Self Aware Prediction System

Provides predictions with quantified uncertainty by evaluating data completeness, prediction type, and confidence to inform decision-making risks.

This skill appears internally coherent and low-risk: it contains documentation and a small local Python function that computes a simple confidence score based on declared inputs. Before installing or enabling for automated use, consider: 1) test the script on representative inputs to confirm its behavior and that its simple heuristics match your needs (it uses a fixed base score and static weights); 2) verify there are no hidden files or later updates that introduce network calls or credential usage; 3) if you plan to supply sensitive content to the skill, ensure your agent's privacy settings are appropriate — the skill itself does not exfiltrate data but your agent may log or route inputs elsewhere; 4) if you need stronger uncertainty quantification, review or replace the heuristic with a more rigorous/statistical method. Overall, nothing in the package requests disproportionate access or contradicts its described purpose.

Type: OpenClaw Skill Name: self-aware-prediction-system Version: 1.0.0 The skill bundle implements a 'Self-Aware Prediction System' designed to help an AI agent quantify and communicate its uncertainty. The Python script (scripts/uncertainty_assessment.py) contains safe logic for calculating confidence scores based on information completeness, and the markdown files (SKILL.MD, README.md) provide conceptual frameworks and instructions for the agent to follow when making predictions. No malicious behaviors, data exfiltration, or harmful prompt injections were detected.