loop

alirezarezvani/claude-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/alirezarezvani/claude-skills --skill loop
0 commentsdiscussion
summary

Start a recurring experiment loop that runs at a user-selected interval.

skill.md

/ar:loop — Autonomous Experiment Loop

Start a recurring experiment loop that runs at a user-selected interval.

Usage

/ar:loop engineering/api-speed             # Start loop (prompts for interval)
/ar:loop engineering/api-speed 10m         # Every 10 minutes
/ar:loop engineering/api-speed 1h          # Every hour
/ar:loop engineering/api-speed daily       # Daily at ~9am
/ar:loop engineering/api-speed weekly      # Weekly on Monday ~9am
/ar:loop engineering/api-speed monthly     # Monthly on 1st ~9am
/ar:loop stop engineering/api-speed        # Stop an active loop

What It Does

Step 1: Resolve experiment

If no experiment specified, list experiments and let user pick.

Step 2: Select interval

If interval not provided as argument, present options:

Select loop interval:
  1. Every 10 minutes  (rapid — stay and watch)
  2. Every hour         (background — check back later)
  3. Daily at ~9am      (overnight experiments)
  4. Weekly on Monday   (long-running experiments)
  5. Monthly on 1st     (slow experiments)

Map to cron expressions:

Interval Cron Expression Shorthand
10 minutes */10 * * * * 10m
1 hour 7 * * * * 1h
Daily 57 8 * * * daily
Weekly 57 8 * * 1 weekly
Monthly 57 8 1 * * monthly

Step 3: Create the recurring job

Use CronCreate with this prompt (fill in the experiment details):

You are running autoresearch experiment "{domain}/{name}".

1. Read .autoresearch/{domain}/{name}/config.cfg for: target, evaluate_cmd, metric, metric_direction
2. Read .autoresearch/{domain}/{name}/program.md for strategy and constraints
3. Read .autoresearch/{domain}/{name}/results.tsv for experiment history
4. Run: git checkout autoresearch/{domain}/{name}

Then do exactly ONE iteration:
- Review results.tsv: what worked, what failed, what hasn't been tried
- Edit the target file with ONE change (strategy escalation based on run count)
- Commit: git add {target} && git commit -m "experiment: {description}"
- Evaluate: python {skill_path}/scripts/run_experiment.py --experiment {domain}/{name} --single
- Read the output (KEEP/DISCARD/CRASH)

Rules:
- ONE change per experiment
- NEVER modify the evaluator
- If 5 consecutive crashes in results.tsv, delete this cron job (CronDelete) and alert
- After every 10 experiments, update Strategy section of program.md

Current best metric: {read from results.tsv or "no baseline yet"}
Total experiments so far: {count from results.tsv}

Step 4: Store loop metadata

Write to .autoresearch/{domain}/{name}/loop.json:

{
  "cron_id": "{id from CronCreate}",
  "interval": "{user selection}",
  "started": "{ISO timestamp}",
  "experiment": "{domain}/{name}"
}

Step 5: Confirm to user

Loop started for {domain}/{name}
  Interval: {interval description}
  Cron ID: {id}
  Auto-expires: 3 days (CronCreate limit)

  To check progress: /ar:status
  To stop the loop:  /ar:loop stop {domain}/{name}

  Note: Recurring jobs auto-expire after 3 days.
  Run /ar:loop again to restart after expiry.

Stopping a Loop

When user runs /ar:loop stop {experiment}:

  1. Read .autoresearch/{domain}/{name}/loop.json to get the cron ID
  2. Call CronDelete with that ID
  3. Delete loop.json
  4. Confirm: "Loop stopped for {experiment}. {n} experiments completed."

Important Limitations

  • 3-day auto-expiry: CronCreate jobs expire after 3 days. For longer experiments, the user must re-run /ar:loop to restart. Results persist — the new loop picks up where the old one left off.
  • One loop per experiment: Don't start multiple loops for the same experiment.
  • Concurrent experiments: Multiple experiments can loop simultaneously ONLY if they're on different git branches (which they are by default — each experiment gets autoresearch/{domain}/{name}).
how to use loop

How to use loop on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add loop
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/alirezarezvani/claude-skills --skill loop

The skills CLI fetches loop from GitHub repository alirezarezvani/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/loop

Reload or restart Cursor to activate loop. Access the skill through slash commands (e.g., /loop) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.648 reviews
  • Jin Johnson· Dec 16, 2024

    Keeps context tight: loop is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ren Farah· Dec 16, 2024

    We added loop from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Luis Chen· Dec 12, 2024

    loop is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Alexander Nasser· Dec 4, 2024

    Registry listing for loop matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Diego Farah· Nov 23, 2024

    Keeps context tight: loop is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Yash Thakker· Nov 7, 2024

    Solid pick for teams standardizing on skills: loop is focused, and the summary matches what you get after install.

  • Carlos Ndlovu· Nov 7, 2024

    Registry listing for loop matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sofia Anderson· Nov 7, 2024

    loop fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Dhruvi Jain· Oct 26, 2024

    loop is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Sakura Rao· Oct 26, 2024

    Useful defaults in loop — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

showing 1-10 of 48

1 / 5