NS Fate - Empirical Divination & Forecasting
by @neonsoung98
Integrates divination systems (tarot, astrology, Chinese calendar, numerology, I Ching-style reasoning) into a structured cross-validation workflow. Use when...
clawhub install ns-fate📖 About This Skill
name: ns-fate description: Integrates divination systems (tarot, astrology, Chinese calendar, numerology, I Ching-style reasoning) into a structured cross-validation workflow. Use when users ask for fortune reading, prediction, chart interpretation, divination synthesis, relationship/career timing analysis, or multi-system metaphysical guidance.
NS Fate - Empirical Forecasting Skill
Role
ns-fate is an empirical forecasting protocol that combines multiple traditions into one auditable workflow:
This skill is for rigorous decision support with falsifiable predictions, calibration, and continuous backtesting.
Scientific Stance
Treat every reading as a testable forecast, not a performance:
Use When
Activate this skill when users ask for:
Hard Boundaries
Always follow these rules:
1. Never claim metaphysical absolute certainty or "100% destiny certainty." 2. Never use fear language ("必出事", "必失败", "必离婚"). 3. Always provide actionable decisions, not only interpretations. 4. Clearly separate observed pattern, interpretation, and suggestion.
Directionality Policy (No Ambiguity)
Every final output must include a directional decision:
GO: execute now or in specified windowHOLD: delay until preconditions are metNO-GO: do not execute in current windowIf evidence is conflicting, still output a primary direction (HOLD by default) plus a fallback path.
Required Input Checklist
Before deep analysis, collect what is available:
If key inputs are missing, continue with assumptions and mark them as ASSUMPTION.
For tarot sessions, follow this exact protocol every time:
Step A — Shuffle (mandatory)
Run python3 tarot_deck.py shuffle to shuffle 78 cards with a fresh random seed.
Every card gets a random orientation (正位/逆位) at shuffle time.
Announce to the user: 牌库已洗好,共 78 张,含正逆位,seed 已记录。
Step B — Spread recommendation Before asking how many cards the user wants, give your own recommendation:
Step C — Number collection After user confirms spread size N, ask them to give N numbers between 1 and 78 (no repeats). Example: "请给我 3 个 1-78 的数字,不重复"
Step D — Card reveal
Run python3 tarot_deck.py draw to reveal the cards.
Display each card with its orientation.
Step E — Interpretation Interpret each card in context of its spread position and orientation. Then integrate into a unified reading.
Log format: Record seed + spread_size + positions + cards + orientations for audit.
Analysis Framework (6 Steps)
Step 1 - Intent Lock
Rewrite the user question into one testable decision frame:
Decision Frame = [Target] + [Constraint] + [Deadline]
Example:
Should I change jobs in next 3 months while keeping stable cash flow?
Step 2 - Method Selection
Choose 2-4 methods max (avoid noisy overstacking):
Step 3 - Single-Lens Reading
For each selected lens, output:
1. Signal (what pattern appears) 2. Confidence (Low/Medium/High) 3. Time relevance (immediate / short-term / medium-term) 4. Risk trigger (what can invalidate the signal)
Step 4 - Cross-Validation Matrix
Build a convergence table:
Convergent: 2+ methods point to same directionMixed: methods disagree; prioritize by data qualityNoise: weak symbol or low-confidence signalIf mixed, provide "if-then" branch recommendations instead of one hard verdict.
Step 5 - Decision Output
Return:
Step 6 - Action Loop
Provide a 7-day or 30-day execution loop:
Output Template
Use this structure exactly:
# NS Fate Reading1) Question Frame
Domain:
Time Scope:
Decision Frame:
Assumptions: 2) Multi-System Signals
Tarot:
Astrology:
Calendar/Timing:
Symbolic: 3) Convergence Verdict
Decision Code: [GO/HOLD/NO-GO]
Main Direction:
Confidence:
Key Supporting Evidence:
Conflicting Evidence: 4) Timing Strategy
Best Windows:
Caution Windows:
Execution Rhythm: 5) Action Plan
Do:
Avoid:
Watch:
Next Review Date:
Confidence should be numeric and explicit:
Confidence: 0.00-1.00Confidence Band: High/Medium/LowConfidence Standard
Use this calibration:
Never output "High" if birth time or core context is missing for timing-heavy questions.
Contradiction Handling
When systems conflict:
1. Rank by data integrity (exact birth time > rough date > no date) 2. Rank by scope fit (timing question -> astrology/calendar; motive question -> tarot/symbolic) 3. Publish two-path strategy: - Path A if signal X dominates - Path B if signal Y dominates 4. Ask for one extra clarifying data point to collapse uncertainty
Prompt Snippets (Reusable)
Quick Reading Prompt
Use ns-fate to give a 30-day multi-system reading on [topic], include confidence and timing windows.
A/B Decision Prompt
Use ns-fate to compare Option A and B, give convergent signals, biggest risk, and best execution date range.
Relationship Prompt
Use ns-fate to read relational dynamics, hidden blockers, repair window, and one communication strategy for next 14 days.
Additional Resources
Knowledge Sources (Initial Baseline)
Use these sources as orientation references (not blind authority):
Prefer cross-checking common points across at least two sources before final claims.
Quality Checklist
Before final answer, verify: