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 raja21068/AutoResearch --skill patent-pipelinegit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/patent-pipeline)<a href="https://agentmods.dev/skills/raja21068/autoresearch/patent-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/patent-pipeline/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/raja21068/autoresearch/patent-pipeline"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/patent-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00081 | $0.03571 |
| Opus 5 | $0.00041 | $0.01785 |
| Sonnet 5 | $0.00016 | $0.00714 |
| Haiku 4.5 | $0.00008 | $0.00357 |
Grade A, and why
patent-pipeline 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 8d 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.
This is a copy
95% identical to patent-pipeline — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 =
invention—invention(发明专利, 20 year protection) orutility_model(实用新型, CN only, 10 year protection, apparatus claims only). Override via argument. - REVIEWER_MODEL =
gpt-5.4— 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
trueonly 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.markdownfor review,docxfor filing-ready.
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.
- 8d ago First seen · 345 lines · 81 tokens per session scan A e4daee340fbe
patent-pipeline is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 81 tokens to every session and 3,571 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to patent-pipeline, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…