BC qa-bug-root-cause-analysis
当某个 Bug 频繁复现、线上有缺陷需要做事后分析、或者发现同一类问题反复出现需要根治时使用此技能。从症状出发用 5Why、因果图和鱼骨图等方法系统化定位缺陷根源，区分直接原因、间接原因和系统原因。不要只修症状——根因分析的价值在于找到让同类 Bug 不再发生的系统性改进措施，同时分析漏测原因来优化测试设计。 ⚠️ 本技能示例可能调用外部日志/监控工具，请在受控环境执行。 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
当某个 Bug 频繁复现、线上有缺陷需要做事后分析、或者发现同一类问题反复出现需要根治时使用此技能。从症状出发用 5Why、因果图和鱼骨图等方法系统化定位缺陷根源，区分直接原因、间接原因和系统原因。不要只修症状——根因分析的价值在于找到让同类 Bug 不再发生的系统性改进措施，同时分析漏测原因来优化测试设计。 ⚠️…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Grep Glob Bash
Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "related_skills" - note
frontmatter-keyunknown frontmatter key "input_format" - note
frontmatter-keyunknown frontmatter key "output_format" - note
frontmatter-keyunknown frontmatter key "categories" - note
frontmatter-keyunknown frontmatter key "depth_requirement_quantification" - note
frontmatter-keyunknown 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. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1071 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 271: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.