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AC harness-engineering

Use when building features, fixing complex bugs, or doing major refactoring. Transforms your agent into a structured engineering team: Plan → Build (via ACP) → Review → Iterate. Inspired by OpenAI, Anthropic & DeerFlow harness design.

ClawHub Agent Skills author: guixiang123124 v0.7.0 MIT-0 11 files body ≈ 2 558 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorAI and agentstype 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
57/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
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: 11. 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 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 8 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (harness-engineering) differs from the folder (harness-factory)
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 70Failures and branches. 4 branches
    • 100Steps. 37 steps
    • 100Execution cost. Instruction body is 2558 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • -5TODO / placeholder text left in the skill
    • -44 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 234: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 37 items
    • +4Has examples (10 code blocks)

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

    External checks

    ClawHub: suspicious
    This is a mostly transparent engineering workflow skill, but it makes push and production deployment part of the default process without a clear approval gate.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026