AgentSkillsCN

tree-of-thoughts

系统性地评估已知选项，从中寻找“最佳”解决方案。探索多种推理路径，为各条分支打分，并剔除信心不足的路径。

SKILL.md
--- frontmatter
name: tree-of-thoughts
description: Systematic evaluation methodology for finding THE best solution among known options. Explores multiple reasoning paths, scores branches, and prunes low-confidence paths.
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Tree of Thoughts (ToT)

Find THE best solution by systematically exploring and evaluating multiple reasoning paths.

When to Use

  • •Clear success criteria exist
  • •Multiple known options to evaluate
  • •Need THE best answer (not all options)
  • •Time permits thorough analysis

Process

Phase 1: Branch Generation

Generate 3-5 distinct approaches to the problem:

code
Root Problem
├── Approach A: [description]
├── Approach B: [description]
├── Approach C: [description]
└── Approach D: [description]

Phase 2: Branch Evaluation

Score each branch on defined criteria (1-10):

ApproachFeasibilityImpactRiskEffortTotal
A?????
B?????
C?????

Phase 3: Pruning

Eliminate branches scoring below threshold (typically 60%):

  • •Keep top 2-3 branches
  • •Document why others were pruned
  • •Note any branches worth revisiting

Phase 4: Deep Exploration

For remaining branches, explore sub-options:

code
Approach A (Score: 8.2)
├── Variant A1: [refinement]
├── Variant A2: [refinement]
└── Variant A3: [refinement]

Phase 5: Final Selection

Compare top candidates:

  1. •Weighted scoring against criteria
  2. •Risk-adjusted evaluation
  3. •Implementation complexity
  4. •Select winner with confidence score

Output Template

markdown
## ToT Analysis: [Problem]

### Branches Explored
1. **[Approach A]** - [1-line description]
2. **[Approach B]** - [1-line description]
3. **[Approach C]** - [1-line description]

### Evaluation Matrix
| Approach | [Criteria 1] | [Criteria 2] | [Criteria 3] | Score |
|----------|--------------|--------------|--------------|-------|
| A | X/10 | X/10 | X/10 | X.X |
| B | X/10 | X/10 | X/10 | X.X |
| C | X/10 | X/10 | X/10 | X.X |

### Pruned (with reasons)
- [Approach D]: [why eliminated]

### Recommendation
**Winner: [Approach X]**
- Confidence: [high/medium/low]
- Key advantages: [list]
- Risks to monitor: [list]

Example

Problem: Choose database for real-time analytics

Branches:

  1. •PostgreSQL with TimescaleDB
  2. •ClickHouse
  3. •Apache Druid
  4. •Elasticsearch

Evaluation (criteria: query speed, scalability, ops complexity):

  • •ClickHouse: 9 + 9 + 7 = 25 ← Winner
  • •TimescaleDB: 7 + 7 + 9 = 23
  • •Druid: 8 + 9 + 5 = 22
  • •Elasticsearch: 6 + 8 + 6 = 20 (pruned)

Result: ClickHouse with 83% confidence