alirezarezvani/resume
engineering/autoresearch-agent/skills/resume/SKILL.md
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment.
$ install --global
skillsauthnpx skillsauth add alirezarezvani/claude-skills resumeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
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Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
50%
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CrowdStrikeAdvanced threat intelligence
50%
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OWASP Dep-Check
50%
Last scanned: Jun 13, 2026, 4:21 AM62.2s1 file scanned
SKILL.md
- name:
- resume
- description:
- Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment.
- command:
- /ar: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.
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