soft-predict-future

What this skill does

# Soft Predict Future — Foresight Engine

Activate when the user asks any future-oriented question — "Will [X]?", "Who will win [X]?", "What happens to [X]?", "Can [X] succeed?", or any question about a future outcome. **Year is NOT required.** Also activate on: foresight analysis, scenario analysis, STEEEP, futures cone, or any prediction request.

---

## Try Asking

```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TRY ASKING  (year optional — engine infers the horizon)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
■ Who will win — Google or Perplexity?
■ Will OpenAI or Anthropic dominate the AI race?
■ Will India become the global AI leader?
■ Will crypto replace banks?
■ Will remote work become permanent?
■ Will EVs dominate Indian cities by 2032?
■ Will UPI become Southeast Asia's default payment rail by 2028?
■ Will Europe lead the green energy transition by 2035?
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

> **Soft Predict Future** uses Claude's native reasoning + web search. Outputs are structurally correct and fast. For deterministic, auditable scoring say: **"Run hard predict future: [your question]"**

---

## The 9-Step Pipeline

Execute ALL steps in order. Never skip. Never combine. Show your work at each step.

---

### Step 1 — Validate Input

Apply exactly 5 binary rules. If ANY rule fails, stop and explain why. Do not proceed.

**Rule 1 — Entity Reality:** Does the entity actually exist in the real world? Fail if fictional, hypothetical, or unnamed.

**Rule 2 — System Existence:** Is the domain observable and researchable? Fail if purely philosophical or metaphysical.

**Rule 3 — Time Horizon:** Is the outcome observable within a 2–30 year window? A specific year is NOT required. If no year is given, infer the most reasonable horizon from the question's nature:
- Competitive race / market dominance questions → 3–10 years (Strategic)
- Technology adoption questions → 5–15 years (Strategic)
- Geopolitical / societal shift questions → 10–20 years (Civilizational)
- Company survival / near-term outcome → 2–5 years (Operational/Strategic)

Fail ONLY if the implied timescale is geological, post-human, or clearly beyond 30 years.

After applying Rule 3, state the inferred horizon (e.g. "2026–2033" or "2028–2038").

**Rule 4 — Signal Availability:** Could real-world evidence plausibly exist? Fail if classified, purely speculative, or unpublished.

**Rule 5 — Minimum Specificity:** Is the question specific enough to produce distinct scenario outcomes? Fail if trivially true for any answer.

Output:
```
VALIDATION
Rule 1 Entity Reality:      PASS / FAIL — [reason]
Rule 2 System Existence:    PASS / FAIL — [reason]
Rule 3 Time Horizon:        PASS / FAIL — [reason] | Inferred horizon: [YYYY–YYYY]
Rule 4 Signal Availability: PASS / FAIL — [reason]
Rule 5 Specificity:         PASS / FAIL — [reason]
Result: PROCEED / STOP
```

---

### Step 2 — Collect Signals

Run exactly 6 web searches. Collect a minimum of 18 signals total. Do not proceed with fewer than 18.

**Search 1:** Current state — `"[topic] current status [year]"`
**Search 2:** Growth indicators — `"[topic] growth data market size [year]"`
**Search 3:** Barriers and headwinds — `"[topic] challenges barriers risks"`
**Search 4:** Policy and regulation — `"[topic] government policy regulation"`
**Search 5:** Technology or infrastructure enablers — `"[topic] technology infrastructure investment"`
**Search 6:** Historical precedent — `"[topic] historical analogue similar transition"`

For each signal, classify all 6 attributes:

| Attribute | Values |
|---|---|
| direction | supporting / opposing / wildcard / neutral |
| steeep_category | Social / Technological / Economic / Environmental / Ethical / Political |
| temporal_layer | Operational (0–3yr) / Strategic (3–10yr) / Civilizational (10+yr) |
| source_type | primary / secondary / opinion |
| recency_days | integer |
| has_evidence | true / false (contains a number, date, or measurable fact) |

Present signals in a table with all 6 columns filled for every row.

---

### Step 3 — Score Signals

Score every signal individually using this formula:

```
score = recency_weight × reliability_weight × type_weight × evidence_multiplier
```

Cap at 1.0. Round to 2 decimal places.

**Recency weights:**
- 0–90 days: 1.00 · 91–365 days: 0.80 · 1–3 years: 0.60 · 3+ years: 0.40 · unknown: 0.50

**Reliability weights:**
- Primary (government/official): 1.00
- Established news (Reuters, Bloomberg, FT, ET): 0.90
- Industry report (McKinsey, Gartner, NASSCOM): 0.85
- Analyst commentary: 0.70 · Opinion/blog/social: 0.50 · Unknown: 0.40

**Type weights:**
- Supporting: 1.00 · Opposing: 1.00 · Neutral: 0.60 · Wildcard: 1.30

**Evidence multiplier:**
- DATA / STATISTIC: × 1.20 · EVENT / INCIDENT: × 1.00 · ANALYSIS / OPINION: × 0.70

Apply regional multiplier after base score:
```
final_score = min(1.0, base_score × regional_multiplier[steeep][temporal])
```

Show scoring table: Signal | recency_w | reliability_w | type_w | evidence_mult | base_score | regional_mult | final_score

---

### Step 4 — Extract Structural Drivers

A **driver** is the deep structural force that explains WHY a cluster of signals exists. Signals are observable. Drivers are causal.

After scoring, group signals by STEEEP category. For each cluster of 3+ signals in the same category, identify the underlying driver.

Extract exactly **3 top drivers**, ranked by the sum of final_scores of the signals they explain.

For each driver state:
- **Name:** 3–5 word label (e.g. "India DPI Infrastructure Advantage")
- **Force:** One sentence — the structural reality this driver represents
- **Signals it explains:** List the signal IDs it accounts for
- **Temporal reach:** Operational / Strategic / Civilizational
- **Stability:** LOCKED (unlikely to change in 10yr) / SHIFTING (could change in 3–5yr) / FRAGILE (could reverse in 1–2yr)

Output:
```
STRUCTURAL DRIVERS
D1 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]

D2 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]

D3 [Name] — [Force]
   Explains: [signal list] | Temporal: [layer] | Stability: [tier]
```

Drivers feed directly into scenario writing in Step 8. Each scenario must be traceable to at least one driver.

---

### Step 5 — Build 6×3 STEEEP Matrix

Populate all 18 cells. Each cell value = average final_score of all signals mapped to that STEEEP × Temporal combination. Empty cells = 0.

|  | Operational (0–3yr) | Strategic (3–10yr) | Civilizational (10+yr) |
|---|---|---|---|
| **Social** | | | |
| **Technological** | | | |
| **Economic** | | | |
| **Environmental** | | | |
| **Ethical** | | | |
| **Political** | | | |

Apply regional multipliers to each cell. Then identify:
- **Hot zones:** cells with score > 0.50
- **Gap zones:** cells with score = 0.00
- **Dominant zone:** single highest-scoring cell

---

### Step 6 — Cross-Impact Analysis

Signals are scored independently in Step 3, but structural forces interact. This step identifies amplification effects across STEEEP categories.

**Rule:** If 2 or more hot zones exist in the SAME temporal layer, a cross-impact convergence exists. Convergence means the probable outcome is structurally reinforced from multiple directions simultaneously.

For each temporal layer (Operational / Strategic / Civilizational):
1. Count hot zones (score > 0.50)
2. If count ≥ 2: flag as **CONVERGENCE** — state which categories are reinforcing each other and why
3. If count = 1: flag as **ISOLATED** — single-category signal, more fragile
4. If count = 0: flag as **BLIND LAYER** — no strong evidence in this time horizon

Also identify any **opposing cross-impacts**: where a hot zone in one STEEEP category directly contradicts or slows a hot zone in another (e.g. Technological/Strategic hot but Political/Strategic opposing). Flag these as **FRICTION POINTS**.

Output:
```
CROSS-IMPACT
Operational:    [CONVERGENCE / ISOLATED / BLIND LAYER] — [explanation]
Strategic: