Resume a paused or context-limited experiment. Reads all history and continues where you left off.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionresumeExecute the skills CLI command in your project's root directory to begin installation:
Fetches resume from alirezarezvani/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate resume. Access via /resume in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Resume a paused or context-limited experiment. Reads all history and continues where you left off.
/ar:resume # List experiments, let user pick
/ar:resume engineering/api-speed # Resume specific experiment
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.
# 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
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)
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
alirezarezvani/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
I recommend resume for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added resume from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: resume is focused, and the summary matches what you get after install.
resume fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
resume reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added resume from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for resume matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: resume is the kind of skill you can hand to a new teammate without a long onboarding doc.
resume fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
resume has been reliable in day-to-day use. Documentation quality is above average for community skills.
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