Judgment_Enhancement_Engine
PassAudited by VirusTotal on May 4, 2026.
Overview
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.
Findings (0)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
Running setup will execute local code from the skill package on the user's machine.
The optional setup script runs local Python verification code and then runs the engine demo. This is expected for a local Python skill and shows no download, network, or destructive behavior, but it is still code execution the user should choose deliberately.
$PYTHON -c "... from engine import JudgmentEnhancementEngine ..."\n$PYTHON "$ENGINE_PY"
Review the setup script before running it, and run it only from a trusted installation location.
Past recorded outcomes may bias future suggested actions, and sensitive action labels or utilities could remain in process memory until cleared.
The engine stores bounded in-memory state/action outcome history and uses it for historical correction. This is disclosed and purpose-aligned, but stored outcomes can influence later recommendations if the same engine instance is reused.
self._history: List[Tuple[int, Action, float]] = []\n...\ndef record_outcome(self, state: State, action: Action, actual_utility: float) -> None:
Use trusted outcome data, avoid placing secrets in action/state labels, and call clear_history or reduce history_size when history should not carry over.