neekware/artifacts/bundle/skills/engineering/resume
artifacts/bundle/skills/engineering/resume/SKILL.md
# /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 b
development
Updated Apr 24, 2026
$ install --global
skillsauthnpx skillsauth add neekware/ehayeskills artifacts/bundle/skills/engineering/resumeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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Clean
TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
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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
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OSV-ScannerOpen Source Vulnerability database check
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OWASP Dep-Check
50%
Last scanned: Apr 24, 2026, 4:02 PM32.4s1 file scanned
SKILL.md
/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.
Creator: Engineering License: MIT Source Repo:
neekware/ehaye-skillsSource Bucket:engineeringOriginal Path:engineering/autoresearch-agent/skills/resume
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