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.
/plugin marketplace add alirezarezvani/claude-skills
/plugin install autoresearch-agent@claude-code-skillsWhat 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-skillsThis installs the whole autoresearch-agent plugin with all its skills
2Skills CLI (Vercel)
npx skills add alirezarezvani/claude-skills --skill ar-resume -a claude-code3Manually: put the files in place
~/.claude/skills/ar-resume/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/ar-resume/
Manually: put the files in place
.agents/skills/ar-resume/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/ar-resume/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install alirezarezvani/claude-skills ar-resume2Manually: put the files in place
.github/skills/ar-resume/SKILL.mdPlatform CLI
gemini skills install https://github.com/alirezarezvani/claude-skills --path engineering/autoresearch-agent/skills/ar-resumeManually: put the files in place
.devin/skills/ar-resume/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set349
The whole set →Similar
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