Autoresearch

# Autoresearch: Autonomous Iterative Experimentation

An autonomous experimentation loop for any programming task. You define the goal and how to measure it; the agent iterates autonomously -- modifying code, running experiments, measuring results, and keeping or discarding changes -- until interrupted.

This skill is inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch), generalized from ML training to **any programming task with a measurable outcome**.

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Agent Behavior Rules

1. **DO** guide the user through the Setup phase interactively before starting the loop.

2. **DO** establish a baseline measurement before making any changes.

3. **DO** commit every experiment attempt before running it (so it can be reverted cleanly).

4. **DO** keep a results log (TSV) tracking every experiment.

5. **DO** revert changes that do not improve the metric (git reset to last known good).

6. **DO** run autonomously once the loop starts -- never pause to ask "should I continue?".

7. **DO NOT** modify files the user marked as out-of-scope.

8. **DO NOT** skip the measurement step -- every experiment must be measured.

9. **DO NOT** keep changes that regress the metric unless the user explicitly allowed trade-offs.

10. **DO NOT** install new dependencies or make environment changes unless the user approved it.

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Phase 1: Setup (Interactive)

Before any experimentation begins, work with the user to establish these parameters.

Ask the user directly for each item. Do not assume or skip any.

1.1 Define the Goal

Ask the user:

> **What are you trying to improve or optimize?**

>

> Examples: execution time, memory usage, binary size, test pass rate, code coverage,

> API response latency, throughput, error rate, benchmark score, build time, bundle size,

> lines of code, cyclomatic complexity, etc.

Record the user's answer as the **goal**.

1.2 Define the Metric

Ask the user:

> **How do we measure success? What exact command produces the metric?**

>

> I need:

> 1. **The command** to run (e.g., `dotnet test`, `npm run benchmark`, `time ./build.sh`, `pytest --tb=short`)

> 2. **How to extract the metric** from the output (e.g., a regex pattern, a specific line, a JSON field)

> 3. **Direction**: Is lower better or higher better?

>

> Example: "Run `dotnet test --logger trx`, count passing tests. Higher is better."

> Example: "Run `hyperfine './my-program'`, extract mean time. Lower is better."

Record:

- `METRIC_COMMAND`: the command to run

- `METRIC_EXTRACTION`: how to extract the numeric metric from output

- `METRIC_DIRECTION`: `lower_is_better` or `higher_is_better`

1.3 Define the Scope

Ask the user:

> **Which files or directories am I allowed to modify?**

>

> And which files are OFF LIMITS (read-only)?

Record:

- `IN_SCOPE_FILES`: files/dirs the agent may edit

- `OUT_OF_SCOPE_FILES`: files/dirs that must not be modified

1.4 Define Constraints

Ask the user:

> **Are there any constraints I should respect?**

>

> Examples:

> - Time budget per experiment (e.g., "each run should take < 2 minutes")

> - No new dependencies

> - Must keep all existing tests passing

> - Must not change the public API

> - Must maintain backward compatibility

> - VRAM/memory limit

> - Code complexity limits (prefer simpler solutions)

Record as `CONSTRAINTS`.

1.5 Define the Experiment Budget (Optional)

Ask the user:

> **How many experiments should I run, or should I just keep going until you stop me?**

>

> You can say a number (e.g., "try 20 experiments") or "unlimited" (I'll run until you interrupt).

Record as `MAX_EXPERIMENTS` (number or `unlimited`).

1.6 Simplicity Criterion

Inform the user of the default simplicity policy:

> **Simplicity policy (default):** All else being equal, simpler is better. A small improvement

> that adds ugly complexity is not worth it. Removing code while maintaining or improving

> the metric is a great outcome. I'll weigh the complexity cost against the improvement

> magnitude