Does it require a specific year to be mentioned in the prompt?

No, the engine automatically infers the most reasonable time horizon (typically 2 to 30 years) based on the nature of the entity or question provided.

What is the difference between 'Soft' and 'Hard' Predict Future?

Soft Predict Future uses native reasoning and web search for fast, structurally correct outputs, while Hard Predict Future is used for more deterministic, auditable scoring.

How does the skill validate prediction requests?

It applies five binary rules to ensure the entities exist, the domain is researchable, the time horizon is realistic, signals are available, and the question is specific enough for analysis.

What is the Soft Predict Future skill?

It is a specialized capability for Claude Code that uses a rigorous 9-step analytical pipeline to forecast future outcomes and build detailed scenarios based on real-world signals.

Why does this skill require web search to be enabled?

The foresight engine requires a minimum of 18 distinct signals across 6 different search types to ensure the prediction is based on current data rather than outdated knowledge.

Foresight Intelligence: Soft Predict Future

Name: Foresight Intelligence: Soft Predict Future
Author: davepoon

bydavepoon

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2,841

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Ciencia de Datos y ML

Automates professional-grade strategic foresight and scenario planning using a rigorous 9-step analytical pipeline.

This skill transforms Claude into a strategic foresight engine capable of predicting future outcomes through empirical signal collection and cross-impact analysis. By executing a structured 9-step methodology—including STEEEP analysis, signal scoring, and historical benchmarking—it moves beyond simple speculation to provide quantified probabilities for probable, plausible, and possible futures. It is ideal for market analysts, strategic planners, and decision-makers looking to understand competitive races, technology adoption, and geopolitical shifts over a 2–30 year horizon using real-world web data.

Características Principales

01Dynamic time horizon inference for strategic, operational, or civilizational analysis

02Quantitative probability scoring based on futures cone methodology and historical analogues

03Web-augmented signal collection requiring at least 18 distinct evidence points

042,841 GitHub stars

059-step automated foresight pipeline with structural driver extraction

06Comprehensive STEEEP matrix evaluation (Social, Tech, Economic, Environmental, Ethical, Political)

Casos de Uso

01Evaluating the adoption rates and infrastructure requirements of emerging technologies

02Assessing long-term geopolitical and societal shifts through scenario planning

03Predicting winners in competitive market races (e.g., AI dominance or energy transitions)

Características Principales

01Dynamic time horizon inference for strategic, operational, or civilizational analysis

02Quantitative probability scoring based on futures cone methodology and historical analogues

03Web-augmented signal collection requiring at least 18 distinct evidence points

042,841 GitHub stars

059-step automated foresight pipeline with structural driver extraction

06Comprehensive STEEEP matrix evaluation (Social, Tech, Economic, Environmental, Ethical, Political)

Casos de Uso

01Evaluating the adoption rates and infrastructure requirements of emerging technologies

02Assessing long-term geopolitical and societal shifts through scenario planning

03Predicting winners in competitive market races (e.g., AI dominance or energy transitions)