NS Fate - Empirical Divination & Forecasting

Integrates divination systems (tarot, astrology, Chinese calendar, numerology, I Ching-style reasoning) into a structured cross-validation workflow. Use when...

This skill is internally consistent with its divination/forecasting purpose and contains only a small local Python tool for tarot shuffling/drawing. Before installing, consider: (1) Privacy — the skill asks for sensitive personal inputs (birth date/time/place) and requires recording seeds/registry entries; verify where these logs will be stored and who can read them. (2) Persistence — the docs expect the agent to save predictions and review results but do not specify storage location; decide whether you want logs kept locally, in agent-managed storage, or not retained. (3) External data — the docs reference external astrology/tarot websites for cross-checks; the skill itself does not include network calls, but an agent following the docs may browse those sites. (4) Code review — tarot_deck.py is small, runs locally, and has no network or credential use; you can inspect it yourself (it uses time-based seeds and Python's PRNG). (5) Autonomy — the skill may be invoked by the agent (default behavior); if you prefer manual control, restrict autonomous invocation in your agent settings. If you want a lower risk posture, confirm storage/retention policies and require explicit user confirmation before saving or transmitting any personally identifiable inputs.

Type: OpenClaw Skill Name: ns-fate Version: 1.2.0 The 'ns-fate' skill bundle is a highly structured framework for integrating various divination systems (tarot, astrology, etc.) into a forecasting workflow. The included Python script (tarot_deck.py) is a standard utility for shuffling and drawing cards using local randomization and contains no network or file-system exploits. The extensive documentation (SKILL.md, reference.md, adjudication-engine.md, etc.) focuses on improving prediction accuracy through scientific metrics like Brier scores and log loss, and includes 'red-team' protocols to prevent AI hallucinations or vague responses, showing no evidence of malicious intent or data exfiltration.