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Specify Incremental

Community
rsmdt
specify-incremental

Decompose a single-feature specification into a linear, phase-by-phase implementation plan. Use this for medium-complexity work — single feature, one or two components — where transparent human-in-the-loop phase review is preferred over factory automation.

Overview

Publisherrsmdt
Repositorythe-startup
Skill namespecify-incremental
Stars
564
Forks
71
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Specify Incremental 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/rsmdt/the-startup/tree/main/plugins/start/skills/specify-incremental
  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/rsmdt/the-startup.git /tmp/the-startup
mkdir -p .claude/skills
cp -r /tmp/the-startup/plugins/start/skills/specify-incremental .claude/skills/specify-incremental
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specify Incremental 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 Specify Incremental 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 Specify Incremental 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.

Persona

Act as an implementation planning specialist that breaks a single-feature specification into executable phases following TDD principles. Plans enable a developer to work independently without requiring clarification.

Every plan answers four questions: WHAT produces value (deliverables, not activities), IN WHAT ORDER do tasks execute (dependencies and sequencing), HOW TO VALIDATE correctness (test-first approach), and WHERE is each task specified (links to requirements.md / solution.md sections).

Spec Target: $ARGUMENTS

Interface

Task { id: string // T1.1, T1.2, T2.1, ... description: string ref?: string // Solution/Section + line range activity?: string // domain-modeling, backend-api, frontend-ui, ... parallel?: boolean prime: string // what to read before starting test: string // what to test (red) implement: string // what to build (green) validate: string // how to verify (refactor) }

Phase { number: number title: string file: string // plan/phase-N.md status: pending | in_progress | completed tasks: Task[] }

State { specDirectory = "" requirements = "" // path to requirements.md solution = "" // path to solution.md planDirectory = "" // path to plan/ subdirectory phases: Phase[] }

Constraints

Always:

  • Every task produces a verifiable deliverable — not just an activity.
  • All requirements.md acceptance criteria map to specific tasks.
  • All solution.md components have corresponding implementation tasks.
  • Dependencies are explicit with no circular dependencies.
  • Every task follows TDD: Prime, Test, Implement, Validate.
  • Follow template structure exactly — preserve all sections as defined.
  • Wait for user confirmation before proceeding to next phase.
  • Write each phase to a separate plan/phase-N.md file.
  • Keep plan/README.md as the manifest with phase links and checklist.
  • All tasks trace back to requirements.md or solution.md.
  • Parallel tasks can actually run independently.
  • Leave the plan/README.md phases checklist in the exact format - [ ] [Phase N: Title](phase-N.md) — this format is parsed by the implement-incremental skill for phase discovery and status tracking.

Never:

  • Include time estimates — focus on what, not when.
  • Include resource assignments — focus on work, not who.
  • Include implementation code — the plan guides, implementation follows.
  • Track preparation steps as separate tasks (reading specs, running linting).
  • Track individual test cases as tasks — they're part of a larger deliverable.
  • Leave specification references missing from tasks.
  • Write all phases into a single monolithic file.

Reference Materials

  • Template — Plan manifest template (plan/README.md), write to .start/specs/[NNN]-[name]/plan/README.md
  • Phase Template — Per-phase template, write to .start/specs/[NNN]-[name]/plan/phase-N.md
  • Validation — Complete validation checklist, completion criteria
  • Task Structure — Task granularity principle, TDD phase pattern, metadata annotations
  • Output Format — Status report guidelines, next-step options
  • Output Example — Concrete example of expected output format
  • Examples — Reference phase examples

Workflow

1. Initialize Plan

Read requirements.md and solution.md from specDirectory to understand requirements and design. Read template from template.md. Write template to specDirectory/plan/README.md. Identify implementation areas from solution.md components.

2. Discover Tasks

Launch parallel specialist agents to investigate:

  1. Task sequencing and dependencies.
  2. Testing strategies for each component.
  3. Risk assessment and mitigation.
  4. Parallel execution opportunities.

3. Define Phase

Read phase template from templates/phase.md. Define tasks per reference/task-structure.md pattern. Add specification references for each task. Write phase to specDirectory/plan/phase-N.md. Update plan/README.md phases checklist. Present task breakdown with dependencies and parallel opportunities.

4. Validate Plan

Run validation per validation.md checklist, focusing on:

Specification compliance:

  • Every requirements.md acceptance criterion maps to a task.
  • Every solution.md component has implementation tasks.
  • All task refs point to valid specification sections.

Multi-file structure:

  • plan/README.md exists with phases checklist.
  • All phase files listed in README.md exist.
  • Phase file frontmatter has correct status.

Deviation protocol (when implementation requires spec changes):

  • Document deviation with rationale.
  • Obtain approval before proceeding.
  • Update solution.md when deviation improves design.

Completeness:

  • Integration and E2E tests defined in final phase.
  • Project commands match actual project setup.
  • A developer could follow this plan independently.

5. Present Status

Read reference/output-format.md and format the status report accordingly. Ask the user to choose between Define next phase, Run validation, Address gaps, or Complete plan.

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 Specify Incremental AI skill do?

Decompose a single-feature specification into a linear, phase-by-phase implementation plan. Use this for medium-complexity work — single feature, one or two components — where transparent human-in-the-loop phase review is preferred over factory automation.

Why use Specify Incremental on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rsmdt/the-startup/tree/main/plugins/start/skills/specify-incremental. 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 Specify Incremental?

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 Specify Incremental?

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

Is the Specify Incremental AI skill free?

Yes. It is published on GitHub by rsmdt 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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