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AD harness-dev-standards

Harness Engineering 开发规范体系 - 全流程质量门禁与自动治理标准。基于企业级全AI研发实践改进，提供完整的代码交付质量保障框架。Use when: (1) 启动新项目开发前, (2) 代码交付前做质量检查, (3) 需要标准化开发流程, (4) 执行架构评审、代码评审, (5) 排查依赖/环境问题

ClawHub Agent Skills author: AIaCheng v1.0.3 MIT-0 11 files · 2 scripts body ≈ 855 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype 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%
99
Quality 40%
79
Run on models
none yet
Process rating
D
46/100
Unfinished process
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv references/standards.md:297
      Reads a .env file
      cp .env.example .env.local

    Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Harness Engineering 开发规范体系 - 全流程质量门禁与自动治理标准。基于企业级全AI研发实践改进，提供完整的代码… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value

    Process rating: all ten parameters 46/100

    • 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
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 62 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 855 tokens
    • 100Running it twice. No mutating operations
    • low 10 top-level sections: this looks like several domains in one skill

    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
    • -243 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 160: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

    External checks

    ClawHub: suspicious
    This is a coherent development-quality skill, but it tells agents to auto-fix code, dependencies, environment files, and port conflicts without enough user control.
    LLM: suspicious (high) · 17 Aug 2026