Loop/Prompt Library / Running AI agents well
๐ Audit your loops
Reviews all your running loops and recommends which to keep, change, retire, or kill, based on real results.
Use it when: You have several loops running and want to know which are worth the cost.
๐ Stops when: Every loop gets exactly one status backed by evidence.
Copy this loop
Review the loops in [list or registry] without running or editing any. For each, check its purpose, success test, budget, stop rules, and results log. For loops with numbers, recompute: how often runs set a new best, how many runs were wasted, and the average gain. Compare to the two previous periods. For other loops, check deliveries, failures, schedule, and budget. Give each one status: NOT ENOUGH EVIDENCE, KEEP, PIVOT, RETIRE, or KILL. Recommend only. Stop when every loop has a status.
Review every change the agent makes before you keep it. Set a budget or round limit โ the prompt includes one.
Idea from Forward Future's Loop Library ยท by quigleyBits (@quigleyBits). Prompt rewritten in plain English by FixKit AI.
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Before a session ends, writes a note so the next person or agent can continue without guessing.
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Before every meaningful action, the agent writes what it expects to happen, then checks if that is what really happened.
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Improves a prompt or piece of code through saved, fairly compared experiments that can pick up again later.
