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simulation-analyst

Analyze MiroFish/OASIS simulation results with market signals to generate product recommendations.

1|Updated Mar 3, 2026

Search Tags:#data synthesis #market signals #hypothesis validation #confidence calibration #simulation analysis #product recommendations

Ready to run

npx skills add https://github.com/Johnnnmai/100x-product-manager --skill simulation-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: simulation-analyst
Source: https://github.com/Johnnnmai/100x-product-manager/tree/main/skills/simulation-analyst
Command: npx skills add https://github.com/Johnnnmai/100x-product-manager --skill simulation-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill transforms raw simulation results and market signals into actionable product recommendations, complete with confidence levels, to guide decision-making.

Core Features & Use Cases

Synthesize Simulation Data: Analyze outputs from tools like MiroFish/OASIS.
Integrate Market Signals: Combine simulation findings with real-world user data.
Generate Data-Backed Recommendations: Provide clear, evidence-based suggestions.
Calibrate Confidence: Assign confidence levels to recommendations based on evidence strength.
Use Case: After running persona simulations for a new feature, use this Skill to analyze the results, cross-reference with user feedback from forums, and generate a report recommending whether to proceed, pivot, or halt development, along with a confidence score for each recommendation.

Quick Start

Analyze the attached simulation results and market signals to generate data-backed product recommendations with confidence levels.

Frequently Asked Questions about simulation-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I turn simulation results into actionable product recommendations?▼

To generate product recommendations from simulation results, you synthesize persona reaction data and scenario outcomes with market signals to produce evidence-based suggestions complete with calibrated confidence levels.

What data do I need to validate product hypotheses using simulation analysis?▼

Validating product hypotheses through simulation analysis requires structured signal summaries, persona reaction data, and scenario outcomes to comprehensively identify risks, opportunities, and inform strategy.

How does confidence calibration work for data-backed product recommendations?▼

Confidence calibration assigns specific confidence levels to product recommendations by evaluating the combined strength of simulation outcomes and integrated real-world market signals.

Can I analyze MiroFish or OASIS simulation outputs for feature development strategy?▼

Yes, you can analyze MiroFish or OASIS simulation outputs by cross-referencing persona reactions with market signals to recommend whether to proceed, pivot, or halt feature development.

When should I use simulation analysis instead of raw user feedback for product strategy?▼

Use simulation analysis when you need to validate hypotheses by combining structured persona reaction data with real-world market signals, moving beyond raw feedback to evidence-backed strategic recommendations.

What is the best way to integrate market signals with persona simulation data?▼

The best way to integrate market signals with persona simulation data is to synthesize structured signal summaries and scenario outcomes together, generating data-backed recommendations with calibrated confidence levels.