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AB cn-labour-contract-decoder

Decode a mainland China labour contract (劳动合同) before signing: probation, pay and its structure, working hours, social insurance, non-compete and confidentiality, termination terms and anything that conflicts with the Labour Contract Law. Use when asked 帮我看看劳动合同, 劳动合同有什么坑, 试用期合法吗, 竞业限制条款, or review my Chinese employment contract. Produces a clause-by-clause decode with red, amber and green flags, the statutory limits each clause must respect, the questions to ask HR, and suggested wording for changes. Not legal advice.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 946 tokens Open the sourcegithub.com↗ analyzed 3 h ago

Decode a mainland China labour contract (劳动合同) before signing: probation, pay and its structure, working hours, social insurance, non-compete and…

As a process B 70/100 · Nearly there — weak spots: when it triggers, failures and branches, progress reporting

AnalyzerLegaltype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: cn-labour-contract-decoder (mohitagw15856/pm-claude-skills)

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: 1. 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 70/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 21 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 946 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 524: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 21 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.