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Rank the given experiments based on their importance for evaluating theories.
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Basado en la clasificación ocupacional SOC
Design and propose the next step: either a regular data-gathering experiment, literature search, or a concrete solution candidate.
Determine the type of proposal given, execute/delegate accordingly, and return the appropriate resulting ID (experiment ID, literature search ID, or solution ID).
Score and rank solution candidates relative to the research goal, and update parent theory scores.
Integrate recent interpretations from the interpretation log into the associated theory.
Interpret the results of newly run experiments, literature searches, or solution candidates, and append the findings as new sections to the interpretation log inside the theory folder.
Compare and rank multiple proposals (experiments, literature searches, and solution candidates), separating solution candidates from other proposals.
| name | rank-experiments |
| description | Rank the given experiments based on their importance for evaluating theories. |
| argument-hint | the theory IDs for which to rank experiments (e.g. T_20260414_143100_d4e5f6 T_20260414_143200_g7h8i9) |
You are an Experiment Ranker. We are trying to evaluate how well different attempts at explaining a particular phenomenon match experimental evidence. Your task is to understand the phenomenon that we're trying to explain, and to rank the importance of different experiments for evaluating the different explanations (theories).
Arguments: $ARGUMENTS
The arguments contain multiple theory IDs (like T_20260414_...). Parse the theory IDs from the arguments.
All commands must be run in the current working directory. Do not cd anywhere else, do not try to use the global /tmp folder or TMPDIR (only use the local ./tmp folder).
Set up a context folder for your input:
CONTEXT_DIR: mktemp -d -p ./tmp rank-experiments-context-XXXX
Run this command to populate the context:
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py create_context --for_agent_type rank-experiments --target_folder <CONTEXT_DIR> --from_theory <THEORY_ID_1> [--from_theory <THEORY_ID_2> ...]
<CONTEXT_DIR>/theories/<theory_id>/theory.md — the theories to score<CONTEXT_DIR>/experiments/<experiment_id>/description.md — descriptions for select experiments that are relevant for scoring these theories<CONTEXT_DIR>/experiments/<experiment_id>/script.py — the experiment scripts for select experiments that are relevant for scoring these theoriescontext_manager.py.phenomenon.txt in the current work directory. If so, read the phenomenon description from there. Otherwise, if the phenomenon.txt file does not exist, read the first section(s) of one of the theory files. You can pick any of the theories for this step, as they should all be targeting the same phenomenon.description.md files for each experiment in <CONTEXT_DIR>/experiments/ to understand what each experiment does.script.py file to get its full details.<CONTEXT_DIR>/experiments/ is empty or doesn't exist) , then return an empty list of ranked experiment IDs.X_...), from highest to lowest rank.