lab:autoresearch
lab:autoresearch runs a self-improving loop that mutates plugin skills one change at a time, scores each against deterministic criteria, and commits improvements while reverting failures. Target the weakest skill or run a full sweep overnight with /loop.
lab:autoresearch automatically improves plugin skills by proposing mutations, evaluating them deterministically, and keeping or reverting based on results.
AI-generated summary based on this skill's SKILL.md
Install
oliver-kriska/claude-elixir-phoenix/autoresearch · repository language: Python
git clone https://github.com/oliver-kriska/claude-elixir-phoenix
cp -r claude-elixir-phoenix/lab/autoresearch ~/.claude/skills/autoresearchnpx skillfed install oliver-kriska/claude-elixir-phoenix/autoresearchFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does lab:autoresearch auto improve plugin skills?
lab:autoresearch automatically improves plugin skills through iterative mutations and deterministic evaluation. It mutates skills one change at a time, scores each mutation against fixed criteria, commits improvements that pass, and reverts failures. You can target the weakest skill dimension or run a full sweep overnight using the /loop command.
Can I run overnight skill optimization loops with lab:autoresearch?
Yes. lab:autoresearch supports running overnight skill optimization loops with targeted or sweep strategies. Use /loop to launch an optimization run—target a specific weak dimension for focused improvement or execute a full sweep across all skill aspects. The loop runs deterministic evaluation on each mutation and commits only successful changes.
Does lab:autoresearch detect and revert skill regressions?
lab:autoresearch detects and reverts skill regressions while keeping successful improvements. Each mutation is scored against deterministic criteria; if a change degrades performance, it's automatically reverted. Only mutations that pass evaluation are committed, ensuring your skills improve without degradation.
How does lab:autoresearch score and track skill weaknesses?
lab:autoresearch scores and tracks the weakest skill dimensions for focused optimization. It evaluates each skill against deterministic criteria and identifies which aspects need the most work. You can then use targeted /loop runs to concentrate improvements on those specific weak areas rather than optimizing blindly.
What is automated skill mutation testing in lab:autoresearch?
lab:autoresearch performs automated skill mutation testing by making one change at a time to your plugin skills, evaluating each mutation deterministically, and keeping or reverting based on results. This iterative skill enhancement approach builds a mutation history and maintains a backlog of deferred optimizations for continuous refinement.
SKILL.md
rendered from the published skill — quoted content, verbatim
Autoresearch — Plugin Skill Self-Improvement
Iteratively improve plugin skills via the autoresearch pattern: propose one mutation -> eval -> keep/revert -> repeat.
Usage
/lab:autoresearch # Targeted: attack weakest skill+dimension
/lab:autoresearch --skill review # Focus on one skill
/lab:autoresearch --strategy sweep # Process all skills alphabetically
/lab:autoresearch --dry-run # Show what would change, don't commit
For overnight runs:
/loop 5m /lab:autoresearch --strategy sweep --max-iterations 200
Iron Laws
- ONE mutation per iteration — if description needs "and", split into two
- NEVER mutate read-only files — check program.md before
(truncated - see the full file via the links below)
Read as markdown · JSON record · Browse the source repository
File tree — 13 files
lab/autoresearch/.gitignore
lab/autoresearch/SKILL.md
lab/autoresearch/program.md
lab/autoresearch/references/mutation-strategies.md
lab/autoresearch/references/state-management.md
lab/autoresearch/retention.py
lab/autoresearch/scripts/checks.sh
lab/autoresearch/scripts/protected_sections.py
lab/autoresearch/scripts/run-iteration.py
lab/autoresearch/scripts/score-skill.py
lab/autoresearch/tests/__init__.py
lab/autoresearch/tests/test_deviation_dispatch.py
lab/autoresearch/tests/test_protected_sections.py