rank-proposals

name	rank-proposals
description	Compare and rank multiple proposals (experiments, literature searches, and solution candidates), separating solution candidates from other proposals.
argument-hint	multiple proposal IDs (e.g. O_20260616_111111_aaaaaa O_20260616_222222_bbbbbb)

Rank Proposals

You are an expert scientific agent. Your goal is to review, compare, and rank multiple alternative proposals (which may include regular experiments, literature searches, and solution candidates).

Mandate & Grouping Rules

1. Identify Proposal Types: Check each checked-out proposal to determine its type by reading its proposal.md header (e.g. # Experiment Proposal, # Literature Search Proposal, or # Solution Candidate Proposal).
2. Experiment & Literature Search Proposals:
   • Group all regular experiment proposals and literature search proposals together.
   • Critically compare and rank them into a single ordered list (rankings), from best/highest priority to worst/lowest priority.
   • Evaluate them based on:
     • Expected Value: How much the proposal's findings would reduce uncertainty or move closer to the optimization goal.
     • Feasibility & Soundness: How well-designed the script or search methodology is, and whether the approach is robust.
     • Cost-Benefit Tradeoff: The value of the potential findings compared to the estimated resource cost, complexity, and runtime. Literature search proposals can be assumed to be cheap, about ~5 minutes in runtime.
   • Remove duplicates: If multiple experiment or literature search proposals are effectively the same (identical or very similar experiment setup or search prompt), only keep the best-ranked one among those and discard the other equivalent ones in your ranking.
3. Solution Candidate Proposals:
   • Do NOT rank solution candidate proposals together with experiments or literature searches.
   • Collect all solution candidate proposals into a separate, unranked list (solution_candidates).

Input

Arguments: $ARGUMENTS

The arguments contain two or more proposal IDs (like O_20260616_...). Parse all IDs from the arguments.

Folder Setup

All commands must be run in the current working directory. Do not cd anywhere else, and do not try to use the global /tmp folder or TMPDIR (only use the local ./tmp folder).

Set up a folder for the input context:

• CONTEXT_DIR: mktemp -d -p ./tmp rank-proposals-context-XXXX

Run this command to populate the context, which retrieves all candidate proposals from the database:

uv run python <SKILL_BASE_DIR>/scripts/context_manager.py create_context \
    --for_agent_type rank-proposals \
    --target_folder <CONTEXT_DIR> \
    --from_proposal <O_ID_1> --from_proposal <O_ID_2> [--from_proposal <O_ID_3> ...]

Context layout:

• <CONTEXT_DIR>/proposals/<O_ID>/ — contains a folder for each checked-out proposal containing:
  • proposal.md (detailing the proposal)
  • Optional companion files (such as script.py for experiments/solutions).

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Execution Steps

1. Context Checkout: Run the create_context command above to check out all candidate proposals.
2. Analyze Proposals: For each candidate under <CONTEXT_DIR>/proposals/<O_ID>/, inspect proposal.md to classify it and review its motivation and methodology.
3. Execute Grouping & Ranking:
   • Sort the regular experiments and literature searches together, ranking them from highest priority to lowest priority, while removing duplicates.
   • Gather all solution candidates into a separate, unranked collection.
4. Output Selection & Rankings: Formulate a numeric ranking for the experiment/literature-search proposals (from 1...n), and list the solution candidates separately. Include both sections in the skill's output.