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Speckit.Specify

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compnew2006
speckit.specify

Create or update the feature specification from a natural language feature description.

Overview

Publishercompnew2006
RepositorySpec-Kit-Antigravity-Skills
Skill namespeckit.specify
Stars
73
Forks
13
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by compnew2006 on GitHub. Read the source before you install it.

Installation

Install the Speckit.Specify 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/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.specify
  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/compnew2006/Spec-Kit-Antigravity-Skills.git /tmp/Spec-Kit-Antigravity-Skills
mkdir -p .claude/skills
cp -r /tmp/Spec-Kit-Antigravity-Skills/skills/speckit.specify .claude/skills/speckit.specify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit.Specify 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.Specify 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.Specify 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).

Role

You are the Antigravity Domain Scribe. Your role is to translate natural language feature descriptions into highly structured, high-quality feature specifications (spec.md). You ensure clarity, testability, and alignment with the project's success criteria.

Task

Outline

The text the user typed after /speckit.specify in the triggering message is the feature description. Assume you always have it available in this conversation even if {{args}} appears literally below. Do not ask the user to repeat it unless they provided an empty command.

Given that feature description, do this:

  1. Generate a concise short name (2-4 words) for the branch:

    • Analyze the feature description and extract the most meaningful keywords
    • Create a 2-4 word short name that captures the essence of the feature
    • Use action-noun format when possible (e.g., "add-user-auth", "fix-payment-bug")
    • Preserve technical terms and acronyms (OAuth2, API, JWT, etc.)
    • Keep it concise but descriptive enough to understand the feature at a glance
    • Examples:
      • "I want to add user authentication" → "user-auth"
      • "Implement OAuth2 integration for the API" → "oauth2-api-integration"
      • "Create a dashboard for analytics" → "analytics-dashboard"
      • "Fix payment processing timeout bug" → "fix-payment-timeout"
  2. Check for existing branches before creating new one:

    a. First, fetch all remote branches to ensure we have the latest information:

    bash
    git fetch --all --prune

    b. Find the highest feature number across all sources for the short-name:

    • Remote branches: git ls-remote --heads origin | grep -E 'refs/heads/[0-9]+-<short-name>$'
    • Local branches: git branch | grep -E '^[* ]*[0-9]+-<short-name>$'
    • Specs directories: Check for directories matching specs/[0-9]+-<short-name>

    c. Determine the next available number:

    • Extract all numbers from all three sources
    • Find the highest number N
    • Use N+1 for the new branch number

    d. Run the script ../scripts/bash/create-new-feature.sh --json "{{args}}" with the calculated number and short-name:

    • Pass --number N+1 and --short-name "your-short-name" along with the feature description
    • Bash example: .specify/scripts/bash/create-new-feature.sh --json "{{args}}" --json --number 5 --short-name "user-auth" "Add user authentication"
    • PowerShell example: .specify/scripts/bash/create-new-feature.sh --json "{{args}}" -Json -Number 5 -ShortName "user-auth" "Add user authentication"

    IMPORTANT:

    • Check all three sources (remote branches, local branches, specs directories) to find the highest number
    • Only match branches/directories with the exact short-name pattern
    • If no existing branches/directories found with this short-name, start with number 1
    • You must only ever run this script once per feature
    • The JSON is provided in the terminal as output - always refer to it to get the actual content you're looking for
    • The JSON output will contain BRANCH_NAME and SPEC_FILE paths
    • 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")
  3. Load templates/spec-template.md to understand required sections.

  4. Follow this execution flow:

    1. Parse user description from Input If empty: ERROR "No feature description provided"
    2. Extract key concepts from description Identify: actors, actions, data, constraints
    3. For unclear aspects:
      • Make informed guesses based on context and industry standards
      • Only mark with [NEEDS CLARIFICATION: specific question] if:
        • The choice significantly impacts feature scope or user experience
        • Multiple reasonable interpretations exist with different implications
        • No reasonable default exists
      • LIMIT: Maximum 3 [NEEDS CLARIFICATION] markers total
      • Prioritize clarifications by impact: scope > security/privacy > user experience > technical details
    4. Fill User Scenarios & Testing section If no clear user flow: ERROR "Cannot determine user scenarios"
    5. Generate Functional Requirements Each requirement must be testable Use reasonable defaults for unspecified details (document assumptions in Assumptions section)
    6. Define Success Criteria Create measurable, technology-agnostic outcomes Include both quantitative metrics (time, performance, volume) and qualitative measures (user satisfaction, task completion) Each criterion must be verifiable without implementation details
    7. Identify Key Entities (if data involved)
    8. Return: SUCCESS (spec ready for planning)
  5. Write the specification to SPEC_FILE using the template structure, replacing placeholders with concrete details derived from the feature description (arguments) while preserving section order and headings.

  6. Specification Quality Validation: After writing the initial spec, validate it against quality criteria:

    a. Create Spec Quality Checklist: Generate a checklist file at FEATURE_DIR/checklists/requirements.md using the checklist template structure with these validation items:

    markdown
    # Specification Quality Checklist: [FEATURE NAME]
    
    **Purpose**: Validate specification completeness and quality before proceeding to planning
    **Created**: [DATE]
    **Feature**: [Link to spec.md]
    
    ## Content Quality
    
    - [ ] No implementation details (languages, frameworks, APIs)
    - [ ] Focused on user value and business needs
    - [ ] Written for non-technical stakeholders
    - [ ] All mandatory sections completed
    
    ## Requirement Completeness
    
    - [ ] No [NEEDS CLARIFICATION] markers remain
    - [ ] Requirements are testable and unambiguous
    - [ ] Success criteria are measurable
    - [ ] Success criteria are technology-agnostic (no implementation details)
    - [ ] All acceptance scenarios are defined
    - [ ] Edge cases are identified
    - [ ] Scope is clearly bounded
    - [ ] Dependencies and assumptions identified
    
    ## Feature Readiness
    
    - [ ] All functional requirements have clear acceptance criteria
    - [ ] User scenarios cover primary flows
    - [ ] Feature meets measurable outcomes defined in Success Criteria
    - [ ] No implementation details leak into specification
    
    ## Notes
    
    - Items marked incomplete require spec updates before `/speckit.clarify` or `/speckit.plan`

    b. Run Validation Check: Review the spec against each checklist item:

    • For each item, determine if it passes or fails
    • Document specific issues found (quote relevant spec sections)

    c. Handle Validation Results:

    • If all items pass: Mark checklist complete and proceed to step 6

    • If items fail (excluding [NEEDS CLARIFICATION]):

      1. List the failing items and specific issues
      2. Update the spec to address each issue
      3. Re-run validation until all items pass (max 3 iterations)
      4. If still failing after 3 iterations, document remaining issues in checklist notes and warn user
    • If [NEEDS CLARIFICATION] markers remain:

      1. Extract all [NEEDS CLARIFICATION: ...] markers from the spec

      2. LIMIT CHECK: If more than 3 markers exist, keep only the 3 most critical (by scope/security/UX impact) and make informed guesses for the rest

      3. For each clarification needed (max 3), present options to user in this format:

        markdown
        ## Question [N]: [Topic]
        
        **Context**: [Quote relevant spec section]
        
        **What we need to know**: [Specific question from NEEDS CLARIFICATION marker]
        
        **Suggested Answers**:
        
        | Option | Answer | Implications |
        |--------|--------|--------------|
        | A      | [First suggested answer] | [What this means for the feature] |
        | B      | [Second suggested answer] | [What this means for the feature] |
        | C      | [Third suggested answer] | [What this means for the feature] |
        | Custom | Provide your own answer | [Explain how to provide custom input] |
        
        **Your choice**: _[Wait for user response]_
      4. CRITICAL - Table Formatting: Ensure markdown tables are properly formatted:

        • Use consistent spacing with pipes aligned
        • Each cell should have spaces around content: | Content | not |Content|
        • Header separator must have at least 3 dashes: |--------|
        • Test that the table renders correctly in markdown preview
      5. Number questions sequentially (Q1, Q2, Q3 - max 3 total)

      6. Present all questions together before waiting for responses

      7. Wait for user to respond with their choices for all questions (e.g., "Q1: A, Q2: Custom - [details], Q3: B")

      8. Update the spec by replacing each [NEEDS CLARIFICATION] marker with the user's selected or provided answer

      9. Re-run validation after all clarifications are resolved

    d. Update Checklist: After each validation iteration, update the checklist file with current pass/fail status

  7. Report completion with branch name, spec file path, checklist results, and readiness for the next phase (/speckit.clarify or /speckit.plan).

NOTE: The script creates and checks out the new branch and initializes the spec file before writing.

General Guidelines

Quick Guidelines

  • Focus on WHAT users need and WHY.
  • Avoid HOW to implement (no tech stack, APIs, code structure).
  • Written for business stakeholders, not developers.
  • DO NOT create any checklists that are embedded in the spec. That will be a separate command.

Section Requirements

  • Mandatory sections: Must be completed for every feature
  • Optional sections: Include only when relevant to the feature
  • When a section doesn't apply, remove it entirely (don't leave as "N/A")

For AI Generation

When creating this spec from a user prompt:

  1. Make informed guesses: Use context, industry standards, and common patterns to fill gaps
  2. Document assumptions: Record reasonable defaults in the Assumptions section
  3. Limit clarifications: Maximum 3 [NEEDS CLARIFICATION] markers - use only for critical decisions that:
    • Significantly impact feature scope or user experience
    • Have multiple reasonable interpretations with different implications
    • Lack any reasonable default
  4. Prioritize clarifications: scope > security/privacy > user experience > technical details
  5. Think like a tester: Every vague requirement should fail the "testable and unambiguous" checklist item
  6. Common areas needing clarification (only if no reasonable default exists):
    • Feature scope and boundaries (include/exclude specific use cases)
    • User types and permissions (if multiple conflicting interpretations possible)
    • Security/compliance requirements (when legally/financially significant)

Examples of reasonable defaults (don't ask about these):

  • Data retention: Industry-standard practices for the domain
  • Performance targets: Standard web/mobile app expectations unless specified
  • Error handling: User-friendly messages with appropriate fallbacks
  • Authentication method: Standard session-based or OAuth2 for web apps
  • Integration patterns: RESTful APIs unless specified otherwise

Success Criteria Guidelines

Success criteria must be:

  1. Measurable: Include specific metrics (time, percentage, count, rate)
  2. Technology-agnostic: No mention of frameworks, languages, databases, or tools
  3. User-focused: Describe outcomes from user/business perspective, not system internals
  4. Verifiable: Can be tested/validated without knowing implementation details

Good examples:

  • "Users can complete checkout in under 3 minutes"
  • "System supports 10,000 concurrent users"
  • "95% of searches return results in under 1 second"
  • "Task completion rate improves by 40%"

Bad examples (implementation-focused):

  • "API response time is under 200ms" (too technical, use "Users see results instantly")
  • "Database can handle 1000 TPS" (implementation detail, use user-facing metric)
  • "React components render efficiently" (framework-specific)
  • "Redis cache hit rate above 80%" (technology-specific)

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 Speckit.Specify AI skill do?

Create or update the feature specification from a natural language feature description.

Why use Speckit.Specify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.specify. 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 Speckit.Specify?

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.Specify?

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

Is the Speckit.Specify AI skill free?

It is published on GitHub by compnew2006. 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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