preflight-checks

name	preflight-checks
description	Test-driven behavioral verification for AI agents. Catches silent degradation when agent loads memory but doesn't apply learned behaviors. Use when building agent with persistent memory, testing after updates, or ensuring behavioral consistency across sessions.
metadata	{"openclaw":{"category":"testing","tags":["testing","verification","behavioral","memory","consistency"]}}

Pre-Flight Checks Skill

Test-driven behavioral verification for AI agents

Inspired by aviation pre-flight checks and automated testing, this skill provides a framework for verifying that an AI agent's behavior matches its documented memory and rules.

Problem

Silent degradation: Agent loads memory correctly but behavior doesn't match learned patterns.

Memory loaded ✅ → Rules understood ✅ → But behavior wrong ❌

Why this happens:

• Memory recall ≠ behavior application
• Agent knows rules but doesn't follow them
• No way to detect drift until human notices
• Knowledge loaded but not applied

Solution

Behavioral unit tests for agents:

1. CHECKS file: Scenarios requiring behavioral responses
2. ANSWERS file: Expected correct behavior + wrong answers
3. Run checks: Agent answers scenarios after loading memory
4. Compare: Agent's answers vs expected answers
5. Score: Pass/fail with specific feedback

Like aviation pre-flight: