Judgment_Enhancement_Engine
PassAudited by ClawScan on May 4, 2026.
Overview
This appears to be a local decision-simulation helper with no evidence of credential access, network exfiltration, or destructive behavior.
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.
Findings (2)
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.