| 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:
# Research Sweep: <topic>
## Date: YYYY-MM-DD | Sources: arxiv, scholar, web, github | Papers found: N
| # | 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:** ...
## Core Technique
<one paragraph>
## Key Results
<bullet points with numbers>
## How to Use (Inference-Time)
<practical implementation notes>
## Applies To
<which of our projects benefit>
## Limitations
<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.
