| name | tot |
| description | Tree-of-Thought reasoning — branch a problem into multiple solution paths, explore each, score and prune branches via a model jury, then synthesize the best. Non-linear reasoning that can backtrack |
| user-invocable | true |
ToT - Tree-of-Thought
Explore a problem as a TREE of solution paths instead of one linear chain. Branch into distinct approaches, develop each, score them, prune the weak ones, and merge the best elements. Unlike linear chain-of-thought (/think), ToT can backtrack and recombine — which wins on problems with multiple viable strategies or where the first idea is often a trap.
Usage
/tot [problem]
/tot [branches] [problem] (branches default 3)
When to Use
- Multiple plausible approaches and it's unclear which wins
- Design/architecture decisions with real trade-offs
- Problems where the first/obvious path is often wrong
- You want breadth BEFORE committing to depth
/tot vs /think vs /judge
| /tot | /think | /judge |
|---|
| Shape | Tree: branch → score → prune → merge | Linear chain | Parallel vote |
| Strength | Explore + backtrack + recombine | Step-by-step depth | Diverse opinions |
| Use when | "Many ways to do this" | "Walk me through this" | "Which is best?" |
Instructions
When user invokes /tot [problem] (parse optional leading branch count, default 3):
Step 1: Branch into distinct paths
mcp__tachibot-mcp__execute_prompt_technique({
technique: "tree_of_thoughts",
tool: "deepseek_reason",
query: "[problem] — branch into [N] genuinely DISTINCT solution paths. For each path give: the core idea, how it would work, and its main risk."
})
deepseek_reason is a strong brancher; fall back to grok_reason or gemini_analyze_text.
Present the branches to the user before scoring.
Step 2: Score & prune the branches (jury)
Get independent scoring so you don't fall in love with one branch:
mcp__tachibot-mcp__jury({
question: "Score these solution paths for [problem] on feasibility, risk, and expected payoff (1–10 each). Rank them and say which to prune.\n\nPaths:\n[the N branches from Step 1]",
jurors: "deepseek,grok,kimi",
mode: "rank"
})
Prune the lowest-ranked branches. Keep the top 1–2.
Step 3: Deepen the surviving branch(es)
For each survivor, develop it with a focused reasoning pass:
mcp__tachibot-mcp__nextThought({
thought: "Develop the winning path for [problem]: [branch]\n\nFlesh out: concrete steps, the hardest sub-part, how to de-risk it, and what would make it fail.",
model: "deepseek",
executeModel: true,
contextWindow: "recent",
nextThoughtNeeded: true
})
Step 4: Synthesize (merge best elements)
mcp__tachibot-mcp__nextThought({
thought: "Synthesize the final answer for [problem].\n\nWinning path(s): [...]\nJury scores/rationale: [...]\n\nMerge the best elements across branches into ONE recommendation. State: the approach, why it beat the pruned branches, and the top risk to watch.",
model: "gemini",
executeModel: true,
contextWindow: "all",
nextThoughtNeeded: false
})
If jury is unavailable, score branches with a single nextThought judge (contextWindow: "all"). If execute_prompt_technique is unavailable, branch with nextThought (contextWindow: "none") asking for N distinct paths.
Step 5: Present Results
## Tree-of-Thought: [problem]
### Branches Explored ([N])
1. [Path A] — score [x/10] — [kept/pruned]
2. [Path B] — score [x/10] — [kept/pruned]
...
### Recommendation
[The synthesized approach]
### Why It Won
[How it beat the pruned branches]
### Top Risk
[What to watch]
Examples
/tot how should we shard this 10TB table?/tot 4 design a caching strategy for a read-heavy API with bursty writes/tot pick a migration path from REST to GraphQL with zero downtime
