agent-introspection-debugging

What problem does it solve? AI agent runs often fail by looping on the same tools, burning tokens without progress, or drifting from the intended task. This Skill provides a structured self-debugging workflow so the agent captures the failure, diagnoses the root cause, applies a contained recovery, and reports the outcome instead of retrying blindly. ## Core Features & Use Cases - Four-Phase Debug Loop: Failure capture, root-cause diagnosis, contained recovery, and a structured introspection report. - Failure Pattern Matching: A diagnostic table maps symptoms like maximum tool calls, context overflow, ECONNREFUSED, 429 quota exhaustion, and stale file state to likely causes and checks. - Recovery Heuristics: Ordered interventions that restate the objective, verify world state, shrink scope, and run one discriminating check before retrying. - Use Case: An agent hits its tool-call limit while repeatedly running the same failing test command. The skill guides it to capture the error, classify the loop pattern, isolate the single failing test, apply one corrective action, and produce a self-debug report for the human. ## Quick Start Ask the agent to use the agent-introspection-debugging skill to diagnose why its current run keeps looping and produce a self-debug report.