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AI Agent 分析
AD

UditAkhourii/adhd

ADHD — a skill for coding agents. Tree-of-thought with pruning, built on the Claude & Codex Agent SDK. Fans out parallel divergent thoughts under different cognitive frames, scores, prunes traps, deepens the survivors. The no-brainer skill for creative and interdisciplinary work.

Star
2.9k
语言
TypeScript
GitHub
来源与合规提示最近同步: Jul 31, 2026

Git-Stars 是独立产品,不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成,依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。

原始 GitHub 来源方法论编辑政策

概览

ADHD is a skill for coding agents that implements a tree-of-thought with pruning approach, built on the Claude & Codex Agent SDK. It fans out parallel divergent thoughts under different cognitive frames, scores them, prunes traps, and deepens survivors, providing an architectural fix for premature convergence in autoregressive reasoning.

安装方式

npm install adhd-agent

解决的问题

ADHD solves the problem of premature convergence in linear Chain-of-Thought reasoning, where the model anchors on its first output. Unlike standard Tree-of-Thought, it spawns isolated reasoning processes with zero shared context during divergence, then uses a separate critic pass to score, cluster, prune traps, and deepen survivors, enabling more creative and diverse solutions.

可以构建什么

Developers can build agents that generate a wide range of creative and non-obvious solutions for design decisions, fuzzy debugging, naming, API surface design, strategy, and any prompt requiring multiple approaches. Real-world use cases include generating diverse retry/timeout strategies, exploring alternative architectures, and surfacing novel ideas that a single-shot approach would miss. The ceiling is high for interdisciplinary and creative work where breadth and novelty are valued over quick, conventional answers.

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结论

ADHD is ideal for developers building coding agents that need creative, diverse, and non-obvious solutions, especially for open-ended problems. It should be avoided when speed and simplicity are paramount, as the parallel divergence adds overhead. The key trade-off is between breadth/novelty and efficiency/conventionality.

由 Git-Stars 分析 - 7/29/2026