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BC qa-regression-testing

根据变更范围、风险等级和时间约束制定分级精准回归方案。当版本迭代了、代码改动了、你需要确定"到底哪些功能要重新测一遍"的时候使用此技能。回归的时间永远不够——此技能帮你做出取舍决策：冒烟回归（P0核心流程）、核心回归（高影响区域）、全量回归（有余力时）。基于变更分析和风险评估选择最省时的回归策略，而不是盲目全量回归。 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 ≈ 913 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

根据变更范围、风险等级和时间约束制定分级精准回归方案。当版本迭代了、代码改动了、你需要确定"到底哪些功能要重新测一遍"的时候使用此技能。回归的时间永远不够——此技能帮你做出取舍决策：冒烟回归（P0核心流程）、核心回归（高影响区域）、全量回归（有余力时）。基于变更分析和风险评估选择最省时的回归策略，而不是盲目全量回归。…

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

ProcedureData and analyticsSoftware developmenttype 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 "categories"
  • note frontmatter-key unknown frontmatter key "depth_requirement_quantification"
  • 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. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 913 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 243: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (8 code blocks)

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

External checks

ClawHub: clean
This skill provides disclosed QA regression-planning guidance and does not request sensitive access or hidden execution.
LLM: benign (high) · VirusTotal: · 1 Sept 2026