Mindmap Mcts Skill

MindMap-MCTS

Evidence-guided reasoning trees for Codex and Claude Code agents.


MindMap-MCTS is an installable Codex skill and Claude Code skill pattern for complex debugging, architecture decisions, and research synthesis. It turns agent reasoning into an auditable reasoning tree: propose branches, run real probes, score evidence, backpropagate values, and choose the next step with lightweight Monte Carlo Tree Search (MCTS) and UCB.

In short: this gives AI agents a visible tree search loop instead of a hidden linear trial-and-error thread.

Search Keywords

Codex skill, Claude Code skill, AI agents, agentic AI, agent reasoning, reasoning tree, evidence-guided reasoning, tree search, Monte Carlo Tree Search, MCTS, UCB, debugging workflow, planning workflow, architecture tradeoffs, research synthesis.

中文摘要：MindMap-MCTS 给 Codex、Claude Code 和其他智能体安装一个可见的推理树，让复杂问题可以用“分支探索、证据评分、MCTS/UCB 选择、回传更新”的方式推进，而不是线性瞎试。

What It Does

• Creates a JSON reasoning tree as the truth source.
• Renders readable Markdown, static HTML, and interactive Markmap HTML mindmap views.
• Selects the next branch with lightweight MCTS/UCB.
• Records V value, N visits, state, probe metadata, and evidence per node.
• Preserves pruned branches so dead ends are not retried.
• Helps agents stop when a high-evidence path converges or when a user decision is needed.


Alpha-Style Search Architecture

MindMap-MCTS turns Codex problem solving into a compact search architecture:

Alpha-style component	MindMap-MCTS counterpart
Policy-like proposal	Codex proposes concrete hypotheses, designs, or next actions.
Search tree	.tree.json stores branches, states, visits, values, and evidence.
Value signal	Probe-backed evidence scores come from tests, logs, code reads, papers, or user input.
Backpropagation	The CLI