Forward detailed error information between consecutive AI coding task attempts.
npx skills add https://github.com/logic-math/rick --skill failure-feedback
Please help me install this Agent Skill. Skill: failure-feedback Source: https://github.com/logic-math/rick/tree/main/.rick/skills/failure_feedback_skill Command: npx skills add https://github.com/logic-math/rick --skill failure-feedback
This Skill provides a mechanism for passing detailed failure information between consecutive task attempts during the AI coding process, improving accuracy and ease of debugging.
To enable detailed failure feedback for a specific task, use the command: rick doing --enable-failure-feedback job_N.
High-intent search queries and answers about installing and using this skill.
To pass error tracebacks to the next AI coding attempt, you need a feedback mechanism that forwards detailed failure information between task retries. This preserves context integrity and improves debugging accuracy.
AI coding prompts grow indefinitely during task retries because error messages accumulate without management. You need a prompt management mechanism to limit feedback size, maintaining context integrity and execution efficiency.
The best way to handle error context preservation in AI debugging is implementing a feedback mechanism that tracks and forwards detailed error information between consecutive attempts, ensuring the AI understands previous failures without exceeding prompt limits.
To enable failure feedback for a specific AI coding task, use the command `rick doing --enable-failure-feedback job_N`. This activates detailed error output and prompt management for that particular job.
Yes, you can use this failure feedback mechanism for any AI coding task requiring retries. It is specifically designed for scenarios where task retries are necessary, managing error feedback size and preserving context integrity.
The limitation of passing detailed error messages is prompt size inflation. Without mechanisms to manage error feedback size and preserve context integrity, accumulating tracebacks can degrade AI processing efficiency and exceed token limits.
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