或无 UI 界面的 API 报错。

ClawHub Agent Skills author: yitao2027 v1.1.0 MIT-0 3 files body ≈ 834 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationInfrastructuretype 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
48/100
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: 3. 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 "install_type"
  • note frontmatter-key unknown frontmatter key "env_vars"
  • note frontmatter-key unknown frontmatter key "no_external_credentials"
  • note frontmatter-key unknown frontmatter key "scope"

Process rating: all ten parameters 48/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
  • 100Steps. 17 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 834 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 10 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 254: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 17 items
  • +4Has examples (3 code blocks)

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

External checks

ClawHub: clean
This is an instruction-only UI debugging skill whose screenshot, click-testing, and code-fix behavior is disclosed and aligned with finding visual bugs.
LLM: benign (high) · VirusTotal: · 29 May 2026