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Patent Pipeline

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wanshuiyin
patent-pipeline

Full patent drafting pipeline from invention description to jurisdiction-formatted filing documents. Supports CN (CNIPA), US (USPTO), EP (EPO). Supports invention patents and utility models. Use when user says "写专利", "patent pipeline", "专利申请", "draft patent", "写权利要求书", or wants to draft a complete patent application.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepatent-pipeline
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Patent Pipeline AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/patent-pipeline .claude/skills/patent-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Patent Pipeline in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Patent Pipeline on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Patent Pipeline is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Patent Pipeline: From Invention to Filing

Draft a complete patent application based on: $ARGUMENTS

Overview

This skill orchestrates the full patent drafting lifecycle -- from prior art search through jurisdiction-formatted filing documents. It chains sub-skills into a patent-specific pipeline:

/prior-art-search → /patent-novelty-check → /invention-structuring → /claims-drafting → /specification-writing → /patent-review → /jurisdiction-format
     (search)           (verify)              (structure)             (claims)            (description)          (examiner)         (compile)
                                                                                              ├── /figure-description
                                                                                              └── /embodiment-description

This is a parallel branch, not part of the linear research pipeline. After /idea-discovery produces validated ideas, the user can either:

  • Go to /experiment-bridge/auto-review-loop/paper-writing (publish track)
  • Go to /grant-proposal (funding track)
  • Go to /patent-pipeline (patent track) <-- this skill
                    ┌→ /experiment-bridge → /auto-review-loop → /paper-writing  (publish track)
/idea-discovery ────┤
                    ├→ /grant-proposal → [get funded] → ...  (funding track)
                    └→ /patent-pipeline → [file patent]       (patent track)

Patents are about protecting inventions (legal scope), not publishing results (academic contribution). This skill handles the unique requirements of patent drafting: prior art analysis, claims hierarchy design, specification writing with enablement support, embodiment descriptions, and jurisdiction-specific formatting.

Constants

  • JURISDICTION = CN — Target patent jurisdiction. Options: CN (CNIPA), US (USPTO), EP (EPO), ALL (generate all three). Override via argument (e.g., /patent-pipeline "invention — US").
  • PATENT_TYPE = inventioninvention (发明专利, 20 year protection) or utility_model (实用新型, CN only, 10 year protection, apparatus claims only). Override via argument.
  • REVIEWER_MODEL = gpt-6-astra — Model used via Codex MCP for examiner-style review.
  • MAX_REVIEW_ROUNDS = 2 — Maximum review-revision cycles.
  • AUTO_PROCEED = false — At each checkpoint, always wait for explicit user confirmation. Patent applications require inventor judgment at every stage. Set true only if user explicitly requests autonomous mode.
  • LANGUAGE = auto — Output language. Auto-detected from jurisdiction: CN->Chinese, US->English, EP->English. Override explicitly if needed.
  • OUTPUT_DIR = patent/ — Directory for generated patent files.
  • OUTPUT_FORMAT = markdown — Draft format. markdown for review, docx for filing-ready.

Override defaults via arguments: /patent-pipeline "invention — US, utility model" or /patent-pipeline "invention — ALL, language: Chinese".

Patent Type Specifications

Invention Patent (发明专利)

FieldDetail
Protection20 years from filing date
Subject matterMethods, systems, products, compositions, processes
ExaminationSubstantive examination required
Inventive stepHigh (must involve an inventive step / 创造性)
Timeline2-4 years to grant (CN); 2-3 years (US); 3-5 years (EP)
ClaimsMethod + system + product claims allowed

Utility Model (实用新型) — CN Only

FieldDetail
Protection10 years from filing date
Subject matterProduct shape, structure, or combination thereof only
ExaminationFormal examination only (no substantive examination)
Inventive stepLower than invention patent
Timeline6-8 months to grant
ClaimsApparatus/device claims only. NO method claims.
RestrictionCN jurisdiction only

State Persistence (Compact Recovery)

Patent drafting is a long task that may trigger context compaction. Persist state to patent/PATENT_STATE.json after each phase:

json
{
  "phase": 3,
  "jurisdiction": "CN",
  "patent_type": "invention",
  "language": "Chinese",
  "codex_thread_id": "019cfcf4-...",
  "invention_title": "...",
  "claims_count": 15,
  "status": "in_progress",
  "timestamp": "2026-04-10T15:00:00"
}

Write this file at the end of every phase. On invocation, check for this file:

  • If absent or status: "completed" -> fresh start
  • If status: "in_progress" and within 24h -> resume from saved phase (read output files to restore context)
  • If older than 24h -> fresh start (stale state)

On completion, set "status": "completed".

Workflow

Phase 0: Input Parsing & Context Gathering

Parse $ARGUMENTS to extract:

  1. Invention description — may be structured (references INVENTION_BRIEF.md), conversational with figures, or output from IDEA_REPORT.md
  2. Jurisdiction — detect from keywords (e.g., "CN" or "中国" -> CN, "US" or "USPTO" -> US, "EP" or "EPO" -> EP, "ALL")
  3. Patent type — detect from keywords (e.g., "utility model" or "实用新型" -> utility_model, default -> invention)
  4. Overrides — language, output format, review rounds

Then gather context from the project directory:

  1. Read INVENTION_BRIEF.md if it exists (user filled in the template)
  2. Read IDEA_REPORT.md if it exists (from /idea-discovery -- can extract invention from research results)
  3. Read refine-logs/FINAL_PROPOSAL.md if it exists
  4. Read NARRATIVE_REPORT.md if it exists (research results that may be patentable)
  5. Search for user-provided figures (PNG, JPG, SVG, PDF) in the project directory
  6. Check for patent/PATENT_STATE.json (resume from prior interrupted run)

If insufficient context exists:

  • No invention description at all -> suggest user describe the invention or fill in INVENTION_BRIEF.md
  • Has IDEA_REPORT.md -> extract patentable aspects from the research
  • Has figures -> reference them in the invention brief
  • No figures -> note that figures will be needed and plan what drawings are required

If the input is conversational (not a structured brief), parse the description into the invention brief structure and write patent/INVENTION_BRIEF.md for downstream phases.

Phase 1: Prior Art Search & Novelty Assessment

1.1 Prior Art Search

Invoke /prior-art-search:

/prior-art-search "patent/INVENTION_BRIEF.md"

This searches patent databases (Google Patents, Espacenet) and academic literature for relevant prior art.

1.2 Novelty Check

Invoke /patent-novelty-check:

/patent-novelty-check "patent/INVENTION_BRIEF.md"

This assesses novelty and non-obviousness against the prior art found in step 1.1.

🚦 Checkpoint: Present the prior art landscape and novelty assessment:

Prior art search complete:
- [X] patent references found
- [Y] non-patent literature references found
- Closest prior art: [reference] -- [why it's closest]
- Novelty assessment: [PATENTABLE / PATENTABLE WITH AMENDMENTS / NOT PATENTABLE]
- Key risk areas: [list]

Ready to proceed with invention structuring?

⛔ STOP HERE and wait for user response. Do NOT auto-proceed unless AUTO_PROCEED=true.

Options:

  • Reply "go" -> proceed to Phase 2
  • Reply with adjustments -> refine the invention scope and re-check novelty
  • Reply "stop" -> save progress to patent/DRAFT_NOTES.md

State: Write PATENT_STATE.json with phase: 1.

Phase 2: Invention Structuring & Claims Design

2.1 Structure the Invention

Invoke /invention-structuring:

/invention-structuring "patent/INVENTION_BRIEF.md"

This decomposes the invention into core inventive concept, supporting features, and optional features. Produces patent/INVENTION_DISCLOSURE.md.

2.2 Draft Claims

Invoke /claims-drafting:

/claims-drafting "patent/INVENTION_DISCLOSURE.md"

This drafts the claims hierarchy -- the most critical part of the patent. Produces patent/CLAIMS.md.

🚦 Checkpoint: Present the invention structure and claims:

Invention structured:
- Core inventive concept: [summary]
- Claim categories: [method, system, etc.]
- Claims drafted: [X] independent + [Y] dependent = [Z] total
- Independent claim 1 (broadest): [first 50 words of claim 1]
- Examiner review score: [X]/10

The claims define the legal scope of protection. Please review before proceeding to specification.

⛔ STOP HERE and wait for user response. Do NOT auto-proceed unless AUTO_PROCEED=true.

Options:

  • Reply "go" -> proceed to Phase 3
  • Reply with adjustments (e.g., "broaden claim 1", "add more dependent claims") -> revise claims
  • Reply "stop" -> save progress

State: Write PATENT_STATE.json with phase: 2.

Phase 3: Specification Writing

Invoke /specification-writing:

/specification-writing "patent/CLAIMS.md"

This writes the full specification section by section. Internally invokes /figure-description (if user-provided figures exist) and /embodiment-description for the detailed description. The specification-writing skill handles figure processing and embodiment writing as sub-skills.

🚦 Checkpoint: Present the specification overview:

Specification written:
- Title: [title]
- Sections: Technical Field, Background, Summary, Drawings Description, Detailed Description, Abstract
- Embodiments: [X]
- Reference numerals: [Y] components mapped
- Abstract length: [Z] words (limit: [jurisdiction limit])
- Claim support: [all elements covered / X elements missing]

Ready to proceed to review?

⛔ STOP HERE and wait for user response.

State: Write PATENT_STATE.json with phase: 3.

Phase 4: Patent Review

Invoke /patent-review:

/patent-review "patent/"

This runs 2 rounds of examiner-style review via GPT-6-Astra xhigh. The examiner evaluates clarity, written description, enablement, novelty, non-obviousness, and claim scope.

State: Write PATENT_STATE.json with phase: 4 and review score.

Phase 5: Jurisdiction Formatting & Output

Invoke /jurisdiction-format:

/jurisdiction-format "patent/"

This compiles the application into the target jurisdiction format(s).

Final Deliverables
OutputLocationDescription
CN: 权利要求书patent/output/CN/Claims in CNIPA format
CN: 说明书patent/output/CN/Description in CNIPA format
CN: 说明书摘要patent/output/CN/Abstract (CN)
US: Claimspatent/output/US/Claims in USPTO format
US: Specificationpatent/output/US/Description in USPTO format
US: Abstractpatent/output/US/Abstract (US)
EP: Claimspatent/output/EP/Claims in EPO format
EP: Descriptionpatent/output/EP/Description in EPO format
EP: Abstractpatent/output/EP/Abstract (EP)
Final Report
markdown
## Patent Pipeline Complete

### Application Summary
- Title: [invention title]
- Jurisdiction: [CN/US/EP/ALL]
- Patent Type: [Invention / Utility Model]
- Language: [Chinese/English]
- Total Claims: [X] independent + [Y] dependent

### Pipeline Scores
| Phase | Score |
|-------|-------|
| Prior Art Search | [completeness assessment] |
| Novelty Assessment | [PATENTABLE/PATENTABLE WITH AMENDMENTS/NOT PATENTABLE] |
| Examiner Review Round 1 | [X]/10 |
| Examiner Review Round 2 | [Y]/10 |
| Final | [Z]/10 |

### Output Files
[Table of all generated files with paths]

### Next Steps
- [ ] Have a patent attorney review the application
- [ ] Conduct professional prior art search (this tool's search is preliminary)
- [ ] Prepare formal drawings (if user figures need professional rendering)
- [ ] File with the patent office
- [ ] For utility model (CN): formal examination typically takes 6-8 months
- [ ] For invention patent: substantive examination may take 2-4 years

State: Write PATENT_STATE.json with phase: 5, status: "completed".

Key Rules

  • Never fabricate prior art references, patent numbers, or citations.
  • Claims must be supported by the specification (written description requirement).
  • Each jurisdiction has strict format requirements -- do not mix formats.
  • Utility model (实用新型) applies ONLY to CN jurisdiction and ONLY covers apparatus/device claims.
  • AUTO_PROCEED defaults to false -- patent applications require human review at every phase. Sub-skills inherit this flag: when AUTO_PROCEED=false, sub-skills present results and wait at their own internal checkpoints too.
  • The patent pipeline produces drafts for attorney review, not final filing documents.
  • Large file handling: if a Write operation fails, retry with Bash cat <<'EOF' heredoc.
  • Never include experimental results or empirical evaluations in the specification.
  • Consistent terminology is mandatory -- same word for the same concept throughout.
  • If mcp__codex__codex is not available (no OpenAI API key), skip external cross-model review and note it in the output. The pipeline must not fail due to missing reviewer access.

Composing with Other Workflows

The patent pipeline can start from multiple entry points:

User describes invention directly ──→ /patent-pipeline

/idea-discovery produces IDEA_REPORT.md ──→ /patent-pipeline (extract patentable aspects)

/research-refine produces FINAL_PROPOSAL.md ──→ /patent-pipeline (from refined research idea)

/auto-review-loop produces strong results ──→ /patent-pipeline (patent the method)

Acknowledgements

Built on the ARIS (Auto-claude-code-research-in-sleep) skill architecture. Patent writing principles adapted from MPEP (US), CN Patent Examination Guidelines (CN), and EPO Guidelines for Examination (EP).

Frequently asked questions

What does the Patent Pipeline AI skill do?

Full patent drafting pipeline from invention description to jurisdiction-formatted filing documents. Supports CN (CNIPA), US (USPTO), EP (EPO). Supports invention patents and utility models. Use when user says "写专利", "patent pipeline", "专利申请", "draft patent", "写权利要求书", or wants to draft a complete patent application.

Why use Patent Pipeline on TypingMind?

Because you install it once and use it with any model. Patent Pipeline is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Patent Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/patent-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Patent Pipeline?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Patent Pipeline?

As many as you like. As long as a model supports skills, you can use Patent Pipeline with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Patent Pipeline AI skill free?

Yes. It is published on GitHub by wanshuiyin under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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