Foresight intelligence

Foresight Intelligence

Strategic foresight using IFTF methodology. Two modes — pick based on what you need.

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MODE SELECTOR

Soft Predict — Claude-native. Instant. Works on claude.ai and Claude Code. Use for: exploration, quick reads, content, early-stage thinking. Say: /foresight-intelligence [your question] or just ask a future-oriented question.

Hard Predict — Deterministic 12-step pipeline. Python computes all arithmetic. Identical output every run. Full JSON audit trail. Requires Claude Code + Python 3.x. Say: hard predict: [your question] or run hard foresight: [your question]

If the user does not specify, default to Soft Predict.

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SOFT PREDICT — 9-Step Pipeline

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

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Step 1 — Validate Input

Apply exactly 5 binary rules. If ANY rule fails, stop and explain why.

Rule 1 — Entity Reality: Does the entity exist in the real world? Fail if fictional or hypothetical. Rule 2 — System Existence: Is the domain observable and researchable? Fail if purely philosophical. Rule 3 — Time Horizon: Observable within 2–30 years? Year NOT required — infer the horizon:

• Competitive race / market dominance → 3–10 years (Strategic)
• Technology adoption → 5–15 years (Strategic)
• Geopolitical / societal shift → 10–20 years (Civilizational)
• Near-term company outcome → 2–5 years (Operational/Strategic)

Rule 4 — Signal Availability: Could real-world evidence plausibly exist? Rule 5 — Minimum Specificity: Specific enough to produce distinct scenario outcomes?

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

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Step 2 — Collect Signals

Run exactly 6 web searches. Collect minimum 18 signals total.

1. "[topic] current status [year]"
2. "[topic] growth data market size [year]"
3. "[topic] challenges barriers risks"
4. "[topic] government policy regulation"
5. "[topic] technology infrastructure investment"
6. "[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

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Step 3 — Score Signals

Score every signal: score = recency_weight × reliability_weight × type_weight × evidence_multiplier. Cap at 1.0.

Recency: 0–90d: 1.00 · 91–365d: 0.80 · 1–3yr: 0.60 · 3+yr: 0.40 · unknown: 0.50 Reliability: Primary: 1.00 · Major news: 0.90 · Industry report: 0.85 · Analyst: 0.70 · Opinion: 0.50 Type: Supporting/Opposing: 1.00 · Neutral: 0.60 · Wildcard: 1.30 Evidence: DATA/STATISTIC: ×1.20 · EVENT: ×1.00 · ANALYSIS/OPINION: ×0.70

Apply regional multiplier (tables at bottom). Show scoring table with all columns.

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Step 4 — Extract Structural Drivers

Group signals by STEEEP. For each cluster of 3+ signals, identify the underlying driver — the deep force explaining WHY those signals exist.

Extract exactly 3 top drivers, ranked by sum of final_scores.

For each driver:

• Name: 3–5 word label
• Force: One sentence — the structural reality
• Signals: Which signal IDs it explains
• Temporal reach: Operational / Strategic / Civilizational
• Stability: LOCKED / SHIFTING / FRAGILE

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Step 5 — Build 6×3 STEEEP Matrix

Each cell = average final_score of signals in that STEEEP × Temporal combination.

	Operational	Strategic	Civilizational
Social
Technological
Economic
Environmental
Ethical
Political

Identify: hot zones (>0.50), gap zones (0.00), dominant zone.

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Step 6 — Cross-Impact Analysis

For each temporal layer:

• ≥2 hot zones → CONVERGENCE (state which categories reinforce each other)
• 1 hot zone → ISOLATED
• 0 hot zones → BLIND LAYER

Identify FRICTION POINTS: hot zones in opposing STEEEP categories. Convergence bonus: if Strategic = CONVERGENCE → +5% to probable score.

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Step 7 — Find 3 Historical Analogues

3 real past cases structurally similar to the question. For each:

• Similarity (%), tipping event, equivalent today (YES/NO/PARTIAL), validates D1/D2/D3.

Prefer similarity ≥ 60%. Below 40% = confidence penalty.

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Step 8 — Compute Probabilities + Confidence

Scores are independent (do NOT sum to 100):

R_probable  = (supporting signals score > 0.70) × 3
            + (best analogue similarity / 100) × 4
            + (hot zone count) × 2 + convergence_bonus

R_plausible = (supporting signals score 0.40–0.70) × 2
            + (second analogue similarity / 100) × 3

R_possible  = (wildcard signals) × 2
            + (opposing signals score > 0.60) × 2
            + (gap zones / 18) × 3

probable_score  = min(100, round((1 − e^(−R_probable  / 18)) × 100))
plausible_score = min(100, round((1 − e^(−R_plausible / 9))  × 100))
possible_score  = min(100, round((1 − e^(−R_possible  / 5))  × 100))

confidence = signal_count (×0.30) + signal_diversity (×0.30) + recency (×0.20) + evidence (×0.20)

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Step 9 — Write Scenarios + Assemble Report

PROBABLE, PLAUSIBLE, POSSIBLE — each must cite a driver, no hedging, include PROOF with number/date, one-sentence IF and BUT.

PREFERABLE — IFTF Backcasting: describe desired state as already achieved, then backcast through Civilizational → Strategic → Operational. End with LEVERAGE (single highest-leverage action today) and DRIVER.

Decision guidance:

• probable > 60: "Align with probable trajectory"
• plausible > 50: "Hedge between probable and plausible"
• possible > 40: "Maintain optionality"
• else: "Defer — insufficient signal clarity"

MANDATORY output — all sections, every run:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
FORESIGHT INTELLIGENCE  ·  SOFT PREDICT
[Query]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

PREDICTIONS
■ Probable  [[X]/100] [████████████░░░░░░░░] — [one sentence, no hedging]
■ Plausible [[X]/100] [████████░░░░░░░░░░░░] — [one sentence, no hedging]
■ Possible  [[X]/100] [████░░░░░░░░░░░░░░░░] — [one sentence, no hedging]
■ Preferable          [stakeholder analysis below]

Confidence: [X]/100  |  Signals: [N]  |  Horizon: [YYYY–YYYY]  |  [YYYY-MM-DD]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

SIGNAL PULSE
Supporting [N] [████░░░░░░░░]  Opposing [N] [██░░░░░░░░░░]  Wild [N]
Net: [SUPPORTING LEADS / OPPOSING LEADS / NEUTRAL]
Hot zone: [dominant STEEEP×Temporal cell]
Gap: [uncovered categories or "None — full coverage"]

STRUCTURAL DRIVERS
D1 [Name] — [Force] ([LOCKED / SHIFTING / FRAGILE])
D2 [Name] — [Force] ([LOCKED / SHIFTING / FRAGILE])
D3 [Name] — [Force] ([LOCKED / SHIFTING / FRAGILE])

CROSS-IMPACT
Operational:    [status] — [explanation]
Strategic:      [status] — [explanation]
Civilizational: [status] — [explanation]
Friction: [pairs or "None detected"]

HISTORICAL MATCH
[Best analogue] ([similarity]% similar)
Tipped by: [event]  |  Equivalent now: [EXISTS/PARTIAL/ABSENT]  |  Validates: D[n]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

■ PROBABLE [[X]%] — [Title]
[2–3 sentences. No hedging.]
PROOF: [number or date]  |  IF: [condition]  |  BUT: [constraint]  |  DRIVER: D[n]

■ PLAUSIBLE [[X]%] — [Title]
[2–3 sentences]
PROOF: [number or date]  |  IF: [condition]  |  BUT: [constraint]  |  DRIVER: D[n]

■ POSSIBLE [[X]%] — [Title]
[2–3 sentences]
PROOF: [number or date]  |  IF: [condition]  |  BUT: [constraint]  |  DRIVER: D[n]

■ PREFERABLE — [Title]
[2–3 sentences: desired state as already achieved.]
BACKCAST
  Civilizational: [far horizon structural truth]
  Strategic:      [medium-term build]
  Operational:    [what begins NOW]
LEVERAGE: [specific actor, specific action]  |  DRIVER: D[n]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

PREFERABLE FUTURES  ·  Per stakeholder
[Player A]:
  Wins IF  → [condition]
  BUT ONLY → [constraint]
  ONLY THEN → [outcome]

[Users/Society]:
  Wins IF  → [condition]
  BUT ONLY → [constraint]
  ONLY THEN → [outcome]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

THE ONE THING
[Single variable that determines which scenario activates]
INCIDENT: [real past event]  |  WATCH: [leading indicator]
IF YES → [what accelerates]  |  IF NO → [what stalls]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

DECISION GUIDANCE
Stance: [from deterministic logic]
Low-regret move: [action that pays off across scenarios]
Risk trigger: [highest-scored opposing signal]

[REGIONAL LENS — [REGION]]
Top multipliers: [STEEEP/temporal]  Key local variable: [one sentence]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

If running on claude.ai with Artifacts enabled, also generate an HTML visual report after the text output (prediction bars, STEEEP matrix heatmap, futures cone SVG). Dark background #0f0f0f, accent #00d4aa. Inline CSS only.

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HARD PREDICT — 12-Step Deterministic Pipeline

CRITICAL: Claude handles intelligence. Python handles all arithmetic. Never skip a step. Never guess Python output. Always wait for exact stdout.

Scripts are at: ${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/

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Step 1 — Validate (Python)

Use the Write tool to write the user query to a temporary file — do NOT interpolate it into a shell command:

• File: ${CLAUDE_PLUGIN_ROOT}/query.txt
• Content: the raw query text exactly as typed

Then run:

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/input_validator.py" "${CLAUDE_PLUGIN_ROOT}/query.txt"

Note: input_validator.py must read the query from the file path using Python open(). If valid=false: output rejection message and STOP. If valid=true: infer horizon and proceed.

Step 2 — Collect Signals (Claude)

Same 6 searches as Soft Predict Step 2. Stop when signals ≥ 18 AND 4+ STEEEP categories covered. Save to: ${CLAUDE_PLUGIN_ROOT}/signals.json

Step 3 — Score Signals (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/signal_scorer.py" "${CLAUDE_PLUGIN_ROOT}/signals.json"

Wait for exact stdout JSON. Use returned data exactly. Script writes scored_signals.json.

Step 4 — Extract Structural Drivers (Claude)

Same as Soft Predict Step 4. Extract 3 drivers from scored_signals.json.

Step 5 — Build STEEEP Matrix (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/matrix_builder.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json"

Wait for exact stdout JSON. Script writes matrix.json.

Step 6 — Cross-Impact Analysis (Claude)

Same logic as Soft Predict Step 6. Read matrix.json. Apply convergence bonus.

Step 7 — Find Historical Analogues (Claude)

Same as Soft Predict Step 7. Save to: ${CLAUDE_PLUGIN_ROOT}/analogues.json

Step 8 — Compute Probabilities (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/probability_calc.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json" "${CLAUDE_PLUGIN_ROOT}/analogues.json"

Wait for exact stdout JSON. Apply convergence bonus: adjusted_probable = min(100, probable + convergence_bonus). Script writes probabilities.json.

Step 9 — Compute Confidence (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/confidence_calc.py" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json" "${CLAUDE_PLUGIN_ROOT}/matrix.json" "${CLAUDE_PLUGIN_ROOT}/analogues.json"

Wait for exact integer output.

Step 10 — Decision Guidance (Python)

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/decision_guidance.py" "${CLAUDE_PLUGIN_ROOT}/probabilities.json" "${CLAUDE_PLUGIN_ROOT}/matrix.json" "${CLAUDE_PLUGIN_ROOT}/scored_signals.json"

Wait for guidance.json.

Step 11 — Write Scenarios (Claude)

Same structure as Soft Predict Step 9 scenarios: PROBABLE, PLAUSIBLE, POSSIBLE, PREFERABLE + THE ONE THING.

Step 12 — Assemble + Format Report (Python)

Combine all outputs into report_data.json then:

python "${CLAUDE_PLUGIN_ROOT}/skills/foresight-intelligence/scripts/report_formatter.py" "${CLAUDE_PLUGIN_ROOT}/report_data.json"

Output template is identical to Soft Predict but header reads HARD PREDICT and includes STEEEP matrix with cell scores.

Error handling: Any Python script fails → report exact stderr, do not proceed. Never fabricate data.

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Regional Multiplier Tables

Apply in scoring and matrix steps.

India

	Operational	Strategic	Civilizational
Social	1.10	1.30	1.20
Technological	1.40	1.30	1.10
Economic	1.20	1.25	1.15
Environmental	0.90	1.00	1.10
Ethical	0.95	1.00	1.05
Political	0.85	0.90	1.00

USA

	Operational	Strategic	Civilizational
Social	1.00	1.10	1.05
Technological	1.20	1.40	1.20
Economic	1.10	1.30	1.10
Environmental	0.95	1.00	1.05
Ethical	1.05	1.10	1.10
Political	0.90	0.95	1.00

Europe

	Operational	Strategic	Civilizational
Social	1.00	1.05	1.10
Technological	1.00	1.10	1.05
Economic	0.95	0.90	0.90
Environmental	1.20	1.40	1.30
Ethical	1.10	1.20	1.20
Political	1.05	1.10	1.10

China

	Operational	Strategic	Civilizational
Social	1.00	1.10	1.05
Technological	1.20	1.50	1.30
Economic	1.10	1.20	1.10
Environmental	0.90	1.00	1.05
Ethical	0.70	0.75	0.80
Political	1.10	1.15	1.00

Global (default)

All multipliers = 1.0. Apply when no region is detectable.