auto-research
Autonomous skill improvement through parallel research agents. Inspired by Karpathy's autoresearch methodology, it spawns 5 parallel agents
Open source Open in the app JSON README (API)
About
Autonomous skill improvement through parallel research agents. Inspired by Karpathy's autoresearch methodology, it spawns 5 parallel agents (domain expert, quality auditor, competitive analyst, gap analyst, tech scout), synthesizes findings into ranked proposals, then runs an iterative keep/discard experiment loop to improve any Claude Code skill. Scores skills on 7 quality dimensions and only keeps changes that genuinely improve the composite score.
Details
- Kind
- Plugins
- Topic
- No topic detected
- Publisher
- gyoz-ai
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 3
- Last push
- 2026-03-28T12:03:50Z
- Repository state
- ativo
- License
- MIT
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
gyoz-ai/auto-research/auto-research
README
# auto-research Autonomous skill improvement for [Claude Code](https://docs.anthropic.com/en/docs/claude-code), inspired by Karpathy's [autoresearch](https://github.com/karpathy/autoresearch). Instead of optimizing a neural network overnight, it optimizes your Claude Code skills through parallel research agents and an iterative keep/discard improvement loop. ## How it works ``` /auto-research my-skill-name ``` The command runs through 5 phases: ### Phase 1: Discovery Reads your skill file, extracts metadata, and creates a backup. ### Phase 2: Parallel Research (5 agents) Spawns 5 research agents simultaneously, each with a focused mission: | Agent | Role | Method | |-------|------|--------| | Domain Expert | Finds best practices and conventions | Web search for guides, docs, style guides | | Quality Auditor | Scores the skill on 7 dimensions | Structural analysis against quality rubric | | Competitive Analyst | Finds how others solve similar problems | Searches cursor rules, AI prompts, cheatsheets | | Gap Analyst | Identifies missing scenarios | User journey and edge case analysis | | Tech Scout | Checks for outdated or deprecated content | Searches changelogs, breaking changes | ### Phase 3: Synthesis Combines all findings, de-duplicates, and ranks improvement proposals using: ``` Priority Score = Impact x Confidence / Complexity ``` ### Phase 4: Iterative Improvement Loop Applies improvements one at a time (like autoresearch experiments): 1. **Apply** a single, focused change 2. **Evaluate** it (accuracy, clarity, value-add, simplicity) 3. **Keep** if it improves the skill, **Discard** if it doesn't 4. **Repeat** for the next proposal ### Phase 5: Results Report Shows before/after quality scores, lists all kept/discarded experiments. ## The autoresearch parallel | autoresearch (ML) | auto-research (Skills) | |---|---| | Modify `train.py` | Modify `SKILL.md` | | Train for 5 min on GPU | Research with 5 parallel agents | | Measure `val_bpb` (lower = better) | Score on 7 quality dimensions (higher = better) | | Keep if metric improves | Keep if quality improves | | `git reset` if worse | Revert edit if worse | | `results.tsv` tracking | Experiment log with keep/discard | | Simplicity criterion | Same -- complex additions with marginal value get discarded | ## Quality dimensions The auditor agent scores skills on: 1. **Actionability** -- Can Claude immediately act on the instructions? 2. **Clarity** -- Is the language unambiguous? 3. **Completeness** -- Does it cover the full workflow? 4. **Examples** -- Are there enough concrete examples? 5. **Edge Cases** -- Does it handle failure modes? 6. **Conciseness** -- Every line earns its place? 7. **Trigger Accuracy** -- Does the description match when it should activate? ## Installation ### Option 1: Plugin Directory (recommended) Install directly from the Claude Code plugin directory: ``` /plugins ``` Search for **auto-research** and install it. ### Option 2: Install plugin from GitHub ```bash # In any Claude Code session: /install-plugin https://github.com/gyoz-ai/auto-research ``` ### Option 3: Manual install Clone and symlink the skill: ```bash git clone https://github.com/gyoz-ai/auto-research.git ~/auto-research mkdir -p ~/.claude/skills/auto-research ln -s ~/auto-research/skills/auto-research/SKILL.md ~/.claude/skills/auto-research/SKILL.md ``` Or copy directly: ```bash mkdir -p ~/.claude/skills/auto-research curl -o ~/.claude/skills/auto-research/SKILL.md \ https://raw.githubusercontent.com/gyoz-ai/auto-research/main/skills/auto-research/SKILL.md ``` ## Usage ```bash # In any Claude Code session: # By skill name (looks in ~/.claude/skills/<name>/SKILL.md) /auto-research my-skill # By full path /auto-research ~/.claude/skills/my-skill/SKILL.md # By project-level path /auto-research .claude/skills/my-skill/SKILL.md ``` The command runs autonomously -- it won't ask for permission between experiments. When it's done, you can: 1. **Review** the changes in detail 2. **Run another cycle** for deeper improvements 3. **Revert** all changes if you don't like them ## Requirements - [Claude Code](https://docs.anthropic.com/en/docs/claude-code) CLI, desktop app, or IDE extension - An existing skill to improve (in `~/.claude/skills/` or `.claude/skills/`) ## Plugin structure ``` auto-research/ ├── .claude-plugin/ │ └── plugin.json ├── skills/ │ └── auto-research/ │ └── SKILL.md ├── LICENSE └── README.md ``` ## Credits Methodology inspired by [autoresearch](https://github.com/karpathy/autoresearch) by Andrej Karpathy -- the concept of autonomous, iterative experimentation with a keep/discard loop and simplicity criterion. ## License MIT