Foresight Intelligence

Strategic foresight engine using IFTF methodology. Two modes: Soft Predict Future (Claude-native skill, instant, works on claude.ai) and Hard Predict Future (deterministic 12-step pipeline, Python-computed, auditable — requires Claude Code). Structural drivers, cross-impact analysis, IFTF backcasting, four independent futures with per-stakeholder conditional analysis. Year is optional — ask any future question and the engine infers the right time horizon.

Author: Santhosh Gandhi · Version: 2.2.0

Try Asking

Year is optional. The engine infers the right horizon from your question.

■ 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?

Two Modes

	Soft Predict Future	Hard Predict Future
How	Claude-native skill — just ask a question	Say "run hard predict: [question]"
Platform	Claude Code, claude.ai, Claude for Work	Claude Code only (needs Python + Bash)
Scoring	Claude estimates using the formula	Python computes deterministically
Reproducibility	±2–5% variance per run	Identical every run
Audit trail	Claude reasoning (implicit)	JSON files for every step
Best for	Exploration, quick reads, content	VC memos, high-stakes decisions

Install on Claude Code

# Step 1 — Add the marketplace (one-time setup)
claude plugin marketplace add isanthoshgandhi/foresight-intelligence

# Step 2 — Install the plugin
claude plugin install foresight-intelligence

Then just ask any future question — Soft Predict activates automatically.

For Hard Predict Future say:

Run hard predict: Will India become the global AI leader by 2050?

To invoke explicitly by name:

/foresight-intelligence:hard-predict-future Will OpenAI or Anthropic win by 2030?

What You Get

Every run — both modes — always outputs the same complete report:

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[SOFT / HARD] PREDICT FUTURE · FORESIGHT INTELLIGENCE
[Query]
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PREDICTIONS
■ Probable  [X/100] [████████████░░░░░░░░] — most likely trajectory
■ Plausible [X/100] [████████░░░░░░░░░░░░] — credible alternative
■ Possible  [X/100] [████░░░░░░░░░░░░░░░░] — low-probability but real
■ Preferable         [stakeholder analysis below]

Confidence: [X]/100 | Signals: [N] | Horizon: [YYYY–YYYY] | [Date]
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SIGNAL PULSE         — supporting / opposing / wildcard counts + visual bars
STEEEP MATRIX        — 6×3 grid with ★ hot  ● warm  ✗ blind indicators
STRUCTURAL DRIVERS   — D1, D2, D3 with stability rating
CROSS-IMPACT         — convergence and friction across time horizons
HISTORICAL MATCH     — best analogue + similarity score
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■ PROBABLE  scenario  — narrative + PROOF + IF + BUT + DRIVER
■ PLAUSIBLE scenario  — narrative + PROOF + IF + BUT + DRIVER
■ POSSIBLE  scenario  — narrative + PROOF + IF + BUT + DRIVER
■ PREFERABLE          — IFTF backcasting from desired future to today
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PREFERABLE FUTURES · Per stakeholder
  [Player A]: Wins IF → [condition]  BUT ONLY → [constraint]  ONLY THEN → [outcome]
  [Player B]: Wins IF → [condition]  BUT ONLY → [constraint]  ONLY THEN → [outcome]
  Users:      Wins IF → [condition]  BUT ONLY → [constraint]  ONLY THEN → [outcome]
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THE ONE THING        — the single variable that determines which scenario activates
DECISION GUIDANCE    — recommended stance, low-regret move, risk trigger
REGIONAL LENS        — India / USA / Europe / China multipliers
METHODOLOGY KEY      — one-line explanation of every score and formula
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How It Works

9-step pipeline (Soft Predict — Claude runs all steps natively):

Validate — 5-rule check: entity real, system observable, time horizon set, signals available, question specific
Collect signals — 6 web searches, minimum 18 signals, all classified by STEEEP + temporal + type
Score signals — 4-factor formula: recency × reliability × type × evidence, regional multipliers applied
Extract drivers — top 3 structural forces behind the signal clusters, ranked by score sum
Build STEEEP matrix — 18-cell grid: 6 categories × 3 time horizons
Cross-impact analysis — convergence and friction points across temporal layers
Find analogues — 3 real historical cases, similarity scored, mapped to drivers
Compute predictions + confidence — 3 independent scores (0–100 each, do NOT sum to 100); confidence penalizes blind spots
Write scenarios + report — PROBABLE / PLAUSIBLE / POSSIBLE + PREFERABLE with IFTF backcasting

Hard Predict extends to 12 steps with Python handling steps 1, 3, 5, 8, 9, 10, 12 deterministically (confidence, decision guidance, and report formatting each get their own dedicated step).

IFTF Methodology

This plugin implements the Institute for the Future futures research framework:

IFTF Concept	Implementation
Futures Cone	PROBABLE / PLAUSIBLE / POSSIBLE / PREFERABLE
Three Horizons	Operational (0–3yr) / Strategic (3–10yr) / Civilizational (10+yr)
STEEEP Scan	6-category signal collection and matrix
Signals → Drivers	Step 4: extract structural forces from signal clusters
Backcasting	PREFERABLE scenario works backwards from desired future
Action Implications	DECISION GUIDANCE: stance, low-regret move, risk trigger

License

MIT