set-method

SET methodology toolkit — classify, score, and recommend across Science, Entrepreneurship, and Technology spheres.

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Why this exists

Every project lives at the intersection of Science, Entrepreneurship, and Technology — but most people can't articulate which sphere dominates their work. This matters because:

• Mentors need to direct people to the right starting point
• Founders need to understand their venture's DNA before building
• Programs need to score and compare ideas objectively

set-method gives you a programmatic way to classify, score, and recommend within the SET framework.

Install

pip install set-method

Requires Python 3.9+. No external dependencies.

Quick Start

from set_method import classify, score, recommend

# Classify a project or idea into SET spheres
classify("We're building an AI diagnostic tool for crop disease")
# → {"science": 0.7, "entrepreneurship": 0.8, "technology": 0.9, "primary": "T"}

# Score a venture idea against the SET framework
score("startup_idea.md", framework="orbit")
# → 0.73

# Get recommended quests based on personality type
recommend(personality="engineer", sphere="T")
# → ["T-1", "T-3", "E-2"]

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Target Audience & Daily Use

🎓 Mentor / Program Coordinator

Their morning: A new member joins the community. They fill out an intro form: "I'm a biology grad, I want to build a startup around crop disease diagnostics." The mentor needs to direct them — learn first? build? research?

The problem: Without a framework, mentors give inconsistent advice. One says "go research", another says "start building". The new member gets lost.

How set-method helps:

from set_method import classify, recommend

profile = classify("I'm a biology grad, I want to build a startup around crop disease diagnostics")
# → {"science": 0.6, "entrepreneurship": 0.7, "technology": 0.8, "primary": "T"}

quests = recommend(personality="engineer", sphere=profile["primary"])
# → ["T-1", "T-3", "E-2"]

They run this during onboarding. The classification tells them which sphere to start in. The recommendation gives specific quest IDs the new member should tackle first.

Install: pip install set-method in the program's onboarding tool or dashboard backend.

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🚀 Startup Founder / Entrepreneur

Their morning: Wakes up with an idea. Writes a one-liner. Needs to validate — is this a tech play? a science project? a business? They need to know before committing time.

The problem: Most founders over-index on one dimension. A biotech founder thinks only about the science, ignoring go-to-market. A SaaS founder thinks only about code, ignoring research validation.

How set-method helps:

from set_method import classify, score

profile = classify("AI-powered platform that matches patients to clinical trials using genomics")
# → {"science": 0.6, "entrepreneurship": 0.7, "technology": 0.8, "primary": "T"}

readiness = score("AI-powered platform that matches patients to clinical trials", framework="orbit")
# → 0.72

They paste their idea into a tool powered by set-method. It shows them: "Your idea is 60% science, 70% entrepreneurship, 80% technology — you're under-investing in science validation." The orbit framework weights entrepreneurship higher (40%), reflecting venture readiness.

Install: pip install set-method in their venture planning tool or CLI.

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🧑‍💻 Community Platform Builder

Their morning: Building a platform that connects learners, builders, and researchers. Needs to auto-tag content, route users, and suggest next steps.

The problem: Manual tagging doesn't scale. Content sits in the wrong category. Users bounce because they can't find relevant material.

How set-method helps:

from set_method import classify

# Auto-tag every new piece of content
tag = classify("New tutorial: deploying ML models on edge devices with TensorFlow Lite")
# → {"science": 0.1, "entrepreneurship": 0.2, "technology": 0.9, "primary": "T"}

They integrate classify() into their content ingestion pipeline. Every new article, project, or idea gets auto-tagged with SET sphere weights. Users filter by sphere. The primary field routes content to the right hub.

Install: Add set-method to the platform's backend API dependencies.

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API Reference

classify(text: str) -> dict

Classifies input text into SET spheres using keyword matching and heuristics.

Returns: dict with science, entrepreneurship, technology scores (0–1) and primary sphere label (S, E, or T).

score(source: str, framework: str = "set") -> float

Scores a project, document, or idea against a framework.

Frameworks: "set" (equal weights) or "orbit" (entrepreneurship-weighted).

Returns: float 0–1.

recommend(personality: str, sphere: str) -> list[str]

Returns recommended quest IDs based on personality type and sphere.

Personalities: fighter, operator, accomplisher, leader, engineer, developer Spheres: S, E, T

Returns: list of quest IDs.

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Innovation

set-method is the first programmatic implementation of the SET methodology:

1. Automated sphere classification — no manual tagging, NLP-based keyword matching with weighted scoring
2. Dual framework scoring — set for balanced assessment, orbit for venture-readiness emphasis
3. Personality-quest mapping — connects who you are (personality type) with what you should do (quest), grounded in the SET sphere

No other toolkit operationalizes the Science-Entrepreneurship-Technology framework as installable software.

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License

MIT © K-RnD Lab