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Vc Generate Spec

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withkynam
vc-generate-spec

Create or update a product-discovery SPEC (requirements doc) for user review. Use when turning RESEARCH findings plus user intent into a reviewable requirements artifact before INNOVATE/PLAN.

Overview

Publisherwithkynam
Repositoryvibecode-pro-max-kit
Skill namevc-generate-spec
Stars
1.1K
Forks
234
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Vc Generate Spec 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/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-generate-spec
  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/withkynam/vibecode-pro-max-kit.git /tmp/vibecode-pro-max-kit
mkdir -p .claude/skills
cp -r /tmp/vibecode-pro-max-kit/.claude/skills/vc-generate-spec .claude/skills/vc-generate-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vc Generate Spec 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 Vc Generate Spec 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 Vc Generate Spec 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.

Generate SPEC

Output style: Follow process/development-protocols/communication-standards.md — answer-first, plain language, no unexplained jargon, TL;DR on long responses.

Use this skill to produce the authoritative product-discovery SPEC artifact for a task — a plain-language requirements document written for user review, not for an engineer.

A SPEC captures what the user wants and why so the user can read it, recognize their own intent, and confirm "yes, build that" before any approach or code is chosen. It is the bridge between RESEARCH (the facts) and INNOVATE (the how). PLAN cannot start until a SPEC exists for non-trivial work.

SPEC consumes: RESEARCH findings + the user's stated intent/brainstorm. It does NOT consume a chosen approach or a Decision Summary — no approach exists yet at SPEC time.

Normal output is one SPEC file: {slug}_SPEC_{dd-mm-yy}.md.

For a phase program, the program-level (umbrella) SPEC is written once during the outer loop and governs every inner phase. Use templates/program-spec-template.md for that case. The inner loop never writes a SPEC.

Workflow

  1. Read references/spec-contract.md for the full section-by-section writing rules.
  2. Run date +%d-%m-%y before choosing the filename.
  3. Confirm the inputs are present: RESEARCH findings + the user's stated intent. If neither is present, emit SPEC_INTENT_BLOCKED instead of writing.
  4. Read process/context/all-context.md first, then load the relevant context group. When the work touches testing/verification, read process/context/tests/all-tests.md so acceptance-criteria scenarios are grounded in the real test-context chain.
  5. Save the SPEC inside the task folder alongside the plan: process/features/{feature}/active/{slug}_{date}/{slug}_SPEC_{date}.md (or process/general-plans/active/{slug}_{date}/{slug}_SPEC_{date}.md). For a phase program, the umbrella task folder holds {program-slug}_SPEC_{date}.md. Per task-folder artefact colocation, never write to the deprecated sibling reports/ or references/ dirs.
  6. Write the SPEC sections in canonical order (see references/spec-contract.md):
    • ## Summary
    • ## User Stories / Jobs To Be Done
    • ## What The User Wants (Behavioral Outcomes)
    • ## Flow / State Diagram (ASCII)
    • ## Acceptance Criteria (Testable Outcomes) — each criterion carries proven by: + strategy:
    • ## Out Of Scope
    • ## Constraints
    • ## Open Questions
    • ## Background / Research Findings
  7. Keep the document oriented for user review: plain language, no file paths, no library names, no schema or code. Anything engineer-only moves to Background or a later phase.
  8. Each acceptance criterion MUST be provable by comprehensive tests, with a fully-automated E2E/integration gate wherever the behavior is automatable. Agent-Probe / Known-Gap stand only as an explicitly-justified residual. Vacuous-green is banned.
  9. If ## Open Questions is non-empty at finalize (interactive session): emit SPEC_INTENT_BLOCKED and stop. Under /goal: record each as a backlog note and continue.
  10. Once Open Questions is empty/"None" (or backlogged under /goal): save the file and the agent emits PHASE_COMPLETE: SPEC.

Important Rules

  • The SPEC is written for a human reviewer. If a sentence only makes sense to an engineer, rewrite it or move it to Background.
  • Do NOT choose an implementation approach (INNOVATE's job, downstream), write implementation steps (PLAN), make schema decisions, or include code/library references.
  • Do NOT modify any file except the SPEC file being created.
  • Acceptance-criteria scenarios must be grounded in the RESEARCH test-context-discovery, not invented here.
  • Once INNOVATE/PLAN begins the SPEC is frozen — later scope gaps go to the phase report ## SPEC Gaps heading and a backlog note, never an edit to the SPEC.
  • For a phase program, write the umbrella SPEC once; the inner loop reads it and never writes a per-phase SPEC.

Required SPEC Sections

Every SPEC file must include all of the following, in this order:

  • ## Summary — one plain-language paragraph: what changes for the user and why
  • ## User Stories / Jobs To Be Done — the heart of the doc; "As a [user], I want [X], so that [Y]" or JTBD
  • ## What The User Wants (Behavioral Outcomes) — observable behavior only, no implementation
  • ## Flow / State Diagram — at least one ASCII flow/state diagram
  • ## Acceptance Criteria (Testable Outcomes) — observable outcomes, each with proven by: + strategy:; max 20
  • ## Out Of Scope — at least one item
  • ## Constraints — user-stated, system/process, and technical limits from research
  • ## Open Questions — each with an owner; empty/"None" required to finalize (interactive)
  • ## Background / Research Findings — key facts from RESEARCH that shaped the requirements

Use Markdown-structured sections, not a second machine-only schema. Markdown sections are stable across all agents (Claude, Codex, future systems) without requiring a parser.

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 Vc Generate Spec AI skill do?

Create or update a product-discovery SPEC (requirements doc) for user review. Use when turning RESEARCH findings plus user intent into a reviewable requirements artifact before INNOVATE/PLAN.

Why use Vc Generate Spec on TypingMind?

Because you install it once and use it with any model. Vc Generate Spec 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 Vc Generate Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/withkynam/vibecode-pro-max-kit/tree/main/.claude/skills/vc-generate-spec. 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 Vc Generate Spec?

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 Vc Generate Spec?

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

Is the Vc Generate Spec AI skill free?

Yes. It is published on GitHub by withkynam under the MIT license. 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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