claude-code - 💡(How to fix) Fix [FEATURE] Users should receive token credits when the AI wastes tokens through repeated incorrect analysis [1 participants]

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anthropics/claude-code#55138Fetched 2026-05-01 05:45:17
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Preflight Checklist

  • I have searched existing requests and this feature hasn't been requested yet
  • This is a single feature request (not multiple features)

Problem Statement

Multiple unnecessary server restarts, Wrong IAM Diagnosis, Circular debugging that could have been resolved with one curl command

Proposed Solution

Users should receive token credits

Alternative Solutions

No response

Priority

High - Significant impact on productivity

Feature Category

Other

Use Case Example

No response

Additional Context

No response

extent analysis

TL;DR

Implementing a token credit system for users could potentially reduce unnecessary server restarts and improve debugging efficiency.

Guidance

  • Investigate the current server restart process to identify areas where token credits could be integrated to prevent unnecessary restarts.
  • Consider implementing a logging system to track the number of restarts and their causes to better understand the impact of token credits.
  • Develop a clear plan for how token credits would be awarded, redeemed, and managed to ensure a fair and effective system.
  • Evaluate the potential benefits of token credits on debugging efficiency, such as reducing circular debugging.

Notes

The proposed solution lacks specific technical details, making it challenging to provide a more concrete implementation plan. Further discussion and clarification on the token credit system and its intended functionality are necessary.

Recommendation

Apply workaround: Implementing a token credit system, as it may help mitigate the issues of unnecessary server restarts and improve debugging efficiency, although more information is needed to fully understand its potential impact.

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claude-code - 💡(How to fix) Fix [FEATURE] Users should receive token credits when the AI wastes tokens through repeated incorrect analysis [1 participants]