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How to turn a research direction into a ranked proposal with Effective Auto Research EAR

Use AutoVibeIdea to map literature, rank candidate ideas and draft a proposal.

  • Research
  • Advanced
  • 120 minutes
  • From the README
  • Codex

Credentials. Live runs use your own authenticated Codex or Claude account, so keep those credentials out of shared workspaces and commits.

Who it is forA researcher starting a new project who has a broad direction and a set of constraints but no settled idea yet.

The problemEarly candidate ideas often overlap with existing work, and checking that by hand takes a long time.

The outcomeA literature landscape, ranked candidates, and a proposal draft saved in its own AutoVibeIdea workspace, with critique that can rule out overlapping ideas early.

What you need first

  • Python 3.10 or newer
  • git, to clone the repository
  • For live research only: an authenticated Codex or Claude setup and the module dependencies described in docs/getting-started.md

Steps

  1. Complete the live setup in docs/getting-started.md, including an authenticated Codex or Claude route and the AutoVibeIdea dependencies.
  2. Write your research direction and constraints as the module input, following docs/getting-started.md#develop-a-research-direction.
  3. Run the AutoVibeIdea workflow from the autovibeidea/ folder in its own workspace, using the exact command given in the linked guide. (inferred, check the README)
  4. Review the literature landscape and the ranked candidates.
  5. Inspect the critique for any candidate that shows overlap with prior work, then take the strongest proposal forward into a validation plan.

Prompt for your agent

In the repo liyingze07-svg/EffectiveAutoResearch-EAR-, follow docs/getting-started.md to run AutoVibeIdea on the research direction <research-direction>. Expected output is a literature landscape, ranked candidates and a proposal draft in its own workspace. Verify that each candidate's novelty judgment cites retrieved prior work before you rank it highest.

Caveat. The README says historical model judgments do not establish conference acceptance, so treat the ranking as a starting point for your own review.

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