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 prior-art-searchgit 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/prior-art-search)<a href="https://agentmods.dev/skills/raja21068/autoresearch/prior-art-search"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/prior-art-search/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/prior-art-search"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/prior-art-search.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 | $0.00052 | $0.01292 |
| Opus 5 | $0.00026 | $0.00646 |
| Sonnet 5 | $0.00010 | $0.00258 |
| Haiku 4.5 | $0.00005 | $0.00129 |
Grade A, and why
prior-art-search 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
100% identical to prior-art-search — 4 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prior Art Search
Search patents and literature for prior art relevant to: $ARGUMENTS
Adapted from /research-lit for patent-specific searching.
Constants
MAX_PATENT_RESULTS = 20— Maximum patent documents to analyze in detailMAX_PAPER_RESULTS = 15— Maximum academic papers to analyze in detailSEARCH_YEARS = 10— How many years back to searchPATENT_DATABASES = "google-patents, espacenet"— Patent databases to search
Inputs
Read the invention description from:
$ARGUMENTSif it contains technical detailspatent/INVENTION_BRIEF.mdif it existsINVENTION_BRIEF.mdif it exists at project root
Shared References
Load ../shared-references/prior-art-databases.md for search strategy templates and IPC/CPC classification guidance.
Workflow
Step 1: Extract Search Concepts
From the invention description, identify:
- Core inventive concept: The primary technical contribution (1-2 sentences)
- Technical problem: What problem it solves
- Key technical features: 4-6 specific technical elements that define the invention
- IPC/CPC classes: Predict relevant classification codes (e.g., G06N, G06F)
Step 2: Patent Search
For EACH search concept, search via:
Google Patents (via WebSearch):
WebSearch: "site:patents.google.com [keywords]"
WebSearch: "[keywords] patent"
- Try primary keywords + technical problem keywords
- Search in English regardless of target jurisdiction
- For CN inventions, also search Chinese keywords via WebSearch
Espacenet (via WebFetch):
- WebFetch worldwide.espacenet.com/search results for key queries
- Search by predicted IPC/CPC classes
Assignee/Inventor Search:
- If known companies/universities work in this area, search their patent portfolios
- WebSearch: "[assignee name] patent [technical area]"
For each potentially relevant patent found:
- WebFetch the patent page to extract: title, abstract, representative claims, filing date, assignee, current status
- Record IPC/CPC classification codes
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 · 147 lines · 52 tokens per session scan A a3cd831e53c0
prior-art-search is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 1,292 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prior-art-search, differing in 4 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…
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…
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…
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…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…