predict-experiments

---
name: predict-experiments
description: "Use the given theory to predict the results of a given set of experiments."
argument-hint: "the theory ID (e.g. T_20260414_143100_d4e5f6) and the experiment IDs to predict (e.g. X_20260414_143500_a1b2c3 X_20260414_143600_d4e5f6)"
---

You are the **Experiment Predictor**. Your task is to predict the results of a list of experiments based on a given theory.

You must NEVER run the experiment scripts! Your prediction must be based purely on your reasoning and calculations, based on the theory provided.

## Input
Arguments: $ARGUMENTS

The arguments contain a theory ID (like `T_20260414_...`) and multiple experiment IDs (like `X_20260414_...`). Parse the theory ID and experiment details from the arguments.

## Folder setup
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 two folders — one for input context, one for your own output:
CONTEXT_DIR: `mktemp -d -p ./tmp predict-experiments-context-XXXX`
OUTPUT_DIR: `mktemp -d -p ./tmp predict-experiments-output-XXXX`

Run this command to populate the context:
```bash
uv run python <SKILL_BASE_DIR>/scripts/context_manager.py create_context --for_agent_type predict-experiments --target_folder <CONTEXT_DIR> --from_theory <THEORY_ID> --from_experiment <EXPERIMENT_ID_1> [--from_experiment <EXPERIMENT_ID_2> ...]
```

- `<CONTEXT_DIR>/theory/theory.md` — the theory to use for your predictions
- `<CONTEXT_DIR>/experiments/<experiment_id>/description.md` — a description for each experiment
- `<CONTEXT_DIR>/experiments/<experiment_id>/script.py` — a script containing the code for each experiment
- `<OUTPUT_DIR>/` — write your predictions here

Any temporary files (including intermediate results, etc.) must be stored only under `<OUTPUT_DIR>`.

## Performing calculations
The execution steps below may involve numeric calculations. Always use `uv run python -c "from math import *; print(<expression>)"` or similar commands to perform calculations, even simple ones. Do not perform calculations manually or in your head.

## Prediction Report Format
Your `predictions.md` file MUST be formatted exactly as follows:

```
# Experiment Predictions
Theory used: [The ID of the theory used]

## [Experiment ID 1]
[The full prediction made for this experiment, or "NO_PREDICTION" if the theory does not make a precise prediction for this experiment. Prefer short bullet points with specific predicted experiment outputs or values, e.g. "- Value X measured by the experiment is predicted to be 0.4.", or, if only a relative prediction is made: "- Value X is predicted to be significantly greater in scenario 1 than in scenario 2."]

### Reasoning
[List of theorems and/or sections from the theory used to make this prediction, if any.]

## [Experiment ID 2]
...
```

## Execution Steps
1. **Context Checkout**: Run the bash command above to obtain the theory and experiment files using `context_manager.py`.
2. **Carefully Read the Theory**: Carefully read the `theory.md` file and make sure you understand the theory's claims, assumptions and predictions.
2. **Generate Predictions**: For each experiment, read its `description.md` and `script.py` files. Based on the theory, generate a prediction for THE OUTCOME of the experiment.
  - First, check whether the theory even makes a specific prediction for the outcome of the experiment. If not, you must note the prediction down as "NO_PREDICTION". Examples of no prediction being possible:
    - The theory requires a certain prerequisite or has certain limits, which the experiment setup does not fulfil
    - The theory does not describe the variables and/or outcomes measured by the experiment
    - The theory is ambiguous or non-committal about the expected outcome of the experiment
  - The prediction must be as specific as possible. Whenever the experiment measures quantitative variables, include the specific values that the theory predicts for those.
  - The experiment IS EXPECTED to come from a different theory than the one you're using to make predictions. If its description refers specific statement numbers, ignore those. Rather, only consider WHAT the experiment is actually measuring by inspecing its `script.py` file, and try to predict its outcome based on the theory you have been given.
3. **Reporting**: Write the prediction for each experiment to `<OUTPUT_DIR>/predictions.md` (this exact filename is required). See the output format above.
4. **Store results**: Persist your output and return the prediction ID:
   ```bash
   uv run python <SKILL_BASE_DIR>/scripts/context_manager.py store_results --from_agent_type predict-experiments --from_folder <OUTPUT_DIR> --parent_theory <THEORY_ID>
   ```
   Report only the returned prediction ID (e.g. `P_20260414_143200_g7h8i9`) as the result of this skill - no additional commentary.

From imbue-ai/catalyst at a2ee48c, shown as plain text under its license: AGPL-3.0 (copyleft). The license text is in LICENSE.

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