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skill Claude Code

ar-resume — Claude Code skill: what it does, how to install

Resumes a paused autoresearch experiment in Claude Code by checking out its git branch, loading previous metric logs, and preparing the next run.

Install into Claude Code · Plugin marketplace All methods and platforms ↓
/plugin marketplace add alirezarezvani/claude-skills
/plugin install autoresearch-agent@claude-code-skills
Open on GitHub

What ar-resume does

The skill picks up an autoresearch workflow that stopped due to a manual pause or hit context limits. When called without arguments, it runs setup_experiment.py --list to display existing runs marked active, paused, or done based on results.tsv timestamps.

After picking an experiment, the agent checks out the branch autoresearch/{domain}/{name}. It inspects config.cfg, reads guidelines in program.md, parses results.tsv, and checks the last 20 git commits. The user gets a breakdown of kept, discarded, or crashed runs alongside best metric values before choosing between a single /ar:ar-run step, an autonomous /ar:loop, or a passive review.

Install this if your project already uses autoresearch workflows. Without the .autoresearch directory structure, the command has nothing to read.

What it helps with

  • resume interrupted autoresearch experiments
  • check out the experiment branch and review the last 20 commits
  • inspect kept and discarded iterations from results.tsv
  • transition back to /ar:ar-run or /ar:loop without manual reconfiguration

Example prompts for the agent

  • /ar:ar-resume
  • /ar:ar-resume engineering/api-speed
  • resume experiment engineering/api-speed and show metric history

How to install ar-resume

1Plugin marketplace

/plugin marketplace add alirezarezvani/claude-skills
/plugin install autoresearch-agent@claude-code-skills

This installs the whole autoresearch-agent plugin with all its skills

2Skills CLI (Vercel)

npx skills add alirezarezvani/claude-skills --skill ar-resume -a claude-code

3Manually: put the files in place

~/.claude/skills/ar-resume/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/ar-resume/

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Questions about ar-resume

How do I install ar-resume in Claude Code?

Add the marketplace source with /plugin marketplace add alirezarezvani/claude-skills and install the package using /plugin install autoresearch-agent@claude-code-skills. You can also run npx skills add alirezarezvani/claude-skills --skill ar-resume -a claude-code.

What does ar-resume do?

It checks out the experiment branch in git, reads recorded metrics from results.tsv, and reports past outcomes. It then asks whether to trigger a single test step or continue an automated loop.

What files does ar-resume check during restoration?

It inspects .autoresearch/{domain}/{name}/config.cfg, program.md, and results.tsv, along with git history from git log --oneline -20.

Source text

SKILL.md · MIT
---
name: "ar-resume"
description: "Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:ar-resume or asks to pick up a previously started autoresearch experiment."
metadata:
  command: /ar:ar-resume
---

# /ar:ar-resume — Resume Experiment

Resume a paused or context-limited experiment. Reads all history and continues where you left off.

## Usage

```
/ar:ar-resume                                  # List experiments, let user pick
/ar:ar-resume engineering/api-speed            # Resume specific experiment
```

## What It Does

### Step 1: List experiments if needed

If no experiment specified:

```bash
python {skill_path}/scripts/setup_experiment.py --list
```

Show status for each (active/paused/done based on results.tsv age). Let user pick.

### Step 2: Load full context

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

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

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

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

# Read recent git log for the branch
git log --oneline -20
```

### Step 3: Report current state

Summarize for the user:

```
Resuming: engineering/api-speed
  Target: src/api/search.py
  Metric: p50_ms (lower is better)
  Experiments: 23 total — 8 kept, 12 discarded, 3 crashed
  Best: 185ms (-42% from baseline of 320ms)
  Last experiment: "added response caching" → KEEP (185ms)

  Recent patterns:
  - Caching changes: 3 kept, 1 discarded (consistently helpful)
  - Algorithm changes: 2 discarded, 1 crashed (high risk, low reward so far)
  - I/O optimization: 2 kept (promising direction)
```

### Step 4: Ask next action

```
How would you like to continue?
  1. Single iteration (/ar:ar-run)  — I'll make one change and evaluate
  2. Start a loop (/ar:loop)     — Autonomous with scheduled interval
  3. Just show me the results    — I'll review and decide
```

If the user picks loop, hand off to `/ar:loop` with the experiment pre-selected.
If single, hand off to `/ar:ar-run`.

© Alireza Rezvani · MIT · alirezarezvani/claude-skills

See also

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