What does this skill do, and when should you use it?
This skill targets Chinese patents with two modes: Mode A mines patent points from project materials, performs prior-art search (prioritizing the CNIPA official publication site), drafts anonymized technical disclosures (with Mermaid diagrams and Word export), and runs a self-check loop; Mode B interprets existing patents (by publication number or PDF) into plain-language narratives, claim trees, and an Obsidian knowledge graph. The skill includes step-by-step prompts, Python utilities (e.g., Office-to-Markdown conversion, EPUB search, diagram rendering), and supports iterative revision (merge/correction) with versioning. Installation follows the AgentSkills convention, requiring Python and optional Node.js dependencies.
Reads project documentation (converting .docx/.pptx first), scans code and design docs, and analyzes candidate patent points; calls cnipa_epub_search.py (primary) or falls back to WebSearch for prior art; generates an anonymized technical disclosure template with Mermaid system/flow diagrams, renders PNGs, and exports .docx; runs an internal self-check checklist; supports merging or correcting an existing disclosure and saving a new timestamped file with a revision log; for existing patents, extracts text (auto-downloading PDF if needed), builds a claim tree, and creates a plain-language narrative plus an Obsidian note with automatic bootstrap (CSS/Canvas).
- R&D engineers: have project design docs and code, want to identify patent points and draft a disclosure—just describe it in natural language.
- Patent engineers: need online prior-art search (preferring CNIPA) and a Word disclosure with system/flow diagrams.
- Iterative review: when you add materials or correct an existing disclosure file, the skill auto-detects and saves a new version.
- Tech enthusiasts: have a publication number or PDF, want to quickly understand the patent and generate a knowledge graph note.
- Knowledge managers: link multiple patent interpretations into an Obsidian vault with cross-referenced Canvas graphs.
How do you install this skill?
- The skill depends on the CNIPA website (epub.cnipa.gov.cn); if unreachable or network-restricted, the prior-art search degrades to WebSearch; verify the reliability of this fallback.
- Obsidian installation and full configuration are strongly recommended, but the degradation path (e.g., outputs/ directory) lacks detailed documentation.
- The toolchain relies on Playwright and Node.js, which are complex to install and large; in constrained environments, core functionality may be unavailable.
- The skill claims automatic bootstrap of Obsidian vault, but provides no verification or rollback mechanism for this process.
- Shell / CLI
- Network access
- Local filesystem
Python 3.9+Node.js (for mermaid)
Place the cloned repository into the skills path of Claude Code or Cursor (e.g., .claude/skills/patent-disclosure-skill). Run pip install -r requirements.txt; optionally pip install -r tools/requirements-cnipa.txt and python -m playwright install chromium for CNIPA search; install Node.js for Mermaid rendering and run npm install in the tools folder or use npx mmdc. See INSTALL.md for details.
git clone --depth 1 https://github.com/handsomestWei/patent-disclosure-skill.git ~/.claude/skills/patent-disclosure-skillGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Write a technical disclosure based on the project at `/path/to/project`
- Explain patent CN12345678A
Describe your need in natural language to the agent, e.g., “Write a technical disclosure based on the project at /path/to/project” or “Explain patent CN12345678A”. The skill auto-selects Mode A or B based on intent. You can also use slash commands like /patent-disclosure-skill and should specify a project path or technical topic in the prompt.
What are this skill's strengths and limitations?
- Covers the full disclosure pipeline: mining, search, drafting, self-check, iteration.
- Prior-art search prioritizes the official CNIPA site, reducing reliance on search engines.
- Supports Office document conversion and integrates Mermaid diagrams with docx export.
- Iteration mode auto-detects, saves new files, and logs revision history.
- Mode B generates Obsidian knowledge graphs, great for personal knowledge management.
- Primarily for Chinese patents; limited support for international or non-Chinese materials.
- CNIPA search depends on Playwright and network, and may fail, falling back to WebSearch.
- No automated test suite; reliability depends on actual runtime conditions.
- Obsidian integration strongly recommends a configured vault path; otherwise degrades to output files.
- Some features (Node.js rendering) require extra installs, with a non-trivial initial setup.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| China Patent Disclosure Assistant this page | 51 · Use with care | ★ 11k | 3d ago | MIT |
| Obsidian Markdown Editor | 57 · Use with care | ★ 49k | 26d ago | MIT |
| Pandoc Chinese Word Template Skill | 54 · Use with care | ★ 1.1k | 3mo ago | — |
| LLM Wiki — a second brain for AI agents | 58 · Recommended | ★ 119 | 28d ago | MIT |
| Autonomous Research Filing | 52 · Use with care | ★ 15k | 1mo ago | MIT |
How did FollowSkills review this skill?
Evidence: The skill declares allowed tools including Bash, WebSearch, Read/Write, and explicitly requires reading prompt files step-by-step; it fixes scripts for prior-art search and PDF download (cnipa_epub_search.py, fetch_patent_pdf.py) and forbids writing ad-hoc download scripts in-session; it includes desensitization requirements for sensitive data; iteration saves new timestamped files without overwriting old drafts. Deductions: No explicit disclosure of data flow (e.g., where search results go, whether processing is local-only); no rollback or undo mechanism; third-party dependencies (Playwright) not subject to security review. Main risks visible but not fully addressed, so 12.
Evidence: The skill provides detailed step-by-step instructions and tool tables, but no executable tests or CI configuration is present; test files exist (tests/test_cnipa_epub_chain.py etc.) covering only some tools, but no CI workflow is in the repo; happy path behavior is described, but error handling (e.g., network failures, missing files) is not clearly specified. Deductions: Key path reproducibility cannot be determined statically; limited feedback on failure scenarios. Thus 8.
Evidence: The skill clearly distinguishes two modes (disclosure drafting and patent reading) with trigger conditions and target scenarios; Chinese language support and access to CNIPA (cnipa.gov.cn) from mainland China are core features; boundaries are stated (e.g., when a patent number is provided, it prioritizes reading mode). Deductions: Capability boundaries (e.g., unsupported patent types) not fully detailed; availability of CNIPA service not verified; if core functionality depended on unreachable overseas services, deduction would apply, but here domestic services dominate, so 10.
Evidence: Repository includes README, INSTALL.md, LICENSE (MIT), examples/ directory, docs/, prompt mapping tables; version is v2.0.0 in SKILL.md; no changelog but documentation is comprehensive. Deductions: No explicit maintenance responsibility or update path; no FAQ; version number appears only in SKILL.md, not unified across the repo. Thus 10.
Evidence: The skill claims to produce technical disclosures and patent reading notes, with examples/ directory, but no verifiable output samples or evidence that outputs are directly usable; value proposition is clear (time-saving, deliverable), but comparative benefit lacks evidence. Deductions: Static read cannot verify output quality; capped at 7, so 7.
Evidence: Repository includes multiple test files (tests/test_cnipa_epub_chain.py etc.) covering some tool modules, serving as auditable primary material, but no CI configuration or third-party execution evidence; key claims (e.g., auto-bootstrap, search success rates) lack independent verification. Deductions: Test coverage is limited, and no independently reproducible conclusions, so 4.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
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