AC ai-gen-guard
生成式 AI 服务合规护栏 v1.0.2。 基于《生成式人工智能服务管理暂行办法》（国家网信办等七部门令第15号，2023年8月15日施行） 及强制性国家标准《网络安全技术 生成式人工智能服务安全基本要求》（2025年11月实施）， 实时评估生成式 AI 服务的合规风险，覆盖备案登记、训练数据、内容安全、用户权益、标识义务五大维度， 输出风险等级与合规缺陷清单。 核心能力： - 🛡️ 生成式 AI 服务场景自动识别（大模型/智能对话/AIGC 等关键词触发） - ✅ 5 维度合规检查：备案登记、训练数据、内容安全、用户权益、标识义务 - 📐 办法 + 配套强制国标双重映射 - 🔴 风险分级裁决（high / medium / low） - 🛡️ 否定前缀检测（"未备案"不会误判为"已备案"） - 📋 合规缺陷清单 + 建议动作输出 - ⚖️ 完全基于中国公开法规 + 强制性国家标准 触发关键词：生成式AI合规、大模型合规、AI服务备案、训练数据合规、 内容安全、算法备案、深度合成标识、AIGC合规 适用范围：提供生成式 AI 服务的中国企业 运行模式：纯本地，零网络请求 外部依赖：Python 标准库（无需额外安装） 本产品为免费护栏，纯本地运行，零网络请求，文本输入即可评估。
生成式 AI 服务合规护栏 v1.0.2。 基于《生成式人工智能服务管理暂行办法》（国家网信办等七部门令第15号，2023年8月15日施行） 及强制性国家标准《网络安全技术 生成式人工智能服务安全基本要求》（2025年11月实施）， 实时评估生成式 AI…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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: 9. 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")
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 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
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 797 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 556: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (5 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.