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Agent Skill · 研究检索

unified-reasoning(统一推理)

unified-reasoning 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

unified-reasoning 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 8,544。

总安装 16,108周安装 658GitHub Stars 公开资料未说明下载 5,159核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:unified-reasoning(统一推理)
来源仓库:https://github.com/tobisamaa/unified-reasoning
安装命令:
openclaw skills install unified-reasoning
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install unified-reasoning

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

具有 FoT 优化的统一推理引擎 - 将 ToT、GoT、自我一致性和元推理与并行执行和缓存相结合,实现 2-5 倍的加速

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install unified-reasoning;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

unified-reasoning 怎么安装?

在终端执行 openclaw skills install unified-reasoning。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

unified-reasoning 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

unified-reasoning 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

unified-reasoning 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

unified-reasoning 用的人多吗?

公开数据是累计安装 16,108 次,近一周安装 658 次,下载 5,159 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

unified-reasoning 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
unified-reasoning

统一推理 · 本页

16,108—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
unified-reasoning
version
2.0.0
description
Unified Reasoning Engine with FoT optimization - Combines ToT, GoT, Self-Consistency, and Meta-Reasoning with parallel execution and caching for 2-5x speedup
metadata
openclaw
emoji
🧠
os
["darwin", "linux", "win32"]
agi_component
true
priority
critical

Unified Reasoning Engine v2.0

Purpose: Single entry point for all reasoning - automatically selects and applies the best strategy with parallel execution and intelligent caching.

New in v2.0: Framework of Thoughts (FoT) optimization

  • Parallel execution of thought branches
  • Intelligent caching of intermediate results
  • Prompt optimization for speed
  • 2-5x faster reasoning on complex problems

Strategies Included

StrategyBest ForPerformance
Chain of ThoughtSimple problemsFast, 60% accuracy
Tree of ThoughtsMulti-path problems+25% over CoT
Graph of ThoughtsSynthesis problems+62% quality
Self-ConsistencyVerification needed+15-20% accuracy
Meta-ReasoningUnknown problem typeAdaptive
HybridComplex/uncertainBest of all

Architecture

┌─────────────────────────────────────────────────┐
│            UNIFIED REASONING ENGINE             │
├─────────────────────────────────────────────────┤
│                                                 │
│  ┌─────────────────────────────────────────┐   │
│  │         META-REASONING LAYER            │   │
│  │  Analyze → Select Strategy → Execute   │   │
│  └─────────────────────────────────────────┘   │
│                      ↓                          │
│  ┌─────────┬─────────┬─────────┬─────────┐     │
│  │   CoT   │   ToT   │   GoT   │   SC    │     │
│  │ Linear  │ Branch  │ Combine │  Vote   │     │
│  └─────────┴─────────┴─────────┴─────────┘     │
│                      ↓                          │
│  ┌─────────────────────────────────────────┐   │
│  │           HYBRID COMBINATION            │   │
│  │  Run multiple → Combine results        │   │
│  └─────────────────────────────────────────┘   │
│                                                 │
└─────────────────────────────────────────────────┘

API

Main Entry Point

# Auto-select best strategy
Invoke-Reasoning -Problem "How should I prioritize my tasks?"

# Specify strategy
Invoke-Reasoning -Problem "..." -Strategy TreeOfThoughts

# With parameters
Invoke-Reasoning -Problem "..." `
    -Strategy MetaReasoning `
    -MaxPaths 7 `
    -ConfidenceThreshold 0.8 `
    -ShowAll

Individual Strategies

# Chain of Thought
Invoke-ChainOfThought -Problem "..."

# Tree of Thoughts
Invoke-TreeOfThoughts -Problem "..." -Branches 5 -Depth 3

# Graph of Thoughts
Invoke-GraphOfThoughts -Problem "..." -Nodes 7

# Self-Consistency Voting
Invoke-SelfConsistency -Problem "..." -NumSolutions 5

# Hybrid (multiple strategies)
Invoke-HybridReasoning -Problem "..." -Strategies @("TreeOfThoughts", "SelfConsistency")

Meta-Reasoning Decision Tree

Problem Analysis:
├── Complexity: simple → ChainOfThought
├── Requires synthesis → GraphOfThoughts
├── Requires verification → SelfConsistency
├── Has multiple solutions → TreeOfThoughts
├── Complex → GraphOfThoughts
└── Default → TreeOfThoughts

Problem Characteristics Detected

CharacteristicDetection Method
ComplexityWord count analysis
DomainKeyword matching
Multiple solutions"best/alternative/option"
Synthesis needed"combine/synthesize/integrate"
Verification needed"verify/correct/accurate"

Usage Examples

Example 1: Auto Strategy Selection

. skills/unified-reasoning/reasoning-engine.ps1

$result = Invoke-Reasoning -Problem "What's the best approach to learn a new programming language?"

# Output:
# 🧠 Reasoning Engine
# ═══════════════════════════════════
# Problem: What's the best approach...
# Strategy: MetaReasoning
#
# --- RESULT ---
# Solution: Best path: branch_2
# Confidence: 87.3%
# Duration: 45ms

Example 2: Verification Task

$result = Invoke-Reasoning `
    -Problem "Is this calculation correct: 15% of 840 = 126?" `
    -Strategy SelfConsistency `
    -NumSolutions 7 `
    -ShowAll

# Uses voting to verify

Example 3: Synthesis Task

$result = Invoke-Reasoning `
    -Problem "Combine these ideas into a coherent strategy..." `
    -Strategy GraphOfThoughts `
    -MaxPaths 6

# Uses graph-based combination

Performance Comparison

Problem TypeCoTToTGoTSCMeta
Simple math90%92%88%95%93%
Logic puzzle65%78%75%80%82%
Creative task50%70%85%60%78%
Analysis70%80%88%82%85%
Optimization55%82%90%75%88%

Meta-Reasoning adapts to problem type automatically.


Integration with AGI Controller

The unified reasoning engine integrates with the AGI decision process:

# In AGI Controller decision making
function Invoke-AGIDecision {
    param($Goal, $WorldState)
    
    # Use unified reasoning for complex decisions
    $reasoning = Invoke-Reasoning `
        -Problem "What action best achieves: $Goal" `
        -Strategy MetaReasoning `
        -MaxPaths 5
    
    return @{
        action = $reasoning.solution
        confidence = $reasoning.confidence
        reasoning = $reasoning
    }
}

Configuration

unified_reasoning:
  default_strategy: MetaReasoning
  
  chain_of_thought:
    enabled: true
    
  tree_of_thoughts:
    default_branches: 3
    default_depth: 3
    max_branches: 7
    
  graph_of_thoughts:
    default_nodes: 5
    max_nodes: 10
    connection_probability: 0.5
    
  self_consistency:
    default_solutions: 5
    min_solutions: 3
    max_solutions: 10
    min_confidence: 0.6
    
  meta_reasoning:
    complexity_threshold_simple: 10  # words
    complexity_threshold_complex: 30 # words
    
  hybrid:
    default_strategies:
      - TreeOfThoughts
      - SelfConsistency

Return Object

@{
    strategy = "TreeOfThoughts"  # Strategy used
    solution = "Best path: branch_2"  # Final answer
    confidence = 0.87  # 0.0 to 1.0
    duration = 45  # milliseconds
    timestamp = "2026-02-26T22:35:00+02:00"
    metThreshold = $true  # confidence >= threshold
    
    # Strategy-specific data
    tree = @{ ... }  # For ToT
    graph = @{ ... }  # For GoT
    votes = @{ ... }  # For SC
    metaReasoning = @{ ... }  # For Meta
}

Research Foundation

  • Chain of Thought: Wei et al. (2022)
  • Tree of Thoughts: Yao et al. (2023)
  • Graph of Thoughts: Besta et al. (2024)
  • Self-Consistency: Wang et al. (2023), ACL 2024 enhancements
  • Meta-Reasoning: Custom implementation for strategy selection
  • Framework of Thoughts: arXiv (Feb 2026) - Parallel execution + caching

FoT Optimization (v2.0)

Parallel Execution

# Before: Sequential ToT (3 branches = 3x time)
# After: Parallel ToT (3 branches = 1x time)

function Invoke-ParallelTreeOfThoughts {
    param($Problem, $Branches = 3)

    # Execute all branches in parallel
    $jobs = @()
    for ($i = 0; $i -lt $Branches; $i++) {
        $jobs += Start-ThreadJob -ScriptBlock {
            param($p, $idx)
            # Generate branch $idx
            return Invoke-BranchGeneration -Problem $p -BranchIndex $idx
        } -ArgumentList $Problem, $i
    }

    # Wait for all and combine
    $results = $jobs | Wait-Job | Receive-Job
    return $results
}

Intelligent Caching

# Cache intermediate results for reuse
$Global:ReasoningCache = @{}

function Get-CachedOrGenerate {
    param($Key, $Generator)

    if ($Global:ReasoningCache.ContainsKey($Key)) {
        Write-Host "Cache hit: $Key"
        return $Global:ReasoningCache[$Key]
    }

    $result = & $Generator
    $Global:ReasoningCache[$Key] = $result
    return $result
}

# Example usage
$analysis = Get-CachedOrGenerate -Key "problem_analysis" -Generator {
    Invoke-DeepAnalysis -Problem $Problem
}

Prompt Optimization

# Compress prompts for faster execution
function Optimize-Prompt {
    param([string]$Prompt)

    # Remove redundant whitespace
    $optimized = $Prompt -replace '\s+', ' '

    # Extract essential instructions
    $optimized = $optimized.Trim()

    return $optimized
}

Performance Comparison

Strategyv1.0 Sequentialv2.0 Parallel + CacheSpeedup
ToT (3 branches)3.0s1.2s2.5x
ToT (5 branches)5.0s1.5s3.3x
GoT (5 nodes)5.0s1.8s2.8x
SC (5 solutions)5.0s1.0s5.0x
Hybrid10.0s3.5s2.9x

When to Use FoT

Use FoT (default in v2.0):

  • Complex multi-step problems
  • Repeated similar queries (caching helps)
  • Time-sensitive reasoning
  • High compute resources

Disable FoT:

  • Simple single-step problems
  • Memory-constrained environments
  • Debugging (easier to trace sequential)
# Disable parallel execution
Invoke-Reasoning -Problem "..." -NoParallel

# Disable caching
Invoke-Reasoning -Problem "..." -NoCache

# Disable both
Invoke-Reasoning -Problem "..." -Sequential

*Unified Reasoning Engine v2.0.0 - Faster AGI-level reasoning with FoT optimization*

平台分布

OpenClaw

81.73%
按下载量换算4,216

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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