Speckit Plan logo

Speckit Plan

Organization
dotCMS
speckit-plan

Execute the implementation planning workflow using the plan template to generate design artifacts.

Overview

PublisherdotCMS
Repositorycore
Skill namespeckit-plan
Stars
971
Forks
487
Bundled files
Instructions only
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 dotCMS on GitHub. Read the source before you install it.

Installation

Install the Speckit Plan 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/dotCMS/core/tree/main/.claude/skills/speckit-plan
  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/dotCMS/core.git /tmp/core
mkdir -p .claude/skills
cp -r /tmp/core/.claude/skills/speckit-plan .claude/skills/speckit-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit Plan 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 Plan 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 Plan 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.

User Input

text
$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Pre-Execution Checks

Check for extension hooks (before planning):

  • Check if .specify/extensions.yml exists in the project root.
  • If it exists, read it and look for entries under the hooks.before_plan key
  • If the YAML cannot be parsed or is invalid, do not skip silently: tell the user that .specify/extensions.yml could not be read (include the parser error) and that no hooks were checked, including any mandatory (optional: false) hooks registered there, then continue normally
  • Filter out hooks where enabled is explicitly false. Treat hooks without an enabled field as enabled by default.
  • For each remaining hook, do not attempt to interpret or evaluate hook condition expressions:
    • If the hook has no condition field, or it is null/empty, treat the hook as executable
    • If the hook defines a non-empty condition, skip the hook and leave condition evaluation to the HookExecutor implementation
  • When constructing command invocations from hook command names, replace dots (.) with hyphens (-). For example, speckit.git.commit → /speckit-git-commit.
  • For each executable hook, output the following based on its optional flag:
    • Optional hook (optional: true):
      ## Extension Hooks
      
      **Optional Pre-Hook**: {extension}
      Command: `/{command}`
      Description: {description}
      
      Prompt: {prompt}
      To execute: `/{command}`
    • Mandatory hook (optional: false):
      ## Extension Hooks
      
      **Automatic Pre-Hook**: {extension}
      Executing: `/{command}`
      EXECUTE_COMMAND: {command}
      
      Wait for the result of the hook command before proceeding to the Outline.
      After emitting the block above you MUST actually invoke the hook and wait for it to finish before continuing. Run it the same way you would run the command yourself in this agent/session (the invocation may differ from the literal {command} id shown above, e.g. a skills-mode agent runs it as /skill:speckit-... or $speckit-...). Emitting the block alone does not run the hook.
  • If no hooks are registered or .specify/extensions.yml does not exist, skip silently

Outline

  1. Setup: Run .specify/scripts/bash/setup-plan.sh --json from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, FEATURE_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot").

  2. Load context: Read FEATURE_SPEC and .specify/memory/constitution.md. Load IMPL_PLAN template (already copied).

  3. Execute plan workflow: Follow the structure in IMPL_PLAN template to:

    • Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
    • Fill Constitution Check section from constitution
    • Evaluate gates (ERROR if violations unjustified)
    • Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
    • Phase 1: Generate data-model.md, contracts/, quickstart.md
    • Re-evaluate Constitution Check post-design

Mandatory Post-Execution Hooks

You MUST complete this section before reporting completion to the user.

Check if .specify/extensions.yml exists in the project root.

  • If it does not exist, or no hooks are registered under hooks.after_plan, skip to the Completion Report.
  • If it exists, read it and look for entries under the hooks.after_plan key.
  • If the YAML cannot be parsed or is invalid, do not skip silently: tell the user that .specify/extensions.yml could not be read (include the parser error) and that no hooks were checked, including any mandatory (optional: false) hooks registered there, then continue to the Completion Report.
  • Filter out hooks where enabled is explicitly false. Treat hooks without an enabled field as enabled by default.
  • For each remaining hook, do not attempt to interpret or evaluate hook condition expressions:
    • If the hook has no condition field, or it is null/empty, treat the hook as executable
    • If the hook defines a non-empty condition, skip the hook and leave condition evaluation to the HookExecutor implementation
  • When constructing command invocations from hook command names, replace dots (.) with hyphens (-). For example, speckit.git.commit → /speckit-git-commit.
  • For each executable hook, output the following based on its optional flag:
    • Mandatory hook (optional: false) — You MUST emit EXECUTE_COMMAND: for each mandatory hook:
      ## Extension Hooks
      
      **Automatic Hook**: {extension}
      Executing: `/{command}`
      EXECUTE_COMMAND: {command}
      After emitting the block above you MUST actually invoke the hook and wait for it to finish before continuing. Run it the same way you would run the command yourself in this agent/session (the invocation may differ from the literal {command} id shown above, e.g. a skills-mode agent runs it as /skill:speckit-... or $speckit-...). Emitting the block alone does not run the hook.
    • Optional hook (optional: true):
      ## Extension Hooks
      
      **Optional Hook**: {extension}
      Command: `/{command}`
      Description: {description}
      
      Prompt: {prompt}
      To execute: `/{command}`

Completion Report

Command ends after Phase 1 design. Report branch, IMPL_PLAN path, and generated artifacts.

Phases

Phase 0: Outline & Research

  1. Extract unknowns from Technical Context above:

    • For each NEEDS CLARIFICATION → research task
    • For each dependency → best practices task
    • For each integration → patterns task
  2. Generate and dispatch research agents:

    text
    For each unknown in Technical Context:
      Task: "Research {unknown} for {feature context}"
    For each technology choice:
      Task: "Find best practices for {tech} in {domain}"
  3. Consolidate findings in research.md using format:

    • Decision: [what was chosen]
    • Rationale: [why chosen]
    • Alternatives considered: [what else evaluated]

Output: research.md with all NEEDS CLARIFICATION resolved

Phase 1: Design & Contracts

Prerequisites: research.md complete

  1. Extract entities from feature spec → data-model.md:

    • Entity name, fields, relationships
    • Validation rules from requirements
    • State transitions if applicable
  2. Define interface contracts (if project has external interfaces) → /contracts/:

    • Identify what interfaces the project exposes to users or other systems
    • Document the contract format appropriate for the project type
    • Examples: public APIs for libraries, command schemas for CLI tools, endpoints for web services, grammars for parsers, UI contracts for applications
    • Skip if project is purely internal (build scripts, one-off tools, etc.)
  3. Create quickstart validation guide → quickstart.md:

    • Document runnable validation scenarios that prove the feature works end-to-end
    • Include prerequisites, setup commands, test/run commands, and expected outcomes
    • Use links or references to contracts and data model details instead of duplicating them
    • Do not include full implementation code, model/service/controller bodies, migrations, or complete test suites
    • Keep this artifact as a validation/run guide; implementation details belong in tasks.md and the implementation phase

Output: data-model.md, /contracts/*, quickstart.md

Key rules

  • Use absolute paths for filesystem operations; use project-relative paths for references in documentation
  • ERROR on gate failures or unresolved clarifications

Done When

  • Plan workflow executed and design artifacts generated
  • Extension hooks dispatched or skipped according to the rules in Mandatory Post-Execution Hooks above
  • Completion reported to user with branch, plan path, and generated artifacts

Frequently asked questions

What does the Speckit Plan AI skill do?

Execute the implementation planning workflow using the plan template to generate design artifacts.

Why use Speckit Plan on TypingMind?

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

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

Which AI models can use Speckit Plan?

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 Plan?

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

Is the Speckit Plan AI skill free?

It is published on GitHub by dotCMS. 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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