BC china-data-compliance
Ensure applications comply with Chinese data protection laws (PIPL, Cybersecurity Law, Data Security Law). Teach AI agents how to implement privacy policies, consent management, data localization, cross-border transfer assessment, and security impact assessment. Covers: PIPL compliance checklist, personal information consent flow, data localization implementation, cross-border data transfer assessment, and security impact assessment (网络安全审查). Triggers on: 中国数据合规, china data compliance, 个人信息保护法, PIPL compliance, 网络安全法, cybersecurity law, 数据安全法, data security law, 数据本地化, data localization, 跨境数据传输, cross-border data transfer, 隐私政策, privacy policy china, 个人信息同意, consent management china, 网络安全审查, security assessment china, 数据出境, data export china
Ensure applications comply with Chinese data protection laws (PIPL, Cybersecurity Law, Data Security Law).
As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 34 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2060 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 top-level sections: this looks like several domains in one skill
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
- +2Single-language instructions
- +3Description length 751: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (8 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.