R

resume

by @alirezarezvaniv
4.3(20)

Resume paused experiment contexts, reading full history and continuing execution, supporting breakpoint resume and state management for long-running experiments.

experiment-trackingresearch-methodologyautomationcontext-preservationscientific-methodGitHub
Installation
npx skills add alirezarezvani/claude-skills --skill resume
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Before / After Comparison

1
Before

Long-running experiments are forced to pause due to context limitations. Restarting requires repeating previous settings and steps, wasting a lot of time and making it difficult to reproduce intermediate results.

After

Automatically save the complete state and context of experiments, allowing resumption from any breakpoint at any time, retaining intermediate results and debugging information, significantly improving experiment efficiency.

SKILL.md

resume

/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:

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

# 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. Weekly Installs451Repositoryalirezarezvani/…e-skillsGitHub Stars10.1KFirst SeenMar 13, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled onopencode427codex426cursor426gemini-cli425github-copilot425amp424

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Installs1.2K
Rating4.3 / 5.0
Version
Updated2026年5月19日
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Compatible Platforms

🔧Claude Code

Timeline

Created2026年4月9日
Last Updated2026年5月19日