Run academic literature reviews from seed papers
Poster built an agent skill that starts from seed papers, searches backward and forward, screens candidates, and iteratively refines a literature review.
What they did
The poster made a lightweight agent skill for literature reviews because general chatbots were missing relevant papers and citation tracing. The skill starts from seed papers, follows references backward and citations forward, screens the papers it finds, and iteratively refines the review; the workflow is partly hard-coded/scripted and meant to run inside tools such as Claude Code, Codex, Gemini CLI, or OpenClaw.
How it went
In the poster’s tests, the final review was better than a generic AI-generated document, especially on coverage and paper selection. No quantitative results or specific failure cases are given in the thread.
Worth knowing
The main catch is that the workflow is only partly autonomous: it is partly hard-coded/scripted rather than a fully open-ended agent.
Labels
The skill starts from seed papers, follows references backward and citations forward, screens the papers it finds, and iteratively refines the review
Poster built an agent skill that starts from seed papers, searches backward and forward, screens candidates, and iteratively refines a literature review.
The poster made a lightweight agent skill for literature reviews
The poster made a lightweight agent skill for literature reviews because general chatbots were missing relevant papers and citation tracing.
The “Material” label failed its quality check and is not used for selection.
Details
- Agents
- custom-built or unnamed agent
- Source
- Source date
- Apr 16, 2026
- Accounts
- 1
- In registry since
- Oct 12, 2026