# How the Patent‑Disclosure‑Skill Solves the "Know‑How But Not How to Write Patents" Problem

> Learn how the patent-disclosure-skill automates patent drafting, transforming your technical know-how into legally formatted disclosures without needing patent law expertise.

- Repository: [handsomestWei/patent-disclosure-skill](https://github.com/handsomestWei/patent-disclosure-skill)
- Tags: how-to-guide
- Published: 2026-09-05

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**The patent‑disclosure‑skill bridges the gap between technical expertise and patent drafting by automating an eight‑step workflow that transforms raw engineering knowledge into a complete, legally‑formatted Chinese patent disclosure without requiring users to learn patent law or writing conventions.**

Engineers and developers often possess deep **technical know‑how** but struggle to articulate their inventions in the structured, formal language required by patent offices. The `handsomestWei/patent‑disclosure‑skill` eliminates this barrier through a deterministic, tool‑driven pipeline that guides users from initial concept to submission‑ready document. Every step is backed by reusable prompts, automated tooling, and CNIPA‑compliant templates.

## The Eight‑Step Solution Architecture

The skill operates as a **deterministic workflow** rather than an open‑ended conversation. Each step is implemented through specific files in the repository, ensuring reproducible results across different technical domains.

### Step 1: Intake and Boundary Definition

The workflow begins with [`prompts/intake.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/intake.md), which captures the project's domain, desired **patent type** (invention, utility model, or design), and contact information through a concise questionnaire. If the user omits the patent type, the system defaults to **invention**—the most common and comprehensive protection type.

This forced articulation makes the hidden inventive concept explicit. Users must distill their technology into a single sentence, which the system then uses to anchor all subsequent analysis.

### Step 2: Automatic Project Scanning

The [`project_scan.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/project_scan.md) module reads source files—including code, documentation, and presentation materials—and converts them to Markdown. From this unified representation, the skill extracts **technical elements** such as modules, algorithms, data flows, and system architectures.

This automation removes the manual "inventory" phase that typically stalls non‑patent professionals.

### Step 3: Patent‑Point Mining

For each patent category, dedicated prompt sets enumerate **candidate inventive points**:

- `prompts/invention/` — for technical solutions solving technical problems
- `prompts/utility_model/` — for structural improvements with practical utility
- `prompts/design/` — for ornamental appearances of industrial products

These candidate points are merged into a coherent set of **inventive concepts** that form the backbone of the disclosure.

### Step 4: Line‑Art and CAD Generation

Visual assets are mandatory for utility models and design patents. The skill generates **clean line drawings** through two specialized gates:

- [`tools/structure_lineart_gate.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/structure_lineart_gate.py) — renders mechanical/structural diagrams for utility models
- [`tools/design_lineart_gate.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/design_lineart_gate.py) — produces orthographic views for design patents

The pipeline can ingest user‑provided assets or invoke a built‑in **CAD‑to‑vector renderer** when source files are unavailable.

### Step 5: Lightweight Prior‑Art Search

The [`tools/crawl/cnipa_epub_search.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/crawl/cnipa_epub_search.py) module executes a **two‑stage search** of the China National Intellectual Property Administration (CNIPA) e‑publication site via Playwright:

1. **First stage:** Issues 2–8 keyword queries, retrieving one page per keyword
2. **Second stage:** Refines results using **IPC/LOC classification codes** extracted from first‑stage hits

This mimics the search strategy of experienced patent searchers without requiring users to understand classification systems.

### Step 6: Disclosure Drafting

The [`prompts/disclosure_builder.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/disclosure_builder.md) module populates **pre‑crafted Markdown templates** with:

- Mined inventive points from Step 3
- Generated line‑art from Step 4
- Prior‑art citations from Step 5

Templates are split into mandatory sections: **technical field**, **background art**, **summary of invention**, **detailed description**, and **claims framework**—all structured to meet CNIPA standards.

### Step 7: Self‑Check and Iteration

The [`prompts/iteration_context.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/iteration_context.md) module runs **automated sanity checks** for:

- Logical consistency between problems and solutions
- Proper formatting of formulas and equations
- Correct numbering of figures and embodiments

Corrections and supplemental materials are stored as **new timestamped files** via [`tools/iteration_dialog_log.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/iteration_dialog_log.py), preserving complete audit trails.

### Step 8: Output Packaging

The final [`tools/md_to_docx.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/md_to_docx.py) module renders the Markdown disclosure into a **polished Word document (DOCX)** with proper styling, plus companion **Mermaid diagrams** for system flows. Files are named with the case name and timestamp for version control.

## Why This Solves the Core Problem

The "know‑how but not how to write patents" challenge manifests in five specific friction points—each addressed by the skill's design:

| Friction Point | Skill Solution |
|----------------|--------------|
| **Inability to articulate the inventive concept** | Forced single‑sentence articulation in intake prompts |
| **Lack of search expertise** | Automated two‑stage CNIPA search with IPC/LOC extraction |
| **Unfamiliarity with visual requirements** | Scripted line‑art generation for all patent types |
| **Ignorance of formal structure** | Template‑driven drafting with mandatory section enforcement |
| **Risk of technical errors** | Automated self‑check with timestamped iteration logging |

## Practical Workflow Example

The following commands demonstrate how an Agent invokes the full disclosure pipeline:

```bash

# 1. Ensure browser engine is available for web crawling

python skills/patent-disclosure/tools/browser.py --probe

# 2. Execute prior-art search for distributed scheduling technology

python skills/patent-disclosure/tools/crawl/cnipa_epub_search.py \
    --type invention \
    分布式 调度 任务队列

# 3. Convert final disclosure to submission-ready Word document

python skills/patent-disclosure/tools/md_to_docx.py \
    -i outputs/patent-disclosure/<timestamp>.md \
    -o outputs/patent-disclosure/<timestamp>.docx

```

The search tool outputs a JSON line prefixed with `EPUB_HITS_JSON:` containing deduplicated prior‑art references, which the drafting module consumes automatically.

## Key Implementation Files

| File | Purpose |
|------|---------|
| [`SKILL.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/SKILL.md) | Entry point mapping eight steps to concrete prompts and tools |
| [`prompts/intake.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/intake.md) | Patent type selection and core technology articulation |
| [`prompts/prior_art_search.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/prior_art_search.md) | Two‑stage CNIPA search protocol with Google Patents fallback |
| [`tools/crawl/cnipa_epub_search.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/crawl/cnipa_epub_search.py) | Playwright‑driven search implementation with JSON output contract |
| [`tools/md_to_docx.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/md_to_docx.py) | Markdown-to-Word conversion with CNIPA‑compliant styling |
| [`prompts/iteration_context.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/prompts/iteration_context.md) | Self‑check logic and correction handling |
| [`tools/structure_lineart_gate.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/structure_lineart_gate.py) / [`tools/design_lineart_gate.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/design_lineart_gate.py) | Automated line‑art generation |

## Summary

- **Structured conversation** through forced‑choice intake eliminates ambiguous starting points
- **Automated heavy‑lifting** covers search, visualization, and formatting that non‑experts cannot perform manually
- **Template enforcement** guarantees CNIPA compliance without user knowledge of patent formalities
- **Modular isolation** allows users to invoke only the disclosure flow without contamination from other patent tasks
- **Deterministic reproducibility** ensures consistent output quality across different technical domains

## Frequently Asked Questions

### Does the skill support English patent applications?

The current implementation targets **Chinese patent disclosure** (交底书) for CNIPA submission. The templates, search tools, and output formatting are optimized for Chinese patent practice. English support would require parallel prompt sets and jurisdiction‑specific templates.

### What technical domains work best with the automated project scan?

The [`project_scan.md`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/project_scan.md) module handles **source code repositories**, technical documentation, and presentation files universally. It performs particularly well on software architectures, mechanical CAD assemblies, and electrical system diagrams—domains where structural elements map cleanly to patent claims.

### How does the prior‑art search compare to professional patent search services?

The [`cnipa_epub_search.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/cnipa_epub_search.py) tool provides **lightweight, automated coverage** of CNIPA's open publication database. It matches the retrieval depth of a preliminary search but lacks the interpretive analysis of a professional searcher. Users should treat results as a starting point; critical filings may warrant supplemental professional search.

### Can the skill handle design patents for non‑mechanical products?

Yes. The [`tools/design_lineart_gate.py`](https://github.com/handsomestWei/patent-disclosure-skill/blob/main/tools/design_lineart_gate.py) module accepts **any product category** supported by the user's input assets or CAD files. The LOC (Locarno Classification) extraction in the prior‑art search adapts to ornamental designs across all industrial product classes.