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SamuelH98
by SamuelH98

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    • A
      license
      Not graded
      quality
      D
      maintenance
      Detects and breaks repetitive fix loops in AI coding assistants by tracking attempts and providing escalating intervention strategies. It utilizes error fingerprinting and similarity analysis to redirect the AI toward new approaches when it gets stuck on the same error.
      4
      4
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides coding agents with durable, cross-session lessons-learned memory, enforcing that success or failure verdicts can only come from human approval, human correction, or objective metrics—never from the agent itself.
      Apache 2.0
    • A
      license
      A
      quality
      A
      maintenance
      Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.
      2
      MIT

    TDQS

    A4.3/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: reporting a failed attempt, confirming a search, viewing status, and resetting state. No overlap or ambiguity between them.

    Naming Consistency4/5

    Three tools follow the verb_noun pattern (report_attempt, confirm_search, reset_loop), while loop_status is noun_noun but still reads as a status query. Minor deviation but overall predictable.

    Tool Count5/5

    Four tools is well-scoped for this narrow domain of tracking and managing failed-attempt loops. No redundant tools, each earns its place.

    Completeness5/5

    The domain is fully covered: report failures, unblock after search, view history, and reset. No obvious gaps for the stated purpose.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues