paper-summarize

name	paper-summarize
description	Deep-reads and summarizes a specific research paper or whitepaper into structured intelligence
tags	["research","papers","summary","analysis","intelligence"]
agent	researcher
var	${var} is the paper URL or arxiv ID to summarize. Required. Example: "https://arxiv.org/abs/2401.12345" or "2401.12345"

paper-summarize

Priority: P2 (on-demand) Data sources: arxiv, Semantic Scholar, direct URL fetch Output: Structured summary in memory/research/summaries/

Instructions

You are executing the paper-summarize skill for the Researcher agent.

Step 1: Resolve Paper Source

Parse ${var} to determine the paper source:

• If it matches \d{4}\.\d{4,5} — it is an arxiv ID
• If it is a URL to arxiv — extract the ID
• If it is any other URL — fetch directly

For arxiv papers:

curl -s "http://export.arxiv.org/api/query?id_list={arxiv_id}"

For other URLs:

curl -sL "{url}" -o /tmp/paper_content.html

Use WebFetch if the URL requires JavaScript rendering.

Step 2: Extract Paper Metadata

Collect:

• Title
• Authors and affiliations
• Publication date
• Abstract
• References count
• Paper length (pages)

For arxiv, also fetch Semantic Scholar data:

curl -s "https://api.semanticscholar.org/graph/v1/paper/ArXiv:{arxiv_id}?fields=title,abstract,citationCount,influentialCitationCount,references,fieldsOfStudy,tldr,venue,year"

Step 3: Deep Analysis

Read the abstract and available text to produce:

1. Core Contribution (2-3 sentences): What is the paper's main claim or innovation?
2. Methodology (2-3 sentences): How did they approach the problem?
3. Key Results (bullet points): What did they find or prove?
4. Limitations (bullet points): What are the acknowledged or apparent weaknesses?
5. Relevance to Stoa (1-2 sentences): How does this relate to our trading/DeFi/agent domain?
6. Actionable Insights (bullet points): What can we do with this knowledge?

Step 4: Extract Key Figures and Data

Identify and describe:

• Key numerical results (performance metrics, benchmarks)
• Comparison tables (how this compares to prior work)
• Any formulas or algorithms that are directly implementable

Step 5: Map Related Work

From the references and Semantic Scholar data:

1. Identify the 3-5 most relevant cited papers
2. Note if any cited work is already in our memory/research/ archive
3. Flag papers we should read next (add to memory/research/reading-queue.json)

Step 6: Save Summary

Write to memory/research/summaries/{paper_id_or_slug}.json:

{
  "paper_id": "2401.12345",
  "title": "...",
  "authors": ["..."],
  "published": "2024-01-15",
  "url": "https://arxiv.org/abs/2401.12345",
  "summarized_at": "ISO-8601",
  "core_contribution": "...",
  "methodology": "...",
  "key_results": ["..."],
  "limitations": ["..."],
  "relevance_to_stoa": "...",
  "actionable_insights": ["..."],
  "key_data_points": {},
  "related_papers": [{"title": "...", "id": "...", "relevance": "..."}],
  "reading_time_estimate": "15 min",
  "complexity_level": "introductory | intermediate | advanced",
  "tags": ["defi", "mev", "solana"]
}

Post high-relevance summaries to analyst mesh.

Anti-Patterns

• Do NOT reproduce entire sections verbatim. Summarize in your own words.
• Do NOT skip the limitations section. Every paper has weaknesses.
• Do NOT rate a paper's relevance without explaining why.
• Do NOT attempt to parse binary PDF content. Work with HTML/text versions.

Exit Codes

• SKILL_OK — paper summarized successfully
• SKILL_PARTIAL — summary generated but some sections incomplete
• SKILL_FAIL — could not access or parse the paper

Output

Commit message format: researcher: paper-summarize — "{short_title}"