Spec

Organization

AgentiveStack

spec

Interview the user to produce a feature spec (PRD), grounded in the project's domain model, context map, and ubiquitous language. Outputs a structured spec document. Use when starting a new feature, enhancement, or significant change.

Overview

Publisher	AgentiveStack
Repository	`skills`
Skill name	`spec`
Stars	72
Forks	17
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 AgentiveStack on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/AgentiveStack/skills/tree/main/spec
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.

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/AgentiveStack/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/spec .claude/skills/spec

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Feature Spec

Produce a structured spec for a feature or change through a focused interview, grounded in the project's domain model.

Process

1. Discover project context

Read existing domain documentation:

docs/CONTEXT_MAP.md — which contexts exist and how they relate
docs/UBIQUITOUS_LANGUAGE.md — canonical terminology
docs/contexts/*/CONTEXT.md — ownership, invariants, contracts for relevant contexts
CLAUDE.md — project-level context

2. Understand the request

If the user hasn't provided enough context, interview briefly (3-5 questions max):

What problem does this solve? For whom?
What does success look like?
What's explicitly out of scope?
Are there constraints (timeline, tech, compatibility)?

Don't over-interview. If the intent is clear from conversation context, skip to the next step.

3. Explore the codebase

Explore the relevant areas to understand:

Current state of related features
Existing patterns and conventions
Which bounded contexts this feature touches
What interfaces already exist that this feature should use or extend

4. Identify modules and test boundaries

Sketch the major modules to build or modify. For each:

What bounded context does it belong to?
Is it a new concept or an extension of an existing one?
Does it cross context boundaries? (If so, what contracts are needed?)
Is there an opportunity for a deep module (small interface, significant implementation)?
Should it have tests? (Use pragmatic TDD: test core logic and boundaries, skip glue/UI)

Check with the user that these modules match their expectations and which should have tests.

5. Write the spec

Write the spec using the template below. Use domain language from UBIQUITOUS_LANGUAGE.md throughout. Save to docs/contexts/<primary-context>/specs/<slug>.md.

If the feature spans multiple contexts, save to the primary context and note the cross-context impact.

[Feature Name]

Status: Draft Context(s): [Which bounded context(s) this touches] Date: [YYYY-MM-DD]

Problem

The problem from the user's perspective. What's broken, missing, or suboptimal?

Solution

The solution from the user's perspective. What will they be able to do after this ships?

User Stories

As a [actor], I want [capability], so that [benefit]
...

Cover the full scope. Include edge cases and error scenarios.

Implementation Decisions

Decisions made during the spec process. Include:

Modules to build or modify (by domain concept, not file path)
Interface contracts between modules
Schema changes (Convex tables, ClickHouse tables)
API contracts (new endpoints, changed responses)
Cross-context contracts (what this feature needs from or exposes to other contexts)
Architectural decisions (with brief rationale)

Do NOT include file paths or code snippets — they go stale.

Testing Strategy

Which modules will have tests (pragmatic TDD: core logic + boundaries)
What makes a good test for this feature (behavioral, through public interfaces)
Prior art (similar tests that already exist in the codebase)
What to mock (only system boundaries: external APIs, databases)

Out of Scope

What this spec explicitly does NOT cover. Be specific.

Open Questions

Unresolved decisions that need answers before or during implementation.

6. Domain model updates

If the spec introduced new terms, refined existing ones, or made decisions that affect the domain model:

Update docs/UBIQUITOUS_LANGUAGE.md with new terms
Update relevant docs/contexts/<name>/CONTEXT.md with new invariants or contracts
Consider whether any decisions warrant an ADR (hard to reverse + surprising + real trade-off)

Related skills

/domain — stress-test the spec against the domain model before implementation
/slice — break the spec into vertical slices for implementation
/tdd — implement each slice with tracer-bullet TDD

Frequently asked questions

What does the Spec AI skill do?

Why use Spec on TypingMind?

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

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

Which AI models can use 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 Spec?

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

Is the Spec AI skill free?

Yes. It is published on GitHub by AgentiveStack 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Feature Spec

Process

1. Discover project context

2. Understand the request

3. Explore the codebase

4. Identify modules and test boundaries

5. Write the spec

[Feature Name]

Problem

Solution

User Stories

Implementation Decisions

Testing Strategy

Out of Scope

Open Questions

6. Domain model updates

Related skills

Frequently asked questions

Related AI Skills

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