SKILLEMALL.ai

AC qa-quality-metrics

当管理层问"质量到底怎么样"、需要量化质量数据来做决策、或者想建立质量看板来跟踪趋势时使用此技能。从过程质量(需求评审通过率/用例覆盖度)、结果质量(Bug 密度/线上事故数)、效率(测试周期/回归耗时)和健康度(自动化通过率/环境稳定性)四个维度设计度量指标。⚠️ 度量的目的不是打分,是发现问题趋势——如果只报喜不报忧,度量就没用了。 触发场景:质量度量、质量指标、怎么量化质量、质量看板、质量数据、趋势分析、向管理层展示质量数据时。 Use when the user asks about: test quality metrics and dashboards — process, result, efficiency, and health indicators for management reporting.

ClawHub Agent Skills author: kokxi v1.8.0 MIT-0 3 files body ≈ 257 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

当管理层问"质量到底怎么样"、需要量化质量数据来做决策、或者想建立质量看板来跟踪趋势时使用此技能。从过程质量(需求评审通过率/用例覆盖度)、结果质量(Bug 密度/线上事故数)、效率(测试周期/回归耗时)和健康度(自动化通过率/环境稳定性)四个维度设计度量指标。⚠️…

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
59/100
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

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 59/100

    • 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
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 257 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
    • +2Single-language instructions
    • +3Description length 364: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This is a QA metrics guidance skill with no automatic execution or hidden data handling, though its optional unpinned install suggestion deserves caution.
    LLM: benign (high) · VirusTotal: · 29 Sept 2026