How do I install the Resume skill?

Run npx skillmds add neekware/resume in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Resume safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Resume?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Resume free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Resume?

neekware (@neekware) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Resume by neekware — Agent Skill

Name: Resume
Rating: 4.2 (5 reviews)
Author: neekware

File contents

/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.

---name: resumedescription: Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating.---# /ar:resume — Resume ExperimentResume 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 neededIf no experiment specified:```bashpython {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 branchgit checkout autoresearch/{domain}/{name}# Read configcat .autoresearch/{domain}/{name}/config.cfg# Read strategycat .autoresearch/{domain}/{name}/program.md# Read full results historycat .autoresearch/{domain}/{name}/results.tsv# Read recent git log for the branchgit log --oneline -20```### Step 3: Report current stateSummarize 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`.

Frequently asked questions