Skill Introduction

Overview

Run a single experiment iteration. Edit the target file, evaluate, keep or discard.

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community work faster.

Target users

Developers, testers, and maintainers who handle Development tasks in Focus Code.
Teams that already trust workflows or content from alirezarezvani.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

Tasks related to github, community that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

Structured around Development, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, so it can be filtered by concrete task intent.

Install and use

Install

Copy Install Command

focus install run-8b50b2

View source

Detail Preview

SKILL.md

Primary filemarkdown3 KB

name: "run" description: "Run a single experiment iteration. Edit the target file, evaluate, keep or discard." command: /ar:run

/ar:run — Single Experiment Iteration

Run exactly ONE experiment iteration: review history, decide a change, edit, commit, evaluate.

Usage

/ar:run engineering/api-speed              # Run one iteration
/ar:run                                     # List experiments, let user pick

What It Does

Step 1: Resolve experiment

If no experiment specified, run python {skill_path}/scripts/setup_experiment.py --list and ask the user to pick.

Step 2: Load context

# Read experiment config
cat .autoresearch/{domain}/{name}/config.cfg

# Read strategy and constraints
cat .autoresearch/{domain}/{name}/program.md

# Read experiment history
cat .autoresearch/{domain}/{name}/results.tsv

# Checkout the experiment branch
git checkout autoresearch/{domain}/{name}

Step 3: Decide what to try

Review results.tsv:

What changes were kept? What pattern do they share?
What was discarded? Avoid repeating those approaches.
What crashed? Understand why.
How many runs so far? (Escalate strategy accordingly)

Strategy escalation:

Runs 1-5: Low-hanging fruit (obvious improvements)
Runs 6-15: Systematic exploration (vary one parameter)
Runs 16-30: Structural changes (algorithm swaps)
Runs 30+: Radical experiments (completely different approaches)

Step 4: Make ONE change

Edit only the target file specified in config.cfg. Change one thing. Keep it simple.

Step 5: Commit and evaluate

git add {target}
git commit -m "experiment: {short description of what changed}"

python {skill_path}/scripts/run_experiment.py \
  --experiment {domain}/{name} --single

Step 6: Report result

Read the script output. Tell the user:

KEEP: "Improvement! {metric}: {value} ({delta} from previous best)"
DISCARD: "No improvement. {metric}: {value} vs best {best}. Reverted."
CRASH: "Evaluation failed: {reason}. Reverted."

Step 7: Self-improvement check

After every 10th experiment (check results.tsv line count), update the Strategy section of program.md with patterns learned.

Rules

ONE change per iteration. Don't change 5 things at once.
NEVER modify the evaluator (evaluate.py). It's ground truth.
Simplicity wins. Equal performance with simpler code is an improvement.
No new dependencies.

run