YSLAB-ai

YSLAB-ai / scenario-lab

Public

Experimental local-first scenario analysis for natural-language forecasting with Codex or Claude

github.comYSLAB-aiscenario-lab agent-tools claude codex decision-support forecasting
18
1
100% credibility
Found Apr 26, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Scenario Lab is an experimental local simulation tool for exploring branching futures of real-world events like geopolitical crises, market shocks, or corporate decisions using AI assistance.

How It Works

1
💡 Discover Scenario Lab

You hear about a fun tool that simulates possible futures for real events like conflicts or market changes.

2
📥 Set it up on your computer

Download and prepare it quickly on your own machine so it runs privately.

3
🎯 Pick your scenario

Describe a situation, like how a tension between countries might unfold over weeks.

4
📚 Add supporting facts

Drop in news articles or notes about key players and recent events to make it realistic.

5
🚀 Launch the simulation

Hit go and watch it explore hundreds of branching paths based on smart rules and your facts.

6
📊 Review the outcomes

See the top-ranked scenarios, like stalemate or talks, with confidence levels explained simply.

✅ Gain clear insights

Understand likely paths and surprises for your event, ready to share or update as things change.

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AI-Generated Review

What is scenario-lab?

Scenario-lab is a Python-based Monte Carlo simulation engine for forecasting real-world events like regional conflicts, market shocks, or company decisions. You feed it a natural-language prompt via CLI—like "scenario how would a U.S.-Iran conflict at the Strait of Hormuz develop over 30 days"—and it builds structured branching futures using domain-specific rules, approved evidence from a local SQLite corpus, and AI adapters for Codex or Claude. It outputs ranked scenarios, confidence labels, and reports, all running locally without cloud dependencies.

Why is it gaining traction?

Unlike generic LLMs or cloud forecasting tools, it enforces domain packs (e.g., interstate-crisis or market-shock) to guide plausible branches, penalizing unrealistic paths based on actor profiles and evidence. Developers dig the interactive workflow—intake, evidence ingestion, approval, simulation, report—plus slash commands for Claude and easy evidence from Markdown/PDFs. As an experimental GitHub project, it stands out for replay calibration and evolution tools that let you tune domains against historical cases.

Who should use this?

Geopolitical analysts modeling crises, quant traders stress-testing markets, or AI researchers prototyping local-first scenario labs. It's for teams doing labor and delivery scenario interview questions, lab safety scenarios, or supply chain disruptions without vendor lock-in—especially if you're already using Claude/Codex for natural-language analysis.

Verdict

Grab it for experimental design on GitHub if local forecasting hooks you, but with 16 stars and 1.0% credibility, it's a raw v0.1.0 preview—solid multilingual docs and CLI, but expect rough edges and contribute to mature it. Worth a demo-run for niche scenario lab.a i workflows.

(198 words)

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