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Spec First Mvp

Community
cnfjlhj
spec-first-mvp

Use when one idea feels exciting enough that the user may rush into implementation before the scope is stable. Turn the idea into a spec seed, define 1-3 throwaway MVP probes, and set a go/no-go gate before full design or execution.

Overview

Publishercnfjlhj
Repositoryai-collab-playbook
Skill namespec-first-mvp
Stars
455
Forks
44
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Spec First 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/cnfjlhj/ai-collab-playbook/tree/main/skills/full/spec-first-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/cnfjlhj/ai-collab-playbook.git /tmp/ai-collab-playbook
mkdir -p .claude/skills
cp -r /tmp/ai-collab-playbook/skills/full/spec-first-mvp .claude/skills/spec-first-mvp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec First 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 Spec First 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 Spec First 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.

Spec First MVP

Overview

Use this skill when the danger is not lack of ideas, but too much momentum too early.

The goal is to stop cyber-wishing-machine behavior by inserting one lightweight gate:

text
raw idea
  -> spec seed
  -> throwaway MVP probe
  -> inspect evidence
  -> go / no-go
  -> only then move to brainstorming, planning, or implementation

Rules

  • Separate problem, guess, solution shape, and wishful extras before proposing implementation.
  • Distill the raw idea into a spec seed first:
    • problem
    • user / context
    • first value to prove
    • explicit non-goals
    • pass / fail signal
  • Propose only 1-3 throwaway MVP probes. Each probe must answer one important uncertainty.
  • Prefer probes that are cheap, inspectable, and disposable:
    • one prompt plus example I/O
    • one static page or mock
    • one minimal retrieval path
    • one scoring or evaluation script
    • one tiny data flow
  • Treat the first MVP as a problem probe, not as the first production version.
  • After the probe, update the spec from evidence instead of expanding the original fantasy unchanged.
  • Route onward only after the user can say what was learned and what should happen next.

When to Use

Use when:

  • the user has one promising idea but keeps jumping straight to "just build it"
  • requirements drift quickly after a polished generated artifact already exists
  • the user asks for MVP, 最小验证, 先做个最小的, 先收敛一下, or 先不要赛博许愿机
  • the work involves a tool, workflow, research helper, personal knowledge system, or product concept whose shape is still unstable
  • the user wants a small probe or subagent prototype specifically to expose missing spec details

Do not use when:

  • multiple materially different directions still need comparison; use brainstorming
  • the task is already precise and ready to implement
  • the main deliverable is a durable plan or spec document rather than a validation loop

Workflow

  1. Triage the raw idea.

    • What problem is definitely real?
    • What part is still a guess?
    • What part is only "would be cool"?
  2. Write a spec seed.

markdown
### SPEC SEED
- Problem:
- User / Context:
- First Value To Prove:
- Non-Goals For This Round:
- Pass / Fail Signal:
  1. Define 1-3 throwaway MVP probes.

    • Each probe tests one risky assumption.
    • Each probe should be small enough that throwing it away is acceptable.
  2. Recommend one first probe.

    • Explain why it reduces uncertainty fastest.
    • State what evidence to inspect after it runs.
  3. Set the go/no-go gate.

markdown
### GO / NO-GO
- Continue if:
- Stop or reshape if:
- Open questions after probe:
  1. Route forward.
    • validated enough -> brainstorming, planning-with-files, or direct execution
    • still ambiguous -> another spec-first-mvp loop

Common Mistakes

  • turning the throwaway probe into a hidden full build
  • keeping too many goals in the first round
  • using generated polish as proof that the concept works
  • comparing giant systems before the core value has been isolated
  • calling something an MVP when it is still trying to validate five things at once

References

  • references/spec-seed-template.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Spec First Mvp AI skill do?

Use when one idea feels exciting enough that the user may rush into implementation before the scope is stable. Turn the idea into a spec seed, define 1-3 throwaway MVP probes, and set a go/no-go gate before full design or execution.

Why use Spec First Mvp on TypingMind?

Because you install it once and use it with any model. Spec First 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 Spec First Mvp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfjlhj/ai-collab-playbook/tree/main/skills/full/spec-first-mvp. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Spec First 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 Spec First Mvp?

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

Is the Spec First Mvp AI skill free?

It is published on GitHub by cnfjlhj. 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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