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BC qa-req-deconstruction

将模糊的需求描述系统化拆分为输入、操作、状态、输出、规则五个可测试维度，同时挖掘显性需求之外的那些"没写出来但必须满足"的隐性需求和衍生需求。当用户的需求描述只有一两句话、或者看起来功能很简单但你可能遗漏了什么的时候，一定要用此技能做深度解构。适用于任何测试任务的第二步骤——无论需求文档有多详细，解构之后总能发现盲区。 本技能属于 QA Test Skills 技能集（49 个技能之一），完整工作流体验需安装全套：npx skills add Kokxi/qa-test-skills

ClawHub Agent Skills author: kokxi v1.7.6 MIT-0 2 files body ≈ 1 057 tokens Open the sourceclawhub.ai↗ analyzed 21 h ago

将模糊的需求描述系统化拆分为输入、操作、状态、输出、规则五个可测试维度，同时挖掘显性需求之外的那些"没写出来但必须满足"的隐性需求和衍生需求。当用户的需求描述只有一两句话、或者看起来功能很简单但你可能遗漏了什么的时候，一定要用此技能做深度解构。适用于任何测试任务的第二步骤——无论需求文档有多详细，解构之后总能发现盲区…

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

Proceduretype 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
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: 2. 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 "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "related_skills"
  • note frontmatter-key unknown frontmatter key "input_format"
  • note frontmatter-key unknown frontmatter key "output_format"
  • note frontmatter-key unknown frontmatter key "depth_requirement_quantification"
  • note frontmatter-key unknown frontmatter key "categories"
  • note frontmatter-key unknown frontmatter key "error_recovery_guidance"

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. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1057 tokens
  • 100Running it twice. No mutating operations
  • low 12 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 244: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (9 code blocks)

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

External checks

ClawHub: clean
This skill is a requirement-analysis helper with limited, disclosed read and URL-fetch capabilities and no executable payload.
LLM: benign (high) · VirusTotal: · 1 Sept 2026