Smart Official Documents Writing Assistant

The Problem to Solve

Professionals in government and enterprises frequently need to draft official documents like requests, reports, and notices. This process is often time-consuming and fraught with challenges:

• Ambiguity in Document Type: It's easy to misclassify the intent or choose the wrong format (e.g., confusing a request with a report).
• Arduous Policy Research: Locating the latest authoritative regulations and policy references manually is slow and prone to missing critical information.
• Complex Formatting Standards: Official documents have strict rules for title structure, language, and layout (e.g., adhering to GB/T 9704-2012), which are difficult to track manually.
• Repetitive Workflow: Similar writing processes are repeated without efficient personalization or reuse.

How It Works: Core Capabilities & Key Steps

This skill functions as an intelligent co-pilot, breaking the writing process into seven manageable phases:

1. Intent Analysis & Element Extraction: It first identifies whether the user wants to create, query, or optimize a document. Through guided dialogue, it automatically extracts core elements like issuing department, subject, and recipient office, and recommends the most appropriate document type.
2. Dynamic Policy Retrieval: Utilizing web search, it prioritizes authoritative sources (.gov.cn domains) to fetch the latest laws and policies, verifies their currency, and provides standardized citation formats (e.g., 《Document Name》 (Document Number)).
3. Framework Generation: Based on the identified document type (e.g., request, notice), it selects a corresponding template and provides content prompts for each structural section.
4. Multi-Dimensional Compliance Check: Before and after draft generation, it performs checks on title format, linguistic style (flagging informal language), logical flow, and layout, offering specific optimization suggestions.
5. Interactive Drafting & Refinement: After generating an initial draft, it supports granular user feedback and revision per paragraph, even offering version comparisons with different styles (e.g., concise vs. detailed).
6. Habit Learning & Personalization: With user consent, it can learn and store preferences (like common document types, issuing units, writing styles) as metadata to pre-fill and streamline future tasks.

The entire process is interaction-first and progressive, ensuring quality checks at critical junctures.

Scope & Important Considerations

When using this skill, it's crucial to understand:

• An Assistive Tool: It significantly standardizes the process, provides references, and detects errors, but final decision-making, review, and approval remain human responsibilities.
• Dependency on Environment: Its policy research capability relies on the host agent's web search functionality; the quality of results depends directly on network access and source availability.
• Standards are Non-Negotiable: The skill is built upon strict adherence to national standards for official document processing and formatting, which is fundamental to its output quality.
• Data Privacy: The habit-learning feature typically only stores preference metadata, not the actual content of documents. Users should review and manage relevant privacy settings.