ar-resume – Agents & Automation skill for AI agents

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.

Agents & AutomationSKILL.mdClaude Code · Codex · Hermes · DeepSeek

Install the ar-resume skill with one command

Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.

Claude Code

mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/ar-resume-a8a6f.tgz | tar xz -C ~/.claude/skills

Call it in Claude Code: /ar-resume – then describe the task you want done.

Codex

mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/ar-resume-a8a6f.tgz | tar xz -C ~/.codex/skills

Call it in Codex: $ar-resume – then describe the task you want done.

Hermes Agent

mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/ar-resume-a8a6f.tgz | tar xz -C ~/.hermes/skills

Call it in Hermes Agent: /ar-resume – then describe the task you want done.

DeepSeek

mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/ar-resume-a8a6f.tgz | tar xz -C ~/.deepcode/skills

Call it in DeepSeek: /ar-resume – then describe the task you want done.

SKILL.md content

/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:

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: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.

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