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Judgment_Enhancement_Engine

by CHEN-feng123 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install judgment-enhancement-engine
Description
AI Agent judgment enhancement via Monte Carlo lookahead, risk-adjusted utility, and historical reflection. Use when an agent needs to evaluate multi-step act...
README (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

Usage Guidance
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.
Capability Analysis
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.
Capability Tags
crypto
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install judgment-enhancement-engine
  3. After installation, invoke the skill by name or use /judgment-enhancement-engine
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Slug judgment-enhancement-engine
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Judgment_Enhancement_Engine?

AI Agent judgment enhancement via Monte Carlo lookahead, risk-adjusted utility, and historical reflection. Use when an agent needs to evaluate multi-step act... It is an AI Agent Skill for Claude Code / OpenClaw, with 53 downloads so far.

How do I install Judgment_Enhancement_Engine?

Run "/install judgment-enhancement-engine" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Judgment_Enhancement_Engine free?

Yes, Judgment_Enhancement_Engine is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Judgment_Enhancement_Engine support?

Judgment_Enhancement_Engine is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Judgment_Enhancement_Engine?

It is built and maintained by CHEN-feng123 (@chen-feng123); the current version is v1.0.0.

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