Forward-Future/loop-library
A library for agents that know when to stop
Loop Library catalogs iterative AI workflows that force agents to check their work and stop when they’re done, instead of spiraling through open-ended tasks.
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What it does Loop Library is a public catalog of iterative AI-agent workflows—called “loops”—plus an optional skill that helps agents find, audit, adapt, or design them. Each loop replaces a one-shot prompt with a bounded cycle: attempt, verify, learn, and either repeat or stop. The website hosts the catalog, while the installable skill lets agents query it during conversation to pick or tailor a loop for a specific task.
The interesting bit The project treats the exit strategy as part of the prompt itself. Each loop is essentially a unit test for agent behavior: it defines the assertion first, then the repetition logic, which keeps agents from confusing motion with progress.
Key highlights
- Public catalog of bounded, repeatable workflows for engineering, evaluation, operations, content, and design
- Each loop specifies what to attempt, how to verify it, what to learn, and when to stop or escalate
- Optional agent skill integrates with Codex, Cursor, and Claude Code to find, audit, adapt, or design loops conversationally
- Agents can consume the catalog directly via JSON, plain text, or dedicated agent guides without installing the skill
- New loops can be published without a site redeploy or repository commit
Verdict Give this a spin if your agents currently treat “keep improving this” as a license to iterate forever. If you only fire off single-turn prompts, you probably do not need a library of playbooks.