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Judgment_Enhancement_Engine

作者 CHEN-feng123 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install judgment-enhancement-engine
功能描述
AI Agent judgment enhancement via Monte Carlo lookahead, risk-adjusted utility, and historical reflection. Use when an agent needs to evaluate multi-step act...
使用说明 (SKILL.md)

Judgment Enhancement Engine

Enhance AI agent decision-making under uncertainty through recursive Monte Carlo lookahead simulation, risk-adjusted utility, and historical reflection.

Quick Start

# Run built-in GridWorld demo
python skills/judgment-enhancement-engine/engine.py

# One-click setup
bash skills/judgment-enhancement-engine/scripts/setup.sh      # Linux/macOS/WSL
skills\judgment-enhancement-engine\scripts\setup.bat          # Windows

Core Usage

from engine import JudgmentEnhancementEngine, JudgmentResult

# 1. Define your world model (must implement WorldModel protocol)
class MyWorldModel:
    def get_possible_outcomes(self, state, action):
        # Returns [(next_state, probability, reward), ...]
        ...

    def is_terminal(self, state):
        ...

    def get_legal_actions(self, state):
        ...

# 2. Define objective function
class MyObjective:
    def evaluate(self, state):
        return float_score  # higher = better

# 3. Create engine
engine = JudgmentEnhancementEngine(
    world_model=MyWorldModel(),
    objective=MyObjective(),
    risk_tolerance=0.5,      # 0=extreme risk-averse, 1=risk-neutral
    lookahead_depth=3,       # recursion depth
    simulation_breadth=4,    # max actions evaluated per level
    use_greedy_rollout=True, # True=greedy (accurate), False=uniform (fast)
    max_compute_time_sec=2.0 # timeout protection
)

# 4. Make a judgment
result = engine.enhance_judgment(current_state)
print(f"Best action: {result.best_action}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Reasoning: {result.reasoning}")

# 5. Record actual outcome (for historical correction)
engine.record_outcome(state, action, actual_utility)

# 6. Optional: clear history
engine.clear_history()

Configuration

Parameter Default Description
risk_tolerance 0.5 0=extreme risk-averse, 1=risk-neutral
lookahead_depth 2 Recursive lookahead levels
simulation_breadth 3 Max actions evaluated per level
history_size 100 Historical records kept
max_compute_time_sec 1.0 Timeout protection (seconds)
use_greedy_rollout True True=greedy (accurate), False=uniform (fast)

JudgmentResult Fields

Field Type Description
best_action Action Selected best action
scores dict Risk-adjusted utility per action
raw_utilities dict Raw expected utility per action
risk_metrics dict Expectation/variance/std/VaR95 per action
reasoning str Human-readable decision reasoning
confidence float 0~1 confidence score

Example: GridWorld

Built-in demo() shows a 5x5 grid world with obstacles and a goal. Run python engine.py to see it in action.

Installation

Method Command
One-click (Linux/macOS) bash scripts/setup.sh
One-click (Windows) scripts\setup.bat
Copy-only Copy engine.py to your project
ClawHub clawhub install judgment-enhancement-engine

File Structure

judgment-enhancement-engine/
├── SKILL.md
├── engine.py              # Core engine (~10KB)
├── index.js               # Node.js bridge
├── package.json
├── assets/
│   └── icon.svg
├── references/
│   ├── API_SPEC.md
│   └── USE_GUIDE.md
└── scripts/
    ├── setup.sh
    ├── setup.bat
    ├── test-basic.py
    └── test-client.js

License

MIT

安全使用建议
This skill looks safe to evaluate as a local Python decision-support library. Before installing, review the setup script, treat its recommendations as advisory rather than automatic permission to act, and clear or limit history if your states/actions may contain sensitive information.
功能分析
Type: OpenClaw Skill Name: judgment-enhancement-engine Version: 1.0.0 The skill bundle implements a legitimate Monte Carlo lookahead engine for decision-making enhancement in AI agents. The core logic in 'engine.py' uses standard mathematical and probabilistic methods (expected utility, variance, risk-adjusted scoring) without any external dependencies, network access, or sensitive file system operations. The setup scripts ('scripts/setup.sh') and test files are focused solely on environment verification and running the built-in GridWorld demo.
能力标签
crypto
能力评估
Purpose & Capability
The artifacts consistently describe and implement a Monte Carlo/risk-adjusted decision helper. The visible code is local-only and does not show network calls, credential use, destructive actions, or account access.
Instruction Scope
The skill provides advisory outputs such as best_action, scores, confidence, and reasoning; the artifacts do not instruct the agent to automatically execute high-impact actions.
Install Mechanism
There is no automatic install spec, but the documentation offers user-run setup/demo commands that execute local Python. This is purpose-aligned, but users should review commands before running them.
Credentials
The skill requires no declared credentials, environment variables, network services, or broad filesystem access. Python/bash usage is proportionate to the stated local library purpose.
Persistence & Privilege
The engine keeps bounded in-memory decision history to adjust future judgments. This is disclosed and core to the purpose, with a clear_history method, and there is no evidence of disk persistence or privileged access.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install judgment-enhancement-engine
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /judgment-enhancement-engine 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of the judgment-enhancement-engine. - Enables AI agent decision-making under uncertainty using recursive Monte Carlo lookahead, risk-adjusted utility, and historical reflection. - Provides configurable parameters for risk tolerance, lookahead depth, and simulation breadth. - Includes a built-in GridWorld demo and easy setup scripts for all major platforms. - Supplies detailed API documentation and usage examples for quick integration.
元数据
Slug judgment-enhancement-engine
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Judgment_Enhancement_Engine 是什么?

AI Agent judgment enhancement via Monte Carlo lookahead, risk-adjusted utility, and historical reflection. Use when an agent needs to evaluate multi-step act... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 53 次。

如何安装 Judgment_Enhancement_Engine?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install judgment-enhancement-engine」即可一键安装,无需额外配置。

Judgment_Enhancement_Engine 是免费的吗?

是的,Judgment_Enhancement_Engine 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Judgment_Enhancement_Engine 支持哪些平台?

Judgment_Enhancement_Engine 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Judgment_Enhancement_Engine?

由 CHEN-feng123(@chen-feng123)开发并维护,当前版本 v1.0.0。

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