Loop Library gives AI agents repeatable work routines
Loop Library is a set of repeatable routines for AI agents. Instead of asking an agent to try something once and guess the rest, a loop tells it how to check the result, choose the next useful step, and stop at the right time. The public website lets people browse routines, read them, and copy their prompts without installing anything.
An optional skill can help an agent find a matching routine, inspect an existing one for weak checks or unclear stopping rules, adapt it to a team’s tools and success standards, or design a new bounded routine through a short conversation. A good loop defines the goal, the evidence that shows whether the latest attempt worked, what to do with what was learned, and when to finish or ask a person for help. The example turns “make this website faster” into a measured cycle: find the slowest page, make one focused improvement, measure again, keep the change only if it helps, and stop when the target is met or further passes no longer improve results.
The skill supports Codex, Cursor, and Claude Code, and installing it does not automatically deploy code, delete data, send messages, or grant new . The project is under the and had 890 at the time checked.
Key points
- Loop Library provides repeatable routines for AI agents with goals, checks, next steps, and stop rules.
- The website can be used directly; the installable skill is optional.
- The skill helps find, inspect, repair, adapt, or loops.
- Choosing a loop does not automatically deploy code, delete data, send messages, or grant .
- The cost-saving angle is indirect: clear checks and stop rules can reduce wasted agent runs and token use.