Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jaccen/Awesome-Gaussian-Skills --skill patent-software-ipgit clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/patent-software-ip)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/patent-software-ip"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/patent-software-ip/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/patent-software-ip"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/patent-software-ip.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.04201 |
| Opus 5.5 | $0.00042 | $0.01680 |
| Sonnet 5.5 | $0.00021 | $0.00840 |
| Haiku 4.5 | $0.00010 | $0.00420 |
Grade A, and why
patent-software-ip scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Correction
Phase 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 |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago Changed · +15 lines 6ca42e672d5a
- 13d ago Changed 83e5fd5ea627
- 19d ago Changed 020bc96e375e
- 1mo ago Changed 0fea4f125ffc
- 1mo ago Changed e865eaeadd32
- 1mo ago Changed 8bb61f7dcd7c
- 1mo ago Changed · +4 lines 0437bdfa58bc
- 2mo ago First seen · 307 lines · 104 tokens per session scan A 5313a8d58654
patent-software-ip is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (176 stars, last pushed yesterday), licensed Apache-2.0. It adds 104 tokens to every session and 4,201 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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