Negative List Research Skill.
Turn a region, sector, and outbound-data scenario into a source-linked research memo—without treating a public list as a substitute for legal analysis.
Built from DCC's maintained registry of China's FTZ and province-level data-export negative lists. The Skill guides an AI agent through jurisdiction, sector, scenario, management model, and source verification before it drafts a research result.
What is included
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A portable
SKILL.mdworkflow for structured intake, routing, source checks, and research output. - A normalized registry snapshot in JSON and CSV, covering the current jurisdictions, sectors, models, dates, and official source URLs.
- A source-file index covering available official documents and DCC English translations.
- A memo template that separates a confirmed list match, a possible match, missing facts, and issues outside the list mechanism.
- Example queries and expected output structure.
What it does—and does not—decide
The Skill is designed for first-pass issue spotting and source retrieval. It can identify published list coverage and explain the local management model.
It does not independently determine whether unlisted data is “important data,” whether another national or sectoral rule applies, or whether a transfer is lawful. Those questions remain explicit follow-up items in every output.
Ask a concrete routing question
A medical-device company in the Beijing “Two Zones” wants to transfer post-market product and customer-support data to its global compliance team. Which negative list should be checked, what local process applies, and what facts are still missing?
The output follows a fixed structure: applicable jurisdiction → possible sector/scenario match → management model → source links → missing facts → limits and next checks.
Checkout will open after delivery is ready.
DCC is completing the Skill package and a verified PayPal Business checkout with immediate download and automatic email backup.