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-reviewgit 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-review)<a href="https://agentmods.dev/skills/raja21068/autoresearch/patent-review"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/patent-review/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-review"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/patent-review.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 | $0.00050 | $0.01624 |
| Opus 5 | $0.00025 | $0.00812 |
| Sonnet 5 | $0.00010 | $0.00325 |
| Haiku 4.5 | $0.00005 | $0.00162 |
Grade A, and why
patent-review 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
94% identical to patent-review — 14 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Patent Examiner Review via Codex MCP (xhigh reasoning)
Get a multi-round patent examiner review of the patent application based on: $ARGUMENTS
Adapted from /research-review. The reviewer persona is a patent examiner, not a paper reviewer.
Constants
REVIEWER_MODEL = gpt-5.4— Model used via Codex MCPREVIEW_ROUNDS = 2— Number of review roundsEXAMINER_PERSONA = "patent-examiner"— GPT-5.4 persona
Prerequisites
- Codex MCP Server configured:
claude mcp add codex -s user -- codex mcp-server
Inputs
patent/CLAIMS.md— all drafted claimspatent/specification/— all specification sectionspatent/figures/numeral_index.md— reference numeral mappingpatent/PRIOR_ART_REPORT.md— known prior artpatent/INVENTION_DISCLOSURE.md— invention structure
Workflow
Step 1: Gather Patent Context
Before calling the external reviewer, compile a comprehensive briefing:
- Read all claims (independent + dependent)
- Read specification sections (at least summary and detailed description)
- Read prior art report for context
- Identify: core inventive concept, claim scope, known prior art, target jurisdiction
Step 2: Round 1 — Full Examiner Review
Send to REVIEWER_MODEL via mcp__codex__codex with xhigh reasoning:
mcp__codex__codex:
config: {"model_reasoning_effort": "xhigh"}
prompt: |
You are a senior patent examiner at the [USPTO/CNIPA/EPO].
Examine this patent application and issue a detailed office action.
CLAIMS:
[all claims]
SPECIFICATION SUMMARY:
[key sections: title, technical field, background, summary, abstract]
PRIOR ART KNOWN:
[prior art references]
PATENTABILITY STANDARDS TO APPLY:
[US: 35 USC 101/102/103/112 | CN: Articles 22, 26 | EP: Articles 54, 56, 83, 84]
Please issue an office action covering:
1. CLAIM CLARITY (112(b)/Art 84):
- Are all terms definite?
- Any indefinite functional language?
- Antecedent basis issues?
2. WRITTEN DESCRIPTION (112(a)/Art 83 first para):
- Does the spec support ALL claim scope?
- Any claim elements without spec support?
3. ENABLEMENT (112(a)/Art 83):
- Can a POSITA practice the invention?
- Any missing algorithm/structure for functional claims?
4. NOVELTY (102/Art 54):
- Would any known reference anticipate any claim?
- Identify the closest single reference.
5. NON-OBVIOUSNESS (103/Art 56):
- Would any combination render claims obvious?
- What is the motivation to combine?
6. CLAIM SCOPE:
- Are independent claims broad enough to be commercially valuable?
- Do dependent claims provide meaningful fallback positions?
- Any claims that are too broad (likely rejected) or too narrow (not valuable)?
7. SPECIFICATION QUALITY:
- Language issues (subjective terms, relative terms, result-to-be-achieved)
- Reference numeral consistency
- Missing embodiments
Format your response as a formal office action with:
- GROUNDS OF REJECTION for each issue (cite statute)
- SUGGESTED AMENDMENTS for each issue
- OVERALL PATENTABILITY SCORE: 1-10
Be rigorous and specific. This is a real examination.
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 · 202 lines · 50 tokens per session scan A 0542b46a17d0
patent-review is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,624 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to patent-review, differing in 14 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…