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Slc Or Mvp

Community
ojiudezue
slc-or-mvp

Use when the user is deciding what kind of first version to ship. Triggers on phrases like "MVP", "should we build an MVP", "minimum viable", "what's the smallest thing", "first version", "ship vs learn", "scope down". Implements the SLC vs MVP decision framework from Ch 3 of Building Rocketships.

Overview

Publisherojiudezue
Repositoryproductmind-skills
Skill nameslc-or-mvp
Stars
81
Forks
18
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ojiudezue on GitHub. Read the source before you install it.

Installation

Install the Slc Or Mvp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ojiudezue/productmind-skills/tree/main/skills/slc-or-mvp
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ojiudezue/productmind-skills.git /tmp/productmind-skills
mkdir -p .claude/skills
cp -r /tmp/productmind-skills/skills/slc-or-mvp .claude/skills/slc-or-mvp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Slc Or Mvp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Slc Or Mvp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Slc Or Mvp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SLC or MVP

What this does

Runs Ch 3's SLC-vs-MVP decision framework. Returns a verdict on which kind of first version to build, plus the scope cuts needed to actually get there, plus what gets thrown away from each path. Refuses to recommend shipping an MVP as if it were a customer product — that's the book's hard anti-pattern.

When to use

Trigger when the user:

  • Is asking "what should the MVP be"
  • Is debating whether to ship to customers or run a learning experiment
  • Has a scope they can't finish and needs to decide what to drop
  • Is pre-launch and unsure how polished v1 needs to be
  • Is mid-build and the team is arguing about "is this good enough to ship"

When NOT to use

  • The product is already shipped and in market — use aha-mapper for activation issues, scope-cutter for feature pruning
  • The problem hasn't been validated yet — run sharp-problem-test first; a fuzzy problem makes both MVP and SLC questions unanswerable
  • The user is asking about pricing or tier structure — that's pricing-tier-math

The two options (from Ch 3)

MVP — Minimum Viable Product

Purpose: learning, not adoption. Tests a specific hypothesis about problem, customer, or willingness to pay.

Properties:

  • Discardable. No one will be heartbroken when it's deleted. No technical debt is being incurred to maintain it.
  • Narrow. Tests one or two hypotheses, not a product.
  • Short-lived. Days to weeks of build.
  • Often manual. A landing page, a Figma click-through, a manual back-end pretending to be an algorithm, a concierge service.
  • Honest about its nature. Users know it's an experiment, or you're collecting signal without claiming to deliver a product.

SLC — Simple, Lovable, Complete

Purpose: adoption. Ships to customers as a real, smaller version of the product.

Properties:

  • Simple — narrowed to one workflow, not many. Smaller surface area.
  • Lovable — quality is high in the slice that exists. Not "barely works"; actually delights inside the scope.
  • Complete — the workflow it covers is end-to-end. Users can hit the aha moment without escape hatches or "coming soon" gaps.
  • Maintainable. This codebase is going to grow, not be thrown away.

The decision rule

QuestionAnswer points to
Is the problem still being validated?MVP
Are you shipping to win adoption / first revenue?SLC
Are users expected to fall in love with this?SLC
Are you OK if no one keeps using it?MVP
Will you delete this code in 3 months?MVP
Will you build the v2 by extending this?SLC

If the answers split, pick SLC. Most "MVPs" should have been SLCs — the book's running observation. The cost of a real shipped product is higher, but the option value of users actually adopting is much higher.

Refusal mode

If the user says "MVP" but the answers point to SLC (paying users expected, will build the v2 from this code), refuse to call it an MVP. Recommend SLC and name the additional scope cuts needed to get there. The "MVP-shipped-as-product" anti-pattern is the most common failure mode in the book — customers form expectations, the team incurs debt to maintain throwaway code, and discarding the experiment is no longer politically possible.

Inputs

  • The product or feature in question
  • Current scope (features being considered)
  • The stage: pre-validation, pre-launch, post-launch
  • Who the users will be (paying customers vs. friendly test users)
  • The hypothesis being tested (if it's truly MVP territory)

Output structure

## Verdict: [MVP | SLC | NEEDS_MORE_EVIDENCE]

### Reasoning
[Which decision-rule signals point which way + how they weigh]

### Scope cuts to reach the recommended path
- Cut: [feature] — [why]
- Cut: [feature] — [why]

### What gets thrown away (MVP path) OR What stays in (SLC path)
[Specific list]

### Acceptance criteria
[How you'll know v1 is done]

### Recommendation
[Concrete next step]

### Analogous case from the book
[A named example]

Worked examples from the book

Calendly v1 (Ch 3) → SLC

  • Simple: one event type, one calendar, one share link. Not "all scheduling needs."
  • Lovable: the booking flow felt magical — paste link, recipient picks a slot, done. No friction.
  • Complete: the workflow ran end-to-end. No "coming soon" gaps in the core path.
  • Verdict: SLC, not MVP. Tope shipped a product, not an experiment.

A typical landing-page test → MVP

  • Discardable: no one is hurt when the page comes down
  • Tests a hypothesis: "will people give us their email for this promise?"
  • Manual back-end: spreadsheet of waitlist, no real product behind it
  • Verdict: MVP, correctly scoped.

Gotchas

  • "MVP" used as cover for "we don't want to invest in quality." Most shipped MVPs are actually under-built SLCs. Customers showed up and got a bad experience; the team learned the wrong lesson ("users don't want this" when the truth is "users wouldn't tolerate THIS version").
  • SLC creep — the team turns SLC into "v1.0 final." SLC means Simple + Lovable + Complete. If the scope keeps expanding to add every nice-to-have, you've lost the S. Re-cut.
  • An MVP that secretly aspires to be a product. If you find yourself picking React, setting up CI, and writing tests for what you said was an MVP, it's an SLC pretending to be an MVP. Either commit to SLC and raise the bar, or strip back to true MVP scope.

Anti-patterns to flag

If the user's plan has any of these, surface them:

  • "MVP" but customers are paying for it — call it SLC and raise the bar
  • "MVP" but the team will extend this codebase in v2 — call it SLC
  • SLC but the workflow has "coming soon" gaps — it's not Complete; cut scope until it is
  • SLC but quality in the existing slice is sloppy — it's not Lovable; invest in polish on the narrow scope before adding more

Source

Ch 3: SLC or MVP.


About this skill

This skill is part of the ProductMind Skills catalog — open-source product-judgment skills for AI coding agents.

Source: Building Rocketships by Oji and Ezinne Udezue — Chapter 3. This skill applies one named framework from the book. It works standalone in any agent that supports the SKILL.md standard.

Want the orchestrated version? The RocketshipsGPT agent runs all 13 book skills together, with persistent memory, framework-fidelity evals, voice-checked output, and the book's anti-pattern catches built in. → rocketshipsgpt.productmind.co

License: Skill content licensed under CC BY-SA 4.0. Attribution required; derivative works must share alike. See ADR 0005 for rationale.

Skill version: 1.0.0 · Published: 2026-06-05

Frequently asked questions

What does the Slc Or Mvp AI skill do?

Use when the user is deciding what kind of first version to ship. Triggers on phrases like "MVP", "should we build an MVP", "minimum viable", "what's the smallest thing", "first version", "ship vs learn", "scope down". Implements the SLC vs MVP decision framework from Ch 3 of Building Rocketships.

Why use Slc Or Mvp on TypingMind?

Because you install it once and use it with any model. Slc Or Mvp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Slc Or Mvp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ojiudezue/productmind-skills/tree/main/skills/slc-or-mvp. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Slc Or Mvp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Slc Or Mvp?

As many as you like. As long as a model supports skills, you can use Slc Or Mvp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Slc Or Mvp AI skill free?

It is published on GitHub by ojiudezue. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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