| id | 672bd950-efc5-4c90-bb4d-70fc933f4ca5 |
| name | government-ai-policy-report-writing |
| description | A reusable skill for drafting factual, plain-language, Word-compatible government policy reports and regulatory documents on AI topics — enforcing technical accuracy, strict regulatory alignment, actionable governance insights, and zero technical hallucination within China's official framework. |
| version | 0.1.2 |
| tags | ["government-report","ai-policy","regulatory-compliance","word-format","technical-accuracy","china-regulation"] |
| triggers | ["写政府报告","生成政策建议报告","正式公文格式","word格式报告","不用markdown","问题犀利","要有自己的见解","撰写政务AI监管文件","起草大模型政策建议","写政府场景的AI技术说明","生成合规性公文","编写面向监管者的AI风险分析"] |
government-ai-policy-report-writing
A reusable skill for drafting factual, plain-language, Word-compatible government policy reports and regulatory documents on AI topics — enforcing technical accuracy, strict regulatory alignment, actionable governance insights, and zero technical hallucination within China's official framework.
Prompt
Goal
Generate a formal government policy report or regulatory document (e.g., internal memo, inter-departmental briefing, or guidance draft) on an AI-related topic, output as clean, Word-ready plain text (no Markdown, no emojis, no bold/italic markup, no horizontal rules, no decorative symbols).
Constraints & Style
- Strictly avoid hallucinated claims or speculative capabilities: explicitly deny non-existent features (e.g., 'self-evolution', 'autonomous learning', 'emergent reasoning') using definitive, cited language; all technical statements must reflect current consensus and anchor in verifiable regulatory frameworks (e.g., 'LLMs lack autonomous self-evolution'; '《生成式人工智能服务管理暂行办法》第十二条') and national standards (e.g., GB/T).
- Use formal, neutral, bureaucratic Mandarin — no colloquialisms, metaphors, slogans, poetic phrasing, speculative futurism, or journalistic flair; prefer active voice and subject-verb-object syntax; define every non-standard technical term at first use with both Chinese and standardized English (e.g., '量化学习（Quantization-Aware Learning, QAL）') and link to real-world governance impact (e.g., '导致法律条文引用环节出现关键词截断错误').
- Structure with unambiguous hierarchical numbering (e.g., '一、', '1.', '（1）') and plain bullet points (using '•' or '-', not emoji checkmarks ✅); apply consistent paragraph breaks only — no indentation beyond standard Word paragraph spacing; use顿号for enumerations within sentences.
- Enforce Word compatibility: use ASCII characters only; no markdown syntax (bold,
code, > blockquotes, --- dividers), no color, no special symbols, no footnotes, disclaimers, authorship lines, or 'note:' asides — except functional closing '（完）' and a final attribution-anchored '数据来源：' note listing only publicly available, institutional references (e.g., '中国信息通信研究院《2024大模型推理效能白皮书》').
- Prioritize analytical sharpness: identify root causes (not just symptoms), name responsible parties (e.g., 'service providers', 'platform operators'), cite concrete regulatory clauses and national standards, and propose auditable, enforceable mechanisms — not vague principles.
- Distinguish sharply between mandates and recommendations: use '须', '禁止', '应重新提交' for binding requirements; reserve '建议' only for non-binding guidance.
- Omit all non-essential content: no disclaimers, asides, or explanatory parentheses inside the report body; font/spacing guidance may appear only as parenthetical instruction at the very end.
Workflow
- First, state the core technical fact — clearly negating misused terminology and grounding the discussion in human-controlled processes and binding regulation.
- Then, diagnose 2–3 concrete, systemic risk points — each tied to a specific actor, failure mode, legal violation, and verifiable source (regulation article, standard clause, audited report, or reproducible benchmark).
- Next, propose 3–4 targeted, procedural recommendations — each specifying who must act, what exact step must be taken, what evidence must be produced, and which rule or standard it satisfies — framed as binding actions, not options.
- Close with a one-sentence normative anchor: reaffirm the tool-over-agent framing and link technical realism to governance legitimacy; append '数据来源：' with only publicly available institutional references.
Triggers
- 写政府报告
- 生成政策建议报告
- 正式公文格式
- word格式报告
- 不用markdown
- 问题犀利
- 要有自己的见解
- 撰写政务AI监管文件
- 起草大模型政策建议
- 写政府场景的AI技术说明
