foresight-engine
Strategic intelligence plugin for Claude Code. Give it any real-world forecasting question and it produces a structured four-scenario analys
Open source Open in the app JSON README (API)
About
Strategic intelligence plugin for Claude Code. Give it any real-world forecasting question and it produces a structured four-scenario analysis (Probable, Plausible, Possible, Preferable) backed by live web signals, historical analogues, and deterministic probability scoring. Built on IFTF foresight methodology: STEEEP framework, Futures Cone, Cross-Impact Analysis, and Backcasting. Two-layer architecture: Python handles all deterministic arithmetic; Claude handles web search, reasoning, and scenario writing.
Details
- Kind
- Plugins
- Topic
- AI, RAG & memory
- Publisher
- isanthoshgandhi
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 1
- Last push
- 2026-04-05T12:52:15Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
isanthoshgandhi/foresight-engine/foresight-engine
README
# 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 ```bash # 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: ``` ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [SOFT / HARD] PREDICT FUTURE · FORESIGHT INTELLIGENCE [Query] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ■ 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` --- ## How It Works **9-step pipeline (Soft Predict — Claude runs all steps natively):** 1. **Validate** — 5-rule check: entity real, system observable, time horizon set, signals available, question specific 2. **Collect signals** — 6 web searches, minimum 18 signals, all classified by STEEEP + temporal + type 3. **Score signals** — 4-factor formula: recency × reliability × type × evidence, regional multipliers applied 4. **Extract drivers** — top 3 structural forces behind the signal clusters, ranked by score sum 5. **Build STEEEP matrix** — 18-cell grid: 6 categories × 3 time horizons 6. **Cross-impact analysis** — convergence and friction points across temporal layers 7. **Find analogues** — 3 real historical cases, similarity scored, mapped to drivers 8. **Compute predictions + confidence** — 3 independent scores (0–100 each, do NOT sum to 100); confidence penalizes blind spots 9. **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](https://www.iftf.org) 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