Spec Workflow logo

Spec Workflow

OrganizationPopular
TencentCloudBase
spec-workflow

Use when medium-to-large changes need explicit requirements, technical design, and task planning before implementation, especially for multi-module work, unclear acceptance criteria, or architecture-heavy requests.

Overview

PublisherTencentCloudBase
RepositoryCloudBase-AI-Toolkit
Skill namespec-workflow
Stars
1.1K
Forks
141
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Spec Workflow 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.
  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/TencentCloudBase/CloudBase-AI-Toolkit.git /tmp/CloudBase-AI-Toolkit
mkdir -p .claude/skills
cp -r /tmp/CloudBase-AI-Toolkit/config/source/skills/spec-workflow .claude/skills/spec-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Workflow 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 Workflow 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 Workflow 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.

Sibling skills (local only)

Sibling CloudBase skills ship beside this skill. Use local relative paths such as ../auth-tool-cloudbase/SKILL.md.

If a referenced sibling skill file is missing from this environment, ask the user to install the full CloudBase plugin (or the missing skill). Do not HTTP-fetch remote skill or protocol markdown into the agent context.

Spec Workflow

Activation Contract

Use this first when

  • The request is a new feature, multi-step product change, cross-module integration, or architecture/design task.
  • Acceptance criteria are unclear and need to be made explicit before implementation.
  • The work involves multiple files, user flows, database design, or UI design that needs staged confirmation.

Read before writing code if

  • You are unsure whether the task should go straight to coding or should first go through requirements, design, and task planning.
  • The request mentions a new page, a new system, a redesign, a workflow, or a multi-module refactor.

Then also read

  • Frontend page or visual design work -> ../ui-design/SKILL.md
  • Advanced data-model work -> ../data-model-creation/SKILL.md

Do NOT use for

  • Small bug fixes with clear scope.
  • One-file documentation updates.
  • Straightforward config changes.
  • Tiny refactors where the user already gave exact implementation instructions.

Common mistakes / gotchas

  • Jumping into coding before acceptance criteria are explicit.
  • Skipping user confirmation between requirements, design, and tasks.
  • Writing vague tasks that do not map back to user-visible outcomes.
  • Treating UI work as purely technical implementation without clarifying design intent.

Minimal checklist

  • Decide whether the change really needs the full spec flow.
  • If yes, stop and produce requirements first.
  • If the change is small, low-risk, and acceptance is already clear, allow direct execution without forcing spec artifacts.
  • Use EARS-style acceptance criteria.
  • Get confirmation before moving to the next phase.

When to use this skill

Use this workflow for structured development when you need to:

  • Define or refine a new feature
  • Design complex architecture
  • Coordinate changes across modules
  • Plan database or UI-heavy work
  • Improve requirement quality and acceptance boundaries

Decision rule

Use the full workflow when

  • The task is medium or large
  • The impact spans multiple modules
  • Acceptance boundaries are fuzzy
  • The user wants disciplined planning before implementation

Skip the full workflow when

  • The task is small, low-risk, and already precise
  • Goal, scope, and acceptance are already clear enough to execute directly
  • The user explicitly wants a direct code change with no planning phase

Core workflow

Phase 1: Requirements

Create specs/<spec_name>/requirements.md.

What to do:

  • Restate the problem and scope
  • Write user stories
  • Write acceptance criteria in EARS style
  • Clarify business rules, constraints, and non-goals

EARS pattern:

text
While <optional precondition>, when <optional trigger>, the <system name> shall <system response>

Example:

text
When the user submits the form, the booking system shall validate required fields before creating the record.

Phase 2: Design

Create specs/<spec_name>/design.md.

What to do:

  • Describe architecture and module boundaries
  • Explain technology choices and trade-offs
  • Define data model, API, security, and testing strategy as needed
  • Use Mermaid only when a diagram materially improves clarity

Phase 3: Tasks

Create specs/<spec_name>/tasks.md.

What to do:

  • Break the design into executable tasks
  • Keep tasks specific and reviewable
  • Link each task back to the relevant requirement
  • Update task status as work progresses

Task format:

markdown
# Implementation Plan

- [ ] 1. Task title
  - Specific work item
  - Another concrete step
  - _Requirement: 1

Phase 4: Execution

Only start implementation after the user confirms the task plan.

During execution:

  • Keep task status current
  • Finish one meaningful unit at a time
  • Preserve traceability from change -> task -> requirement

Working rules for the agent

  1. Ask follow-up questions when the request is underspecified; do not guess core product behavior.
  2. Require confirmation between requirements, design, and task breakdown.
  3. Pull in ui-design early when the change includes end-user pages or visual decisions.
  4. Keep documents concise but testable.
  5. Prefer user-visible outcomes over implementation-detail task names.

Output expectations

  • requirements.md -> problem, scope, user stories, EARS acceptance criteria
  • design.md -> architecture, technical approach, data/API/security/test notes
  • tasks.md -> actionable implementation checklist tied to requirements

Frequently asked questions

What does the Spec Workflow AI skill do?

Use when medium-to-large changes need explicit requirements, technical design, and task planning before implementation, especially for multi-module work, unclear acceptance criteria, or architecture-heavy requests.

Why use Spec Workflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/spec-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Spec Workflow?

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 Workflow?

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

Is the Spec Workflow AI skill free?

Yes. It is published on GitHub by TencentCloudBase 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇