Agent Self Verification Gate

The problem

Long AI coding sessions eventually suffer from context rot, leading to regression loops, ignored instructions, and hallucinated logic. You end up wasting time re-rejecting the same ideas or fixing code that was already working twenty minutes ago.

What it does

• Detects regression loops by identifying suggestions that conflict with earlier session decisions.
• Monitors instruction drift to ensure the agent adheres to original constraints and project styles.
• Triggers a structured checkpoint protocol to save progress and clear stale context.
• Forces a recalibration phase where the agent re-reads files from disk instead of relying on degraded memory.
• Generates recoverable markdown snapshots of decisions, directives, and completed tasks.

Frameworks & tools

Designed for AI agents including Claude Code, Cursor Agent, and Codex CLI. Works across any programming language by managing the agent's internal state via filesystem checkpoints.

Why this beats prompting it yourself

Manually telling an agent to "remember everything" fails because the model's self-perception of its own memory is flawed. This skill provides a specific algorithmic protocol for the agent to audit its own output quality and perform a hard reset when specific drift signals are met.

Use cases

• Executing complex, multi-file refactors that last over 30 minutes.
• Maintaining strict architectural patterns across a long feature sprint.
• Preventing agents from re-introducing bugs that were fixed earlier in the session.

Known limitations

Does not resolve model-level infrastructure errors like API rate limits. Effective only for within-session recovery, not for long-term project memory across different days.