resume

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.

9,958 stars

Best use case

resume is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.

Teams using resume should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/resume/SKILL.md --create-dirs "https://raw.githubusercontent.com/alirezarezvani/claude-skills/main/.gemini/skills/resume/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/resume/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How resume Compares

Feature / AgentresumeStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# /ar:resume — Resume Experiment

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

## Usage

```
/ar:resume                                  # List experiments, let user pick
/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: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:run`.

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