Fast research sweep — arxiv, semantic scholar, github, web. Finds papers, scores relevance, extracts actionable insights, stores to wiki. Triggers on: research search, find papers, latest research, arxiv, what's new in, sweep papers, research sweep.
name research-search description Fast research sweep — arxiv, semantic scholar, github, web. Finds papers, scores relevance, extracts actionable insights, stores to wiki. Triggers on: research search, find papers, latest research, arxiv, what's new in, sweep papers, research sweep. version 1.0.0 tags ["research","arxiv","papers","knowledge","ingestion"] /research — Fast Research Sweep Find the latest research on a topic, score it for relevance, extract what you can BUILD with it, store the best finds. Usage /research <topic> # Sweep a topic, show top results /research <topic> --ingest # Sweep + store best finds to wiki /research <topic> --deep <arxiv-url> # Deep-read a specific paper /research --sweep # Run all topics from program.md /research --trending # What's hot this week in your areas On invoke Step 0: Load the research program Read atris/skills/research/program.md for: Active research topics (what to search for) Scoring criteria (what makes a paper relevant) Date window (default: last 6 months) Prior results from atris/skills/research/results.tsv Step 1: Multi-source search For the given topic, search ALL of these sources in parallel (use Agent tool for parallelism): Source A — arxiv API Run via Bash: python3 atris/skills/research/arxiv_search.py "<topic>" --after 2025-10-01 -- limit 20 Returns JSON array of papers with title, authors, abstract, date, url, categories. Source B — Semantic Scholar API Run via Bash: python3 atris/skills/research/scholar_search.py "<topic>" --after 2025-10-01 -- limit 20 Returns JSON array with title, authors, abstract, date, url, citation count, venue. Source C — Web search Use WebSearch tool: "<topic>" site:arxiv.org OR site:github.com 2025..2026 Source D — GitHub Use WebSearch tool: "<topic>" site:github.com stars:>100 pushed:>2025-10-01 Step 2: Deduplicate and rank Merge results from all sources. Deduplicate by title similarity. For each paper, score 1-10 on: Relevance : Does this directly apply to our research program? Recency : Published in the target date window? Actionability : Can we BUILD something with this? Not just theory? Novelty : Is this a new technique, or incremental on known work? Compute total = (relevance * 3 + actionability * 3 + recency * 2 + novelty * 2) / 10 Step 3: Present results Show a ranked table:
| # | Score | Title | Date | Key Insight | Source |
|---|---|---|---|---|---|
| 1 | 9.2 | ... | ... | ... | arxiv |
| 2 | 8.5 | ... | ... | ... | scholar |
| For the top 5, show: | |||||
| One-line insight | |||||
| : What's the actionable takeaway | |||||
| Applies to | |||||
| : Which of our projects/experiments this helps | |||||
| Build it | |||||
| : What we'd actually implement | |||||
| Step 4: Deep read (optional, on request or --ingest) | |||||
| For papers the user selects (or top 3 if --ingest): | |||||
| Use WebFetch to read the full arxiv abstract page | |||||
| If PDF: note the URL for manual reading, extract what you can from abstract + related work | |||||
| Extract: | |||||
| Core technique (one paragraph) | |||||
| Key results (numbers, benchmarks) | |||||
| How to implement at inference time (if applicable) | |||||
| Dependencies (what you need: fine-tuning? API access? special hardware?) | |||||
| Limitations the authors acknowledge | |||||
| Step 5: Store (if --ingest) | |||||
| Write each top paper to | |||||
| atris/wiki/research/<slug>.md | |||||
| : |
title: < paper title
source: <arxiv/scholar/github url> date: < publication date
relevance score: <1-10> last compiled: < today
tags: [ < topic tags
< Paper Title
Authors: ... Published: ... URL: ...
< one paragraph
< bullet points with numbers
< practical implementation notes
< which of our projects benefit
< what the authors say doesn ' t work
Update atris/wiki/index.md with the new pages. Step 6: Log Append to atris/skills/research/results.tsv : timestamp topic papers_found top_score top_paper source_breakdown Over time, this log shows which topics are producing the best finds and which sources are most useful. RL Integration The research program evolves: After each sweep, note which papers scored highest and from which source If a paper leads to a successful implementation (tracked via /storysim or /autoresearch), boost that topic's weight If a sweep produces nothing actionable, refine the search queries The program.md file is the "policy" — update it as you learn what works Rules Date filter is HARD. Do not include papers outside the configured window. Actionability > novelty. A mediocre paper you can build with beats a brilliant paper you can't. No summaries without sources. Every claim needs a URL. Prefer papers with code (GitHub links, "code available at..."). Don't deep-read everything. Score first, read the top 3-5. If a paper requires fine-tuning and the user only has API access, flag it clearly.