# Auto Research

Run fast, progressive experiments to map tunable parameters and their effects.

- Repository: https://github.com/dzhng/skills (1394 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add dzhng/skills --skill auto-research -g -a claude-code -y`
Codex: `npx skills add dzhng/skills --skill auto-research -g -a codex -y`
Cursor: `npx skills add dzhng/skills --skill auto-research -g -a cursor -y`
Gemini CLI: `npx skills add dzhng/skills --skill auto-research -g -a gemini-cli -y`

## Description

Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Auto Research

Optimize **learning per unit of time**. Start with one fast, revealing task,
plan competing hypotheses, test them separately, combine verified winners, then
expand coverage. The durable result is a **parameter-effect map** plus the best
verified artifact. An experiment is a checkpoint, not a stopping point.

## Workflow

1. **Recover the research state.** Read the objective, user feedback, project
   instructions, and existing research records. Reconcile the handoff with actual
   artifacts and running jobs; collect a pending attempt before launching another.
   Identify the best verified artifact (the incumbent), active task, regression
   set, hypothesis queue, and remaining budget. Reuse existing record locations.

2. **Clarify success before experimenting.** Recover any already agreed criteria;
   do not ask the user to repeat them. If the evaluator, metric, comparison
   baseline, or required improvement is unclear, ask focused questions and wait
   for answers before editing candidates or launching evaluations. Inspect
   existing artifacts and propose concrete options to make answering easier;
   do not silently choose what success means. Continue clarification until the
   answers define a checkable criterion, including evaluation scope, aggregation,
   and non-regression gates. Restate that criterion in the research contract.

The rest: https://github.com/dzhng/skills/blob/HEAD/skills/engineering/auto-research/SKILL.md