Loopy
A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows.
Install
npx skills add Forward-Future/loopy --skill loopy -gclaude mcp add loopy -- npx skills add Forward-Future/loopy --skill loopy -g{
"mcpServers": {
"loopy": {
"command": "npx",
"args": [
"skills",
"add",
"Forward-Future/loopy",
"--skill",
"loopy",
"-g"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Loop Library
Loop Library has two separate but related parts in this repository:
| Part | What it is | Where it lives |
|---|---|---|
| Loop Library website | The public catalog where people and agents can browse published loops, read them, and copy their prompts. No installation is required. | Live website · all website code under loop-library/ (shell in loop-library/site/, database and rendering in loop-library/worker/) |
| Loopy skill | An optional installable guide that helps an AI agent discover, find, audit, repair, craft, run, debrief, save, or prepare loops for publication. It uses the website's live catalog when recommending or publishing loops. | source in skills/loopy/ |
The website is the library; Loopy is a companion way to work with it. You can browse or give an agent the website without installing Loopy. Installing Loopy adds the guided workflow, but it does not install or host the website.
Agents that do not have Loopy can use the published agent guide, agent instructions, JSON catalog, or plain-text catalog directly.
Each published loop tells an agent what to do, how to check its work, what to try next, and when to stop.
What is a loop?
Most prompts ask an agent to do something once. A loop gives the agent a way to learn from the result and take the next useful step.
For example, a one-shot prompt might say:
Make this website faster.
A loop adds the feedback that makes the work repeatable:
Find the slowest page, make one focused improvement, and measure it again. Keep the change only if it helps. Repeat until every page meets the target or another pass stops producing a meaningful improvement.
Think of a loop as a playbook with feedback built in. It is useful when the first attempt probably will not be the final answer, such as fixing production errors, improving test coverage, reviewing a product, or keeping documentation current.
A good loop answers four simple questions:
- What is the agent trying to accomplish?
- How will it know whether the latest attempt worked?
- What should it do with what it learned?
- When should it finish or ask for help?
Why loops are powerful
AI agents can move quickly, but an open-ended instruction like "keep improving this" leaves too much room for guessing. A loop gives the work a clear finish line and a consistent way to judge progress.
That makes the work easier to trust and easier to repeat. The agent can compare results instead of relying on confidence, keep improvements instead of merely making changes, and stop when it succeeds or stops making progress. The same loop can also be reused by another person or agent without rebuilding the workflow from scratch.
Loops are not permission for an agent to run forever. The best ones are deliberately bounded. They include a real check, a clear stopping point, and a moment to hand control back to a person when judgment or approval is needed.
What Loopy does
Loopy gives your agent direct access to the ideas in the library. You can use it to:
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