>Agents look magical in demos and fall apart the second something unexpected happens. I’ve watched this pattern play out enough times to stop trusting the hype. These three prompts won’t fix every failure mode, but they’ll get you a tighter spec, a faster debug loop, and a runnable starter you can poke at. Paste, iterate, ship.
[Claude]
You are an agent architect. Given the task ‘monitor a folder, summarize new files, and email a digest daily’, produce a one-page agent spec: the trigger, the tools it needs, the decision rules for when to stop and ask a human, and a fallback for each failure mode. Output as a markdown spec with a numbered runbook. No generic advice; every rule must be executable.
[ChatGPT]
Act as a debugging partner for a flaky AI agent. Paste your agent’s logs and its last three outputs. Identify the exact step where it diverged from its instructions, name the likely cause (context drift, missing tool, ambiguous goal), and rewrite that one instruction so the failure cannot recur. Output a before/after table of the changed instruction plus a one-line regression test.
[Cursor]
In this repo, build a minimal Python agent that takes a natural-language goal, breaks it into 3 steps, and executes each with a retry-and-rollback guard. Use only the standard library plus requests. Add a config file for the model endpoint and a dry-run flag. Output the code files, a 5-line usage example, and a test that proves rollback works when a step fails.
Full archive at aitoolalliance.com/tag/prompt-pack.