zeelin-us-iran-forecast_skill

This skill collects, verifies, and forecasts war developments using authoritative sources and scenario-based predictions to provide disciplined,
  • Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

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Installation

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npx veilstrat add skill openclaw/skills --skill zeelin-us-iran-forecast

  • _meta.json305 B
  • SKILL.md13.7 KB

Overview

This skill collects, verifies, and forecasts war and geopolitical conflict developments using authoritative open sources, cross-validation, and scenario-based prediction. It enforces strict separation of confirmed facts, assessments, and forecasts, and emphasizes explicit uncertainty, time-boxed forecasts, and trigger/disconfirming signals. The goal is disciplined, evidence-led intelligence synthesis for open-source analysis, not classified reporting or propaganda.

How this skill works

The skill builds a fact-first timeline from Tier 1 sources (Reuters, AP, official primary releases) and supplements with hard-data indicators (satellite reporting, shipping traffic, market moves). It tags each claim with a verification label (Confirmed, Single-party claim, High-confidence inference, Unverified) and separates battlefield, capability, political, regional spillover, and forward-indicator buckets. Forecasts are produced for three horizons (24 hours, 3–7 days, 2–6 weeks) with supporting evidence, constraints, confirming signals, and disconfirming signals.

When to use it

  • Rapid synthesis after a new incident or escalation to establish verified facts
  • Regular situation updates during an ongoing conflict (daily or multi-day cadence)
  • Decision support when policymakers need probability-weighted near-term forecasts
  • Monitoring spillover risks to markets, shipping, or neighboring states
  • Preparation of scenario-based briefings for humanitarian or operational planners

Best practices

  • Always separate facts, assessment, and forecast in outputs
  • Build the backbone with Reuters/AP/official statements before using social media leads
  • Use concrete dates and explicit time windows for all events and forecasts
  • Label major claims with the mandatory verification categories
  • For each forecast list supporting evidence, constraints, confirming and disconfirming signals
  • Keep language probabilistic (high/medium/low probability) and avoid theatrical certainty

Example use cases

  • Produce a dated 10-event timeline after a cross-border strike with source confidence labels
  • Forecast likely retaliatory patterns over next 24 hours and list immediate watchlist indicators
  • Assess whether a campaign is shifting to underground/strategic targeting and what constrains it
  • Estimate regional spillover risk to shipping and oil markets over the next 2–6 weeks
  • Create scenario-tree end-state probabilities with triggers that would move each branch

FAQ

It prioritizes Reuters, AP, and official primary releases first, then hard-data indicators, major international outlets, and uses social media only as early leads requiring verification.

How are forecasts expressed?

Forecasts are time-boxed into next 24 hours, next 3–7 days, and next 2–6 weeks and expressed in probability bands with explicit confirming and disconfirming signals.

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