Paper to Chinese Patent Drafter
Yuan1z0825/nature-skills
Drafts Chinese invention patent applications and technical disclosures from research papers or inventor materials, tying each claim feature to source evidence.
Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs.
Install Patent Software Ip
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ip --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/patent-software-ip .claude/skills/patent-software-ip && rm -rf skills-srcUse ~/.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/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .claude/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ipType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ip --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/patent-software-ip .agents/skills/patent-software-ip && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .agents/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ip --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/patent-software-ip .cursor/skills/patent-software-ip && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .cursor/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jaccen/Awesome-Gaussian-Skills.git --path skills/patent-software-ip--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ip --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/patent-software-ip .gemini/skills/patent-software-ip && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .gemini/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ipInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/patent-software-ip .github/skills/patent-software-ip && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .github/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jaccen/Awesome-Gaussian-Skills patent-software-ip --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/patent-software-ip .opencode/skills/patent-software-ip && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "patent-software-ip" agent skill from https://github.com/jaccen/Awesome-Gaussian-Skills/tree/main/skills/patent-software-ip into .opencode/skills/patent-software-ip/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "patent-software-ip", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
patent-software-ip- GitHub stars
- 161
- Token cost
- ~4.3k tokens
- SKILL.md length
- 1,773 words
- Files
- 4 (incl. references)
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs.
- Works in 6 steps: Method + System claims in pairs → Independent: preamble (prior art) +… → Dependent: "according to claim X..."… → …
- : drafting CN patents from AI code/docs
- SKILL.md covers Triggers, Overall Flow, Phase A: Requirement Diagnosis and AI Domain Taxonomy, plus 11 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Patent Software Ip is an agent skill from jaccen/Awesome-Gaussian-Skills. Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs. Covers 7 AI domains + big data, 11 claim templates, auto domain detection, desensitization, prior-art search, and self-check. Use when: drafting CN patents from AI code/docs, generating software copyright materials, prior-art search for AI inventions, 专利撰写/软件著作权/AI知识产权/脱敏处理.
Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/ai-patent-claims-guide.md`, `references/ai-patent-special.md` and `references/ai-software-copyright-guide.md`).
It sits in Legal & Compliance, covering Intellectual property. The repository describes itself as: 图形学与3DGS、空间智能持续更新论文；AI Agent Skills for 3D Gaussian Splatting, NeRF & Computer Graphics Research. 800+ methods, 25categories, 12skills. OpenClaw / Claude Code compatible. The licence is Apache-2.0.
When your agent uses it
- : drafting CN patents from AI code/docs
- Generating software copyright materials
- Prior-art search for AI inventions
- 专利撰写/软件著作权/AI知识产权/脱敏处理
Example prompts
- “/patent-software-ip”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Method + System claims in pairs
- Independent: preamble (prior art) + "characterized by" (essential features)
- Dependent: "according to claim X..." with further limitation
- Every step must link to system component
- Avoid functional limitation; prefer structural/step-based description
- Quantify effects where possible ("improves accuracy by X%", "reduces latency to Y ms")
What it can do on your machine
Read from SKILL.md and the folder at commit e569b20. 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.
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 Software Ip loads about 4.3k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 1,773 words of instructions outside code blocks.
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 jaccen/Awesome-Gaussian-Skills at commit e569b20, republished under its Apache-2.0 licence (© jaccen). 1,773 words, ~4,333 tokens.
Download SKILL.mdSave it as
.claude/skills/patent-software-ip/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- patent-software-ip
- description
- Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs. Covers 7 AI domains + big data, 11 claim templates, auto domain detection, desensitization, prior-art search, and self-check. Use when: drafting CN patents from AI code/docs, generating software copyright materials, prior-art search for AI inventions, 专利撰写/软件著作权/AI知识产权/脱敏处理.
- license
- Apache-2.0
- user-invocable
- true
- metadata.version
- 1.4.1
- metadata.author
- jaccen
- metadata.tags
- patent, software-copyright, ip, ai, big-data, 3d-vision, generative-ai, embodied-ai, nlp, rag, ai-engineering, ai-safety
- metadata.when_to_use
- Draft CN invention patents from AI code or docs, Generate software copyright materials, Perform prior-art search for AI inventions, Desensitize project code…
Patent & Software Copyright Generation (AI + Big Data)
Generate CNIPA invention patent documents or CPCC software copyright materials from AI / big-data project code, design docs, and research papers.
Covers 7 AI domains + Big Data (23 sub-directions), 11 claim templates.
Full version (Chinese, with Word/PPT output): see AI-Copyright-Skill project.
Triggers
patent / claims / specification / software copyright / disclosure / IP application / paper-to-patent / /patent-software-ip
Overall Flow
Phase A Requirement Diagnosis -> path + domain classification + risk level
Phase B Project Analysis -> auto-detect domain + extract key technical points
Phase C Generation (branch by path)
C1 Patent: prior art search -> claims (11 templates) -> specification -> abstract -> self-check
C2 Software Copyright: manual -> source code doc -> self-check
Phase D Iterative CorrectionPhase A: Requirement Diagnosis
Confirm: path (patent/copyright/both), tech topic, applicant/inventor info, existing materials.
Auto domain classification (see Section "AI Domain Taxonomy" below).
Gate: 3-5 line diagnosis summary including domain + risk level.
AI Domain Taxonomy
| Domain | Sub-directions | High-Risk Flags |
|---|---|---|
| D1 Perceptual Intelligence | 2D vision, 3D vision, multi-sensor fusion | 3D vision: bind 4-stage pipeline |
| D2 Cognition & Language | NLP, multimodal LLM, RAG, knowledge graph | RAG: show full 5-stage chain |
| D3 Generative AI | Diffusion, LLM text gen, cross-modal gen, AIGC watermark | Must bind condition injection method; pure content gen = rejected |
| D4 Decision & Interaction | Embodied AI, reinforcement learning, multi-agent | Must bind sensor + actuator; RL: bind reward to concrete task |
| D5 AI Engineering | Training/fine-tuning, inference deployment, data engineering, edge IoT | Training: bind to specific model architecture; inference: bind to hardware |
| D6 AI Safety & Governance | Adversarial robustness, watermark/tracing, privacy, alignment, 3DGS provenance | Need concrete technical measure, not policy-level description. 3DGS IP: reference GaussTrace (ICML 2026) for evidence-driven provenance graphs |
| D7 Industry Applications | Autonomous driving, industrial, medical, financial, AI4Science | Must bind data processing means; financial: bind to data analysis |
| D8 Big Data | Distributed computing, data pipeline, stream processing, data quality, real-time analytics | Must bind to specific application scenario; pure platform = rejected |
Phase B: Project Analysis
B.1 Auto-Detection Decision Tree
Source files -> domain mapping:
| Key file | Detected domain |
|---|---|
model.py, unet.py, vae.py | D3 Generative AI |
train.py, finetune.py | D5 AI Engineering (Training) |
inference.py, triton_serve.py, onnx_export.py | D5 AI Engineering (Inference) |
render.py, gaussian.py, splat.py | D1 3D Vision |
llm.py, chat.py, rag_chain.py | D2 NLP / RAG |
robot.py, vla.py, env.py | D4 Embodied AI |
reward.py, ppo.py | D4 Reinforcement Learning |
watermark.py, embed_watermark.py | D6 AI Safety / Watermark |
spark_job.py, flink_job.py, kafka_consumer.py | D8 Big Data |
etl.py, data_pipeline.py, feature_store.py | D8 Big Data (Data Engineering) |
stream.py, realtime_analytics.py | D8 Big Data (Streaming) |
dataset.py, dataloader.py | D5 AI Engineering (Data) |
privacy.py, dp_train.py | D6 AI Safety (Privacy) |
config.yaml, pipeline.py + langchain | D2 RAG / Agent |
Also detect 6 industry contexts: medical, financial, autonomous driving, industrial, smart city, education.
B.2 Technical Points Extraction
Priority: model definition -> training/inference -> domain-specific core -> papers/design docs -> README.
Output: Key Points List (innovations, scheme skeleton, key params, distinctions, quantifiable effects, domain classification).
Gate: Present key points list for user confirmation.
Phase C1: Patent Application
C1.1 Prior Art Search
Online search 2-3 rounds: CNIPA patent DB, Google Patents, arXiv. Each result: source ID, scheme summary, limitations.
CPC suggestions by domain:
- D1 3D Vision: G06T 7/50, G06T 17/00
- D2 NLP/RAG: G06F 40/30, G06N 3/08
- D3 Generative AI: G06N 3/045, G06T 13/00
- D4 Embodied: G05B 19/00, B25J 9/16
- D5 AI Engineering: G06N 3/084
- D6 AI Safety: G06F 21/60
- D7 Industry: varies by sector
- D8 Big Data: G06F 16/245, G06F 16/903
C1.2 Claims (11 Templates)
Structure: Method (1 independent + 3-8 dependent) + System (1 independent + 3-8 dependent) + Storage Medium (1 independent).
Template selection by domain:
| Template | Domain | Independent claim skeleton |
|---|---|---|
| T1 Model Architecture | D1/D2/D5 | Predefined network -> layer composition -> feature extraction -> output |
| T2 3D Vision | D1 3D | Capture -> sparse reconstruction -> dense optimization -> rendering (expand formula) |
| T3 Training Strategy | D5 | Data construction -> model initialization -> loss design -> optimization -> convergence |
| T4 Multimodal Fusion | D1/D2 | Multi-modal input -> modality-specific encoding -> cross-modal alignment -> fused output |
| T5 RAG Pipeline | D2 | Parse -> retrieve -> rerank -> reconstruct -> generate |
| T6 Diffusion Model | D3 | Noise scheduling -> condition injection (specify: cross-attention/adapter/ControlNet) -> denoising -> decode |
| T7 Agent | D2/D4 | Environment perception -> task decomposition -> tool selection -> execution -> feedback |
| T8 Embodied Intelligence | D4 | Sensor input -> perception -> planning -> actuator output + safety constraint (dependent) |
| T9 Inference Optimization | D5 | Model loading -> computation graph optimization -> kernel fusion -> output |
| T10 Big Data Processing | D8 | Data ingestion -> distributed processing (specify: Spark/Flink/MapReduce) -> aggregation -> storage/output |
| T11 Data Engineering & Quality | D8 | Data collection -> quality assessment -> anomaly detection -> cleaning -> feature extraction -> storage |
Drafting rules (all domains):
- Method + System claims in pairs
- Independent: preamble (prior art) + "characterized by" (essential features)
- Dependent: "according to claim X..." with further limitation
- Every step must link to system component
- Avoid functional limitation; prefer structural/step-based description
- Quantify effects where possible ("improves accuracy by X%", "reduces latency to Y ms")
C1.3 Specification
5-chapter: Tech Field -> Background (prior art + defects) -> Invention Content (problem + scheme + effects, quantified) -> Figure Description -> Specific Embodiments.
Desensitization:
- Dataset name -> "preset dataset"
- Parameter count -> "preset-scale model"
- Hardware -> "graphics processor" / "distributed computing node"
- Training duration -> "preset period"
- Framework -> "DL framework" / "distributed computing framework"
- API -> "remote interface"
- Company -> "institution"
- Specific values -> ranges
Figures (mermaid flowchart TB/LR): System architecture + method flow + domain-specific pipeline (training/rendering/data pipeline/stream topology/etc.).
C1.4 Abstract
<=300 chars. Tech domain + core scheme + main effect. No commercial terms.
C1.5 Self-Check
- Independent claim contains all essential features
- Dependent claims correctly reference
- Method + System + Medium triple complete
- Specification sufficiently disclosed (enabling)
- Embodiments cover all claim features
- Beneficial effects quantified
- Terminology consistent throughout
- Abstract corresponds to claim 1
- Desensitization complete (no company/person/business name leak)
- Figure numbering consistent
- Domain-specific checks passed (see below)
Domain-specific self-check:
| Domain | Extra checks |
|---|---|
| D1 3D Vision | Rendering formula in claim? 4-stage pipeline? |
| D2 NLP/RAG | Full 5-stage RAG chain? Specific embedding model? |
| D3 Generative AI | Condition injection method specified? Not pure content gen? |
| D4 Embodied | Sensor + actuator bound in every step? Safety dependent claim? |
| D5 AI Engineering | Specific model architecture? Hardware binding for inference? |
| D6 AI Safety | Concrete technical measure? Not policy-level? |
| D7 Financial/Medical | Data processing means bound? Not pure business method? |
| D8 Big Data | Specific application scenario bound? Not pure platform? Distributed topology described? |
Phase C2: Software Copyright
C2.1 Software Manual (10-15 pages, >=6 screenshots)
Structure: Introduction (env + capability) -> Installation (env + weights + config) -> Functions (core + data + API + monitoring) -> Non-functional -> FAQ.
Templates by domain:
- General AI: standard template
- 3D Vision: add rendering/visualization section
- Generative AI: add sampling/inference section
- Embodied AI: add sensor/hardware integration section
- Big Data: add data pipeline/deployment section (distributed topology, cluster config, streaming topology diagram)
C2.2 Source Code Document (front 30 + back 30 pages, >=50 lines/page)
File priority by domain:
| Domain | Required files | Domain-specific required |
|---|---|---|
| D1 3D Vision | model.py, train.py, inference.py, render.py | render.py |
| D2 NLP/RAG | model.py, train.py, inference.py, retriever.py | retriever.py |
| D3 Generative AI | model.py, train.py, inference.py, generate.py | generate.py |
| D4 Embodied | model.py, train.py, inference.py, control.py, env.py | control.py |
| D5 AI Engineering | model.py, finetune.py, export.py, deploy.py | finetune.py |
| D6 AI Safety | model.py, watermark.py, adv_train.py | watermark.py |
| D8 Big Data | pipeline.py, etl.py, stream.py, config.yaml | pipeline.py |
<3000 lines: submit all; >3000: front 1500 + back 1500 by priority.
Desensitization: Remove API keys, absolute paths, internal addresses, personal info, hardware models, cloud URLs, DB passwords. Retain algorithm comments.
Show full SKILL.md (694 more words)Show less
C2.3 Self-Check
- Pages >= 15
- Screenshots >= 6
- Feature coverage complete
- Non-technical description for reviewers
- Code pages with >= 50 lines/page
- Name consistency
- No secret leaks
Knowledge Index
Deep-dive reference files for domain-specific patent writing rules, claim templates, and software copyright guides.
| File | Sections | Key Content |
|---|---|---|
| references/ai-patent-claims-guide.md | 11 claim templates (T1-T14) | Full legal claim text per template: method/system/medium triples with dependent claims; Big Data T10-T14 included |
| references/ai-patent-special.md | Patentability framework, 8 risk domains, CPC codes, desensitization rules | AI+Big Data patentability risk assessment; domain mapping; figure requirements; industry desensitization; CPC classification (7.1-7.7); 9-domain quick reference |
| references/ai-software-copyright-guide.md | Type detection, source file priority, 5 domain templates, FAQ | Decision tree for 10+ project types; source code priority by domain; Big Data dedicated template (section 3.5); desensitization checklist; common pitfalls |
3DGS Patentable Innovation Examples (CVPR 2026)
| Innovation | Method | Patentable Aspect | Claim Template |
|---|---|---|---|
| 3DGS-Physics Engine Bridge | RAF (Representation Abstraction Framework) | Bidirectional mapping between Gaussian primitives and physics simulation state; claim the abstraction layer + synchronization protocol | T2 (3D Vision) + T8 (Embodied) |
| Elastic Eigenmode Deformation | FreeForm | Eigenmode-based elastic deformation for Gaussians; claim the modal analysis pipeline + real-time deformation update | T2 (3D Vision) |
CVPR 2026 accepted 116 3DGS-related papers, creating a surge of patentable innovations. When filing patents for 3DGS methods:
- The 4D reconstruction wave (D4RT and followers) creates IP opportunities in temporal Gaussian representations
- Physics-integrated rendering (FieryGS, RAF) opens claims for simulation-rendering bridges
- Articulated 3DGS methods generate IP around interaction primitives and joint representations
- File early -- the dense publication cohort means similar innovations may appear concurrently
Knowledge base: 872 methods across 23 categories (updated for v0.8.4 cycle).
Phase D: Iterative Correction
Identify -> Locate -> Targeted fix -> Save as v{N} -> Re-run affected self-check items only. Do NOT re-run full pipeline.
Output
outputs/{case-id}/
patent/ claims.md + specification.md + abstract.md + full.md
software-copyright/ manual.md + source_code.mdProhibitions: No skill name/repo path/disclaimers in deliverables. No self-check section in body. No fabricated patent numbers/links. No "approximately" in claims. No commercial terms in abstract.
Quick Reference: 8 High-Risk Rejection Patterns
| Pattern | Why rejected | Fix |
|---|---|---|
| Pure content generation (no condition injection) | "Intellectual activity rules" | Specify cross-attention/adapter/ControlNet in claims |
| Financial AI without data processing means | "Business method" | Bind to specific feature engineering + model architecture |
| Embodied AI without sensor/actuator binding | "Pure algorithm" | Add "executed via LiDAR module" + "motor controller" |
| RAG without full pipeline | "Insufficient disclosure" | Show all 5 stages in method claim |
| Big Data platform without application | "Abstract idea" | Bind to specific scenario (e.g., real-time traffic analytics) |
| RL without reward function | "Insufficient disclosure" | Include reward computation formula |
| AI watermark without robustness test | "Insufficient technical effect" | Add adversarial/noise/compression robustness claim |
| Medical AI without clinical validation | "Insufficient enablement" | Add evaluation on specific dataset with clinical metrics |
Red Lines
The following are categorical prohibitions. Violating any of these invalidates the output:
- No invented data: Never fabricate patent claims, prior art references, or legal requirements not in the loaded reference files. If a value is not found, write "data not available" or "N/A".
- No hallucinated citations: Never invent paper titles, authors, DOIs, arXiv IDs, patent numbers, or venue names. Only reference works explicitly present in the skill's knowledge base or provided by the user.
- No silent speculation: If you are uncertain about a technical or legal detail, explicitly flag it with "[UNCERTAIN]" rather than presenting it as fact.
- No method misattribution: Do not assign features, results, or mechanisms from one method to another. Each method's data is specific to that method.
- No legal advice: Never provide definitive legal advice. All patent and copyright output is draft material requiring review by a qualified attorney.
Related Skills
- 3dgs-method-compare — Method comparison (use for prior art analysis and novelty assessment)
- 3dgs-paper-reader — Paper analysis (use for extracting patentable contributions)
- 3dgs-code-reviewer — Code review (use for identifying technical innovations in code)
- 3dgs-engineering-guide — Deployment guidance (use for documenting industrial applications)
Guardrail: Do Not Apply From Memory
Do NOT try to apply the logic, method data, bug patterns, or technical details described in this skill from memory. Always read the SKILL.md and referenced files from disk before producing any output. The knowledge base is updated frequently; stale memory may produce outdated, inaccurate, or fabricated results.
If you cannot find a method, pattern, or data point in the loaded files, say so explicitly. Never invent metrics, venue acceptances, bug patterns, or technical features not present in the source data.
© jaccen, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in skills/patent-software-ip of jaccen/Awesome-Gaussian-Skills.
- SKILL.md
- references/ai-patent-claims-guide.md
- references/ai-patent-special.md
- references/ai-software-copyright-guide.md
Open the folder on GitHubat commit e569b20
Compare with similar skills
Patent Software Ip 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Patent Software Ip this skilljaccen/Awesome-Gaussian-Skills | 161 | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | |
| Paper to Chinese Patent DrafterYuan1z0825/nature-skills | 47k | 1 repos | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Paper To Cn Patentsnipp-zha/Paper-to-patent-Skill | 106 | 1 repos | ~959 | Automated safety check: Pass | None | |
| Patent Examinegfodor/legal-skills | 393 | — | ~4.8k | Automated safety check: Pass | GPL-3.0 | |
| Patent Auditgfodor/legal-skills | 393 | — | ~2.9k | Automated safety check: Pass | GPL-3.0 | |
| Replica BrandJakeschincariol/replica-skill | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT |
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Iteratively examine and revise a draft U.S. An agent skill from gfodor/legal-skills.
Patent Audit
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Adversarially pressure-test a draft or pending U.S. An agent skill from gfodor/legal-skills.
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Questions about Patent Software Ip
What does Patent Software Ip do?
Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs. Patent Software Ip is an agent skill from jaccen/Awesome-Gaussian-Skills. Generate CN patent docs (claims, specification, abstract) and software copyright materials from AI/big-data project code or docs.
When should I use Patent Software Ip?
Patent Software Ip fits situations like: : drafting CN patents from AI code/docs; generating software copyright materials; prior-art search for AI inventions; 专利撰写/软件著作权/AI知识产权/脱敏处理.
How do I install Patent Software Ip in Claude Code?
Run `npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a claude-code`. Or copy the skill folder (skills/patent-software-ip in jaccen/Awesome-Gaussian-Skills) into .claude/skills/patent-software-ip in your project. Claude Code loads it when a task matches its description.
How do I install Patent Software Ip in Codex?
Run `npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -a codex`. Or copy the skill folder (skills/patent-software-ip in jaccen/Awesome-Gaussian-Skills) into .agents/skills/patent-software-ip in your project. Codex loads it when a task matches its description.
Can I use Patent Software Ip 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 jaccen/Awesome-Gaussian-Skills --skill patent-software-ip -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-software-ip, .gemini/skills/patent-software-ip, .github/skills/patent-software-ip and .opencode/skills/patent-software-ip in your project.
What does Patent Software Ip need to run?
SKILL.md names no scripts, command-line tools or credentials: Patent Software Ip is instructions for the agent only.
Does Patent Software Ip 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 Software Ip 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 Software Ip use?
Patent Software Ip is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Patent Software Ip use?
About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 14k tokens, read only when the agent opens those files.
What are the alternatives to Patent Software Ip?
Skills that share tags, products or a category with Patent Software Ip: 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 Software Ip?
jaccen (a GitHub user) maintains it in jaccen/Awesome-Gaussian-Skills, which has 161 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 9, 2026.
Source: jaccen/Awesome-Gaussian-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.