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AC autopilot-flow

自动驾驶流程是把重复任务自动化的端到端能力包。它不停留在抽象的"识别→抽象→自动化" 口号，而是把六步法每步都具象化为可执行动作，并补齐原版缺失的错误处理、监控、 文档化环节。 核心能力： - 六步具象化：观察（日志法）、抽象（模式提取）、设计（流程图）、实现（脚本/配置）、 测试（dry-run）、优化（迭代指标） - 三个端到端模板：周报自动化、数据清洗自动化、文件整理自动化 - 错误处理：五级降级策略 - 监控：自动化健康度看板 - 触发思维：不是"等用户要求"，而是"看到重复立刻构建" 适用场景： - 每周手动做周报 - 每天清洗相似结构的数据 - 下载目录天天堆满待整理文件 - 任何"这事我做过三次了"的重复任务 差异化： - 原始版本只有6步口号式描述，本版把每步具象化为可执行动作与产物 - 新增三个端到端模板（周报、数据清洗、文件整理） - 新增错误处理五级降级与监控看板 - 新增"触发思维"章节，让Agent主动识别重复而非被动响应 - 增加FAQ与故障排查 触发关键词：重复任务、自动化、周报、数据清洗、文件整理、观察抽象、自动驾驶

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 1 584 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

自动驾驶流程是把重复任务自动化的端到端能力包。它不停留在抽象的"识别→抽象→自动化" 口号，而是把六步法每步都具象化为可执行动作，并补齐原版缺失的错误处理、监控、 文档化环节。 核心能力： - 六步具象化：观察（日志法）、抽象（模式提取）、设计（流程图）、实现（脚本/配置）、…

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

IntegrationZapierAI and agentsCustomer supporttype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
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: 0. 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 "summary"
  • note frontmatter-key unknown frontmatter key "tools"

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. 51 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1584 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

  • +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
  • +5Description quotes 3 example trigger phrases
  • +3Description length 487: enough signal without eating the budget
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (13 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
The skill is a legitimate automation guide, but it encourages agents to build or enable automations too proactively, including scripts, cron jobs, file moves, and email actions, without consistently requiring explicit user approval.
LLM: suspicious (high) · 17 Jul 2026