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Transform research papers into interactive AI agents for exploration
Passed 8 of 23 test cases with the skill, and 15 of 23 without it.
1 fixed15 passed either way5 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Before executing a full multi-step extraction on a 50-page economics paper, which specific sections should be analyzed first to evaluate whether full parsing into an interactive agent is worthwhile?
| Checked | Without skill | With skill |
|---|---|---|
| Advises starting with the abstract and conclusion sections | Missed | Passed |
The instructions your model reads when the skill loads.
A skill for transforming published research papers into interactive AI agents that can answer questions, explain methodology, and help replicate findings. Based on Paper2Agent (2K stars), this skill guides the agent through extracting structured knowledge from academic papers and creating conversational interfaces for deep exploration.
Traditional paper reading is linear and passive. Paper-to-Agent converts this into an active, queryable experience. By parsing a paper's structure, extracting key claims, methodology details, and results, the agent becomes an expert on that specific paper, ready to answer follow-up questions, explain complex sections, and connect findings to the broader literature.
This approach is especially valuable for interdisciplinary researchers who need to quickly understand papers outside their primary expertise, for journal clubs seeking deeper discussion, and for students learning to critically evaluate published research.
The agent should follow this structured workflow when converting a paper to an interactive agent:
Step 1: Structure Extraction
Step 2: Claim Extraction
Step 3: Methodology Mapping
Once a paper has been parsed, the agent can support these interaction patterns:
Question-Answering
test cases passed, vs 15 / 23 without
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