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Agent skill

Patent Continuous Learning

by LeoYeAI in LeoYeAI/openclaw-master-skills

Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.

MITAuto-check passedLegal & Compliance

Install Patent Continuous Learning

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill patent-continuous-learning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills patent-continuous-learning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/patent-professional-agents/skills/continuous-learning .claude/skills/patent-continuous-learning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
patent-continuous-learning
GitHub stars
2.2k
Token cost
~1.8k tokens
SKILL.md length
235 words
Files
1
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.

  • Tasks that involve Intellectual property
  • SKILL.md covers Trigger Conditions, Core Concept: Patent Instinct, Patent Instinct Types and Confidence Evolution, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Patent Continuous Learning is an agent skill from LeoYeAI/openclaw-master-skills. Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Legal & Compliance, covering Intellectual property. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve Intellectual property

Example prompts

  • “/patent-continuous-learning”

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Patent Continuous Learning loads about 1.8k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 235 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 235 words, ~1,754 tokens.

Download SKILL.mdSave it as .claude/skills/patent-continuous-learning/SKILL.md (or your agent's skills folder).
name
patent-continuous-learning
description
Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.
origin
professional-patent-agents
version
1.0.0

Patent Continuous Learning Skill

Automatically extract reusable patterns from patent drafting sessions to form a patent knowledge base.

Trigger Conditions

  • After patent drafting is complete (patent-auditor review passed)
  • When search strategy is particularly effective
  • When user corrects writing style
  • When new writing techniques or patterns are discovered
  • When user provides access to patent database APIs
  • When new patent search skills are found on ClawHub

Core Concept: Patent Instinct

A Patent Instinct is an atomic learning unit that records a specific patent-related experience:

yaml
---
id: prefer-quantified-effect
trigger: "When writing technical effects"
confidence: 0.8
domain: "patent-writing"
source: "session-observation"
scope: global
---

# Prefer Quantified Technical Effects

## Trigger Condition
When writing the "Advantages Over Prior Art" section of a patent

## Action
Use quantified data to describe technical effects, such as:
- Efficiency improved by XX%
- Latency reduced by XXms
- Success rate improved by XX%

## Evidence
- 2026-03-19: User corrected "high efficiency" to "efficiency improved by 30%"
- 2026-03-18: Audit recommendation to add quantified data

Patent Instinct Types

TypeDescriptionScope
keyword-strategyEffective search keyword combinationsproject
writing-patternWriting techniques and sentence patternsglobal
tech-descriptionTechnical description patternsproject
claim-structureClaim structure patternsglobal
search-tacticSearch platform usage tipsglobal
error-avoidanceCommon error avoidanceglobal
api-recommendationPatent database API recommendationsglobal
skill-discoveryClawHub skill discovery patternsglobal

Confidence Evolution

ScoreMeaningBehavior
0.3TentativeSuggest but don't enforce
0.5MediumApply when relevant
0.7StrongAuto-apply
0.9CertainCore behavior

Confidence Increase:

  • Pattern observed repeatedly
  • User confirms effectiveness
  • Audit passed

Confidence Decrease:

  • User explicitly corrects
  • Causes problems

Learning Flow

Patent drafting session
      |
      | Observe key events
      v
+------------------------------------------+
|  observations/                           |
|   - Successful search strategies          |
|   - User correction records               |
|   - Audit feedback                        |
|   - Newly discovered patterns             |
+------------------------------------------+
      |
      | Extract instincts
      v
+------------------------------------------+
|  instincts/                              |
|   - keyword-strategy/  (project scope)    |
|   - writing-pattern/   (global scope)     |
|   - tech-description/  (project scope)    |
+------------------------------------------+
      |
      | /evolve clustering
      v
+------------------------------------------+
|  evolved/                                |
|   - skills/patent drafting enhanced skill |
|   - templates/reusable templates          |
+------------------------------------------+

Commands

CommandDescription
/patent-learnExtract patent instincts from current session
/patent-instinctsDisplay learned patent instincts
/patent-evolveCluster related instincts into skills

Directory Structure

patent/
├── learning/
│   ├── observations.jsonl     # Observation records
│   ├── instincts/
│   │   ├── global/            # Global instincts
│   │   │   ├── prefer-quantified-effect.yaml
│   │   │   └── avoid-complete-code.yaml
│   │   └── projects/
│   │       └── project-name/  # Project scope
│   │           ├── keyword-strategy.yaml
│   │           └── tech-description.yaml
│   └── evolved/
│       ├── skills/
│       └── templates/

Example: Auto-learned Instincts

Patent Database API Recommendation
yaml
---
id: recommend-patent-database-api
trigger: "When starting patent prior art search"
confidence: 0.9
domain: "api-recommendation"
scope: global
---

# Recommend Patent Database APIs for Professional Search

## Trigger Condition
When user requests patent prior art search and default channels may not be sufficient.

## Action
1. Ask user about available patent database APIs
2. Recommend appropriate APIs based on search needs:
   - Global search: Google Patents, Lens.org
   - US patents: USPTO, PatentsView
   - European patents: EPO Espacenet
   - Chinese patents: CNIPA
   - International: WIPO
3. Check ClawHub for patent search skills: `clawhub search patent`
4. Use installed skills if available

## Evidence
- 2026-03-19: User feedback that default channels are not accurate enough for patent search
- Patent prior art search requires professional patent database access
- ClawHub may have specialized patent search skills
Search Keyword Strategy
yaml
---
id: keyword-device-pairing
trigger: "When searching device pairing patents"
confidence: 0.85
domain: "keyword-strategy"
scope: project
project: example-project
---

# Device Pairing Search Keywords

## Keyword Combinations
- Primary keywords: device, terminal, pairing, connection
- Combination methods: `device pairing`, `terminal quick connection`
- Platform preference: Google Patents (English), AMiner (Academic)

## Evidence
- 2026-03-18: Found 5 highly relevant references using this combination
- Confidence increased from 0.5 to 0.85
Writing Technique
yaml
---
id: avoid-complete-code
trigger: "When writing patent embodiments"
confidence: 0.95
domain: "writing-pattern"
scope: global
---

# Avoid Complete Code

## Rule
Patent documents should not contain complete executable code. Use instead:
- Algorithm pseudocode
- Flowcharts
- Functional module descriptions

## Evidence
- 2026-03-17: Audit found complete code, recommended removal
- 2026-03-18: User confirmed this rule
- Verified across multiple patents

Integration into Patent Workflow

Auto-trigger learning in all three scenarios:

Scenario 1: User Idea → Drafting
After patent-auditor review passes
      |
      | Check for new patterns learned
      v
patent-continuous-learning extracts instincts
Scenario 2: User Draft → Optimization
User correction or audit recommendation
      |
      | Record effective improvements
      v
patent-continuous-learning updates instincts
Scenario 3: Agency Feedback
Targeted optimization successful
      |
      | Record effective differentiation descriptions
      v
patent-continuous-learning updates instincts

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/patent-professional-agents/skills/continuous-learning of LeoYeAI/openclaw-master-skills.

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Patent Continuous Learning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Patent Continuous Learning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Patent Continuous Learning this skillLeoYeAI/openclaw-master-skills2.2k—~1.8kAutomated safety check: PassMIT
Paper to Chinese Patent DrafterYuan1z0825/nature-skills47k1 repos~1.1kAutomated safety check: PassApache-2.0
Paper To Cn Patentsnipp-zha/Paper-to-patent-Skill1061 repos~959Automated safety check: PassNone
Patent Examinegfodor/legal-skills393—~4.8kAutomated safety check: PassGPL-3.0
Patent Auditgfodor/legal-skills393—~2.9kAutomated safety check: PassGPL-3.0
Replica BrandJakeschincariol/replica-skill1.2k—~1.1kAutomated safety check: PassMIT

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Categories

Questions about Patent Continuous Learning

What does Patent Continuous Learning do?

Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base. Patent Continuous Learning is an agent skill from LeoYeAI/openclaw-master-skills. Automatically extract reusable patterns from patent drafting sessions, including keyword strategies, writing techniques, and search methods, to build an accumulative patent knowledge base.

When should I use Patent Continuous Learning?

Patent Continuous Learning fits situations like: tasks that involve Intellectual property.

How do I install Patent Continuous Learning in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill patent-continuous-learning -a claude-code`. Or copy the skill folder (skills/patent-professional-agents/skills/continuous-learning in LeoYeAI/openclaw-master-skills) into .claude/skills/patent-continuous-learning in your project. Claude Code loads it when a task matches its description.

How do I install Patent Continuous Learning in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill patent-continuous-learning -a codex`. Or copy the skill folder (skills/patent-professional-agents/skills/continuous-learning in LeoYeAI/openclaw-master-skills) into .agents/skills/patent-continuous-learning in your project. Codex loads it when a task matches its description.

Can I use Patent Continuous Learning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill patent-continuous-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/patent-continuous-learning, .gemini/skills/patent-continuous-learning, .github/skills/patent-continuous-learning and .opencode/skills/patent-continuous-learning in your project.

What does Patent Continuous Learning need to run?

SKILL.md names no scripts, command-line tools or credentials: Patent Continuous Learning is instructions for the agent only.

Does Patent Continuous Learning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Patent Continuous Learning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Patent Continuous Learning use?

Patent Continuous Learning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Patent Continuous Learning use?

About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Patent Continuous Learning?

Skills that share tags, products or a category with Patent Continuous Learning: Paper to Chinese Patent Drafter (Yuan1z0825/nature-skills, 47k stars), Paper To Cn Patent (snipp-zha/Paper-to-patent-Skill, 106 stars), Patent Examine (gfodor/legal-skills, 393 stars) and Patent Audit (gfodor/legal-skills, 393 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Patent Continuous Learning?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.