Generate an evidence-based workflow to recover from AI agent looping and drift.
npx skills add https://github.com/Pyl-Tech/stream-coding --skill self-debug
Please help me install this Agent Skill. Skill: self-debug Source: https://github.com/Pyl-Tech/stream-coding/tree/main/.agents/skills/self-debug Command: npx skills add https://github.com/Pyl-Tech/stream-coding --skill self-debug
Self-debug prevents wasted retries by helping an AI agent recognize looping or drifting, capture the exact failure state, diagnose the likely agent-level cause, and perform the smallest recovery step before escalating.
Use this skill when you notice the agent is looping by running /self-debug and generating a Self-Debug Report with capture, diagnosis, one minimal recovery action, and final escalation if still blocked.
High-intent search queries and answers about installing and using this skill.
To stop an AI coding agent from looping and burning tokens, you need an evidence-based debugging workflow that detects repeated tool calls, freezes the failure state, and applies a minimal recovery step before escalating. This prevents wasted retries when the agent gets stuck rather than the code being incorrect.
Context drift in AI-assisted coding occurs when the agent loses sight of the original objective. To recover, you capture the exact failure state, classify whether the agent or the spec failed, select a diagnosis pattern, and run a single discriminating check to verify the world state before proceeding.
You should use a self-debug workflow for agent failure recovery when retries repeat three or more times, token burn occurs, or context drifts away from the objective. It applies specifically when the agent is stuck rather than the code or specification being incorrect.
To diagnose whether your AI agent or code spec is causing repeated failures, you classify the failure mode by capturing the last successful step and the repeated pattern observed. This structured failure capture distinguishes agent-level looping from actual specification errors.
The best way to prevent an AI coding agent from repeating the same failed recovery cycle is to enforce an escalation guardrail that allows only one minimal recovery action. If still blocked, the agent outputs a structured report instead of retrying to avoid infinite loops.
This debugging workflow requires no external dependencies or specific components to function. It operates as a standalone process within AI-assisted coding environments to detect looping, capture failure state, and produce structured reports for escalation.
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