Self-Improving Proactive Decision Making Agent

Structured decision support with self-improving memory. Agent learns your decision style, risk profile, and framework preferences over time — and gets better...

What to check before installing/running: - Back up SOUL.md, AGENTS.md, and HEARTBEAT.md before applying any suggested edits. The skill asks you to append steering snippets to these files; review and approve exact text first. - Fix the path mismatch: most files use ~/decision-making/, but one memory.md example references ~/self-improving/. Decide which path you want and update the templates so files are created where you expect. - Understand persistence: the skill suggests adding lines that make it load memory automatically before decision tasks. If you prefer to invoke the skill only when needed, skip adding the auto-load snippets and call the skill manually instead. - Inspect created files after the first run (memory.md, domains/, decisions/, heartbeat-state.md). Confirm the skill is not logging anything you consider sensitive. The skill includes explicit boundaries (do not store credentials, medical data, etc.), but it will store whatever the agent is given — avoid giving secrets in decision contexts. - Test in a disposable environment (separate user account or container) if you want to verify behavior before granting it ongoing presence in your main agent configuration. Why I flagged it as suspicious rather than benign: the overall design matches the declared purpose, but the path inconsistency and the requested edits to global agent steering/config increase the chance of accidental misconfiguration or broader persistence than a casual user may expect. If you resolve the path inconsistency and explicitly approve any SOUL/AGENTS/HEARTBEAT changes, the concerns are largely mitigated.

Type: OpenClaw Skill Name: neosoul-decision-agent Version: 1.0.0 The 'neosoul-decision-agent' is a comprehensive decision-support skill that implements a structured, local memory system in `~/decision-making/`. It includes extensive security and privacy safeguards in `boundaries.md`, explicitly forbidding the storage of credentials, financial data, or third-party information, and mandates a 'Kill Switch' for data deletion. The logic for learning user preferences (found in `decision-signals.md` and `operations.md`) is conservative, requiring multiple consistent signals before updating the 'HOT' memory, and the skill explicitly restricts the agent from making final decisions or accessing external networks.