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AB pipl-gdpr-crosswalk

Compare China's Personal Information Protection Law (PIPL) with the EU GDPR for a specific data flow, and produce the gap list and cross-border transfer path for a company operating in both. Use when asked PIPL vs GDPR, 个人信息保护法和 GDPR 的区别, how do we transfer personal data out of China, our app serves users in China and Europe, or 数据出境 compliance. Produces a requirement crosswalk for the described processing, the transfer mechanism that likely applies in each direction, a prioritised gap list, and the questions to take to counsel. Not legal advice.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 1 147 tokens Open the sourcegithub.com↗ analyzed 3 h ago

Compare China's Personal Information Protection Law (PIPL) with the EU GDPR for a specific data flow, and produce the gap list and cross-border transfer path…

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

GeneratorLegaltype 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: pipl-gdpr-crosswalk (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. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1147 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 552: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)

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