Skill: chen-feng123/judgment-enhancement-engine (version 1.0.0)

--- name: 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 action consequences under uncertainty with configurable risk tolerance. --- # Judgment Enhancement Engine Enhance AI agent decision-making under uncertainty through recursive Monte Carlo lookahead simulation, risk-adjusted utility, and historical reflection. ## Quick Start ```bash # 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 ```python 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)