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Speckit Workflow

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tzachbon
speckit-workflow

Comprehensive understanding of the spec-kit methodology. Constitution-driven feature development with specify, plan, tasks, and implement phases.

Overview

Publishertzachbon
Repositorysmart-ralph
Skill namespeckit-workflow
Stars
559
Forks
50
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 tzachbon on GitHub. Read the source before you install it.

Installation

Install the Speckit 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.
    https://github.com/tzachbon/smart-ralph/tree/main/plugins/ralph-speckit/skills/speckit-workflow
  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/tzachbon/smart-ralph.git /tmp/smart-ralph
mkdir -p .claude/skills
cp -r /tmp/smart-ralph/plugins/ralph-speckit/skills/speckit-workflow .claude/skills/speckit-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit 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 Speckit 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 Speckit 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.

SpecKit Workflow

The SpecKit methodology is a constitution-driven approach to feature development. It ensures consistency across features by grounding all decisions in project principles.

Core Philosophy

Constitution First: Every feature is designed against the project's constitution - a living document of principles, constraints, and standards.

Governance Over Convention: Rather than implicit patterns, SpecKit makes governance explicit through:

  • Constitution principles (MUST, SHOULD, MAY)
  • Feature specifications tied to principles
  • Quality checklists as "unit tests for requirements"
  • Consistency analysis across artifacts

Directory Structure

text
.specify/
├── memory/
│   └── constitution.md       # Project principles and standards
├── .current-feature          # Active feature pointer
├── templates/                # Artifact templates
│   ├── spec-template.md
│   ├── plan-template.md
│   ├── tasks-template.md
│   └── checklist-template.md
└── specs/
    └── <id>-<name>/          # Feature directories
        ├── .speckit-state.json
        ├── .progress.md
        ├── .coordinator-prompt.md
        ├── spec.md           # Feature specification
        ├── plan.md           # Technical design
        ├── tasks.md          # Implementation tasks
        ├── research.md       # Research findings (optional)
        ├── data-model.md     # Entity definitions (optional)
        ├── contracts/        # API contracts (optional)
        └── checklists/       # Quality checklists

Feature ID System

Features use auto-incremented 3-digit IDs:

  • 001-user-auth
  • 002-payment-gateway
  • 003-notification-system

Benefits:

  • Natural ordering in filesystem
  • Easy reference in commits/PRs
  • Prevents naming conflicts

Workflow Phases

Phase 1: Constitution (/speckit:constitution)

Establish or update project-wide principles.

Inputs: Project context, team preferences Outputs: .specify/memory/constitution.md

Constitution sections:

  • Identity: Project name, purpose, core domain
  • Principles: MUST/SHOULD/MAY rules
  • Technology Stack: Languages, frameworks, tools
  • Patterns: Architecture, naming, error handling
  • Quality Standards: Testing, performance, security

Phase 2: Specify (/speckit:specify)

Define the feature specification against constitution.

Inputs: Feature goal, constitution reference Outputs: spec.md

Specification contains:

  • Feature overview and goals
  • User stories with acceptance criteria
  • Constitution alignment markers
  • Out of scope items
  • Dependencies and risks

Phase 3: Clarify (/speckit:clarify) - Optional

Resolve ambiguities through structured Q&A.

Inputs: spec.md with ambiguities Outputs: Updated spec.md with clarifications

Rules:

  • Maximum 5 clarifying questions per session
  • Each question has 2-4 options + "Other"
  • Recommendations marked when applicable
  • Clarifications appended to spec

Phase 4: Plan (/speckit:plan)

Generate technical design from specification.

Inputs: spec.md, constitution, codebase context Outputs: plan.md, optionally data-model.md, contracts/

Plan contains:

  • Architecture overview
  • Component breakdown
  • Data flow diagrams
  • API contracts
  • Integration points
  • Risk mitigation

Phase 5: Tasks (/speckit:tasks)

Break plan into dependency-ordered implementation tasks.

Inputs: plan.md, spec.md Outputs: tasks.md

Task format:

markdown
- [ ] T001 [P] [US1] Task description `path/to/file.ts`

Components:

  • T001: Sequential task ID
  • [P]: Parallel marker (optional)
  • [US1]: User story reference (optional)
  • Description with file path

Task phases:

  1. Setup: Environment, dependencies, scaffolding
  2. Core: Main implementation tasks
  3. Integration: Connect components
  4. Polish: Error handling, edge cases
  5. Verification: Quality checkpoints

Phase 6: Implement (/speckit:implement)

Execute tasks via Ralph Wiggum loop.

Inputs: tasks.md, state file Outputs: Code changes, commits, updated progress

Execution model:

  • Coordinator reads state, delegates to executor
  • 4-layer verification before advancing
  • Parallel execution for [P] marked tasks
  • Fresh context per task

State Management

State File (.speckit-state.json)

json
{
  "featureId": "001",
  "name": "user-auth",
  "basePath": ".specify/specs/001-user-auth",
  "phase": "execution",
  "taskIndex": 0,
  "totalTasks": 15,
  "taskIteration": 1,
  "maxTaskIterations": 5,
  "globalIteration": 1,
  "maxGlobalIterations": 100,
  "awaitingApproval": false
}

Progress File (.progress.md)

Tracks:

  • Completed tasks with commit hashes
  • Learnings and context for future tasks
  • Blockers and resolutions
  • Cross-task dependencies

Quality Assurance

Checklists (/speckit:checklist)

Domain-specific quality checklists:

  • UX checklist
  • API checklist
  • Security checklist
  • Performance checklist
  • Accessibility checklist

Checklists are "unit tests for requirements" - verifiable criteria before implementation.

Analyze (/speckit:analyze)

Cross-artifact consistency analysis:

  • Spec ↔ Constitution alignment
  • Plan ↔ Spec coverage
  • Tasks ↔ Plan traceability
  • Identifies gaps, conflicts, ambiguities

Command Reference

CommandPurposePhase
/speckit:start <name>Create or resume featureEntry
/speckit:constitutionCreate/update project principles1
/speckit:specifyDefine feature specification2
/speckit:clarifyResolve spec ambiguities3
/speckit:planGenerate technical design4
/speckit:tasksBreak plan into tasks5
/speckit:implementExecute tasks6
/speckit:analyzeCheck consistencyAny
/speckit:checklistGenerate quality checklistAny
/speckit:statusShow current stateAny
/speckit:switch <id>Change active featureAny
/speckit:cancelStop execution, cleanupAny

Agent Ecosystem

AgentPurposeUsed By
constitution-architectCreate/update constitutionconstitution
spec-analystGenerate specificationsspecify
plan-architectTechnical designplan
task-plannerTask breakdowntasks
spec-executorExecute single taskimplement
qa-engineerVerification tasksimplement

Constitution Integration

All phases reference the constitution:

  1. Specify: Maps features to constitution principles
  2. Plan: Architecture follows constitution patterns
  3. Tasks: Quality checkpoints enforce constitution
  4. Implement: Executor validates against standards

Constitution markers in artifacts:

  • [C§3.1]: References constitution section 3.1
  • [MUST]: Required by constitution
  • [SHOULD]: Recommended by constitution
  • [MAY]: Optional per constitution

Best Practices

Starting New Features

  1. Ensure constitution exists and is current
  2. Use descriptive feature names (kebab-case)
  3. Include clear success criteria in spec
  4. Reference related features if applicable

During Implementation

  1. Follow task order (dependencies matter)
  2. Commit after each task
  3. Update progress with learnings
  4. Run verification checkpoints

Maintaining Constitution

  1. Version constitution changes semantically
  2. Run sync impact analysis after updates
  3. Update affected features if needed
  4. Document rationale for changes

Frequently asked questions

What does the Speckit Workflow AI skill do?

Comprehensive understanding of the spec-kit methodology. Constitution-driven feature development with specify, plan, tasks, and implement phases.

Why use Speckit Workflow on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tzachbon/smart-ralph/tree/main/plugins/ralph-speckit/skills/speckit-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Speckit 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 Speckit Workflow?

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

Is the Speckit Workflow AI skill free?

Yes. It is published on GitHub by tzachbon 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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