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AB factory-acceptance-test

Write a factory acceptance test (FAT) plan or report — test coverage matrix against spec, AQL sampling plan, pass/fail criteria, golden-sample handling, deviation log, and sign-off structure. Use when asked to write a FAT plan, define outgoing quality inspection, set AQL levels, prepare for a factory acceptance or pre-shipment inspection, or document FAT results. Produces a complete FAT plan or report with sampling tables, defect classification, and a sign-off block.

ClawHub Agent Skills author: mohitagw15856 v1.0.0 MIT-0 2 files body ≈ 1 151 tokens Open the sourceclawhub.ai↗ analyzed 15 h ago

Write a factory acceptance test (FAT) plan or report — test coverage matrix against spec, AQL sampling plan, pass/fail criteria, golden-sample handling…

As a process B 77/100 · Nearly there — weak spots: when it triggers, failures and branches

GeneratorQuality controlLogistics and warehouseData and analyticstype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
77/100
Nearly there
Failures and branches w 10
0
When it triggers w 12
20
Inputs and preconditions w 11
70
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 77/100

    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 30 steps
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1151 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 471: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 30 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: clean
    The skill is a document-writing helper for factory acceptance testing and does not request unusual access or hidden execution.
    LLM: benign (high) · VirusTotal: · 17 Jul 2026