score-theories

name	score-theories
description	Score the quality of the given theories relative to each other and update all population scores.
argument-hint	the theory IDs to score (e.g. T_20260414_143100_d4e5f6 T_20260414_143200_g7h8i9)

You are the Theory Scoring Coordinator. Your task is to compare the quality of different theories by coordinating different scoring subagents, and then combining their results into overall theory scores.

Input

Arguments: $ARGUMENTS

The arguments contain multiple theory IDs (like T_20260414_...). Parse the theory IDs from the arguments.

Execution Steps

Follow the following steps carefully. Do not skip anything. Do not take shortcuts.

1. Obtain Ranked Experiment IDs: Launch a subagent instructed to invoke the rank-experiments skill, passing to it the list of theory IDs. The subagent will return the highest-ranking experiment IDs (X_...), from highest rank to lowest rank. These are the experiment IDs that we will be using to evaluate the theories in the following steps. Wait for the subagent to finish and collect its response with the ranked experiment IDs.

• Edge case: If the rank-experiments subagent returns an empty list, that means that there are no experiments to evaluate the theories on. You should assign a prediction accuracy and prediction coverage score of 1 across all theories in that case.

2. Generate Theory Predictions: For each of the theory IDs individually:

• Spawn a subagent instructed to invoke the predict-experiments skill, passing to it the specific theory ID and the list of ranked experiment IDs. This subagent will return a prediction ID (e.g. P_20260414_143052_a1b2c3). All subagents can run in parallel.

3. Collect Predictions: Wait for each subagent to finish and collect their final result messages containing the prediction IDs.
4. Rank Predictions: For each of the ranked experiment IDs individually:

• Spawn a subagent instructed to invoke the rank-predictions skill, passing to it the list of prediction IDs from the previous step, and a single experiment ID. This subagent will return a ranked list of theory IDs for that one experiment. It might report some theory IDs as NO_PREDICTION if those theories do not make predictions for that experiment (that is fine). All subagents can run in parallel.

5. Score Prediction Rankings: For each theory ID individually, invoke a script to calculate the prediction accuracy score and the prediction coverage score:

uv run python <SKILL_BASE_DIR>/scripts/compute_prediction_scores.py -n <NUMBER_OF_THEORIES> --theory_id <THEORY_ID> --ranks <PREDICTION_RANK_ON_EXPERIMENTS_LIST>

• <NUMBER_OF_THEORIES> is the total number of theories being scored (i.e. the length of the input theory ID list).
• <THEORY_ID> is the specific theory ID for which you are calculating the scores.
• The <PREDICTION_RANK_ON_EXPERIMENTS_LIST> is a comma-separated list of the ranks of this theory's predictions for each of the selected experiments, in order of experiment ranking (the order is important!). Include a "NO_PREDICTION" for experiments where the current theory did not make a prediction. For example, if there were 3 experiments and this theory ranked 1st for the first experiment, 3rd for the second, and did not make a prediction for the third, then the list would be: 1,3,NO_PREDICTION.
• The script will output the prediction accuracy score and the prediction coverage score for this theory.

6. Rank Explanatory Power: Spawn a single subagent instructed to invoke the rank-explanatory-power skill, passing to it the list of theory IDs. It will return an explanatory power score for each theory. This subagent can run in parallel with the other subagents from the next step.
7. Obtain Additional Per-Theory Scores: For each of the theory IDs individually:

• Spawn a subagent instructed to invoke the score-theory-local-subscores skill, passing to it the specific theory ID. The subagent will return multiple scores between 0 and 1: a soundness score, a length score, and a guidance adherence score. All subagents can run in parallel.

8. Collect Subscores: Wait for all subagents from the previous steps to finish and collect their final result messages.
9. Overall Theory Score: Combine the scores for each theory into an overall score object, one for each theory, by running:

uv run python <SKILL_BASE_DIR>/scripts/combine_scores.py --theory_id <THEORY_ID> --prediction_accuracy <PREDICTION_ACCURACY_SCORE> --prediction_coverage <PREDICTION_COVERAGE_SCORE> --soundness <SOUNDNESS_SCORE> --explanatory_power <EXPLANATORY_POWER_SCORE> --length <LENGTH_SCORE> --adherence <GUIDANCE_ADHERENCE_SCORE>

• The script will output a JSON object {<THEORY_ID>: { ... }} containing both the overall score, and all subscores for the given theory. You will use this output in the next step.

10. Save Scores: Save the overall scores and the detailed subscores to a database, using this bash command:

uv run python <SKILL_BASE_DIR>/scripts/context_manager.py rescore_theories '{<THEORY_ID_1>: <THEORY_1_SCORES_OBJECT>, <THEORY_ID_2>: <THEORY_2_SCORES_OBJECT>, ...}'

• Where <THEORY_X_SCORES_OBJECT> is the JSON object output from the previous step for theory X, containing both the overall score and the subscores for that theory.
• This step is critical! If you don't save the scores to the database, all of your work will be lost!

11. Final Output: Report the list of all theory IDs along with their final scores. Also include a breakdown of the different subscores for each theory.