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AC automotive-testcase-generator

面向车载（汽车电子/智能网联）领域的标准化测试用例生成技能。 当用户提到以下内容时触发：车载测试、汽车电子、ECU、CAN/CAN FD/LIN/FlexRay/ 车载以太网、SOME/IP、UDS/OBD 诊断、DTC、域控制器、智能座舱、IVI 车机、仪表、 T-BOX、ADAS/智能驾驶、HIL 测试、功能安全 ISO 26262、ASPICE、CANoe/CAPL、OTA、 车载软件测试、汽车功能测试等需求的测试用例生成。 输入需求文档（Markdown/PDF/Word/Excel）或需求描述，输出按车载模块分组的、 带优先级着色与模块分隔行的结构化 Excel 测试用例文件。 不适用于：机器人/工业/视觉领域（请使用对应领域技能）、自动化测试脚本生成、测试执行。

ClawHub Agent Skills author: kokxi v1.1.0 MIT-0 6 files body ≈ 2 892 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

面向车载（汽车电子/智能网联）领域的标准化测试用例生成技能。 当用户提到以下内容时触发：车载测试、汽车电子、ECU、CAN/CAN FD/LIN/FlexRay/ 车载以太网、SOME/IP、UDS/OBD 诊断、DTC、域控制器、智能座舱、IVI 车机、仪表、 T-BOX、ADAS/智能驾驶、HIL…

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

GeneratorExcelData and analyticsInfrastructuretype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
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: 6. 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")

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. No external tools needed
  • 100Steps. 62 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2892 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 341: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This is a content-only automotive test-case generation skill whose file reading and Excel output are disclosed and aligned with its purpose.
LLM: benign (high) · VirusTotal: · 31 Aug 2026