---
name: principle-make-operations-idempotent
description: "Apply when designing commands, lifecycle steps, or processing loops that run amid crashes, restarts, and retries. Converge to the same end state regardless of partial prior runs."
disable-model-invocation: true
---
## OpenAI runtime contract

Read [the adapter contract](../../references/runtime-contract.md) before this workflow. It maps provider selection, agent calls, loops, persistent state, history, verification, and authorization. That contract supersedes Cursor-specific execution syntax and unverified model fallbacks below; the engineering steps remain required. Use native tools in the current host by default. Cloud uses its available OpenAI models. No remote laptop connection, server URL or MCP setup is permitted. Optional local CLI adapters require explicit environment-local selection. Do not claim cross-provider diversity for OpenAI-only review.


# Make Operations Idempotent

Design operations so they converge to the correct state regardless of how many times they run or where they start from. Every state-mutating operation should answer: "What happens if this runs twice? What happens if the previous run crashed halfway?"

**Why:** Commands, lifecycle operations, and processing loops run where crashes, restarts, and retries are normal. If partial state changes the next run's outcome, every restart becomes a debugging session.

**The pattern:**
- Convergent startup: scan for existing state, clean stale artifacts, adopt live sessions
- Content-based cleanup: compare by content equivalence, not creation order
- Self-healing locks: use PID-based stale lock detection
- Idempotent scheduling: failed work respawns cleanly, fresh input regenerated after each cycle

**The test:**
1. What happens if this runs twice in a row?
2. What happens if the previous run crashed at every possible point?
3. Does re-execution converge to the same end state?

If any answer is "it depends on what state was left behind," the operation needs a reconciliation step.