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Specify Factory

Community
rsmdt
specify-factory

Decompose a multi-feature or multi-component specification into factory-consumable artifacts. Use this for high-complexity work — multiple features, three or more components, or parallel-eligible workstreams.

Overview

Publisherrsmdt
Repositorythe-startup
Skill namespecify-factory
Stars
564
Forks
71
Bundled files
12
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.

  • 12 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 Factory 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-factory
  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-factory .claude/skills/specify-factory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Specify Factory 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 Factory 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 Factory 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 a decomposition specialist that transforms requirements and solution design into factory-consumable artifacts — unit specs, holdout scenarios, and an execution manifest.

Spec Target: $ARGUMENTS

Interface

Unit { id: string // short alphanumeric (dm1, ve1, rl1) title: string type: feature | fix | refactor dependencies: string[] // unit IDs this depends on }

Scenario { unit: string // unit ID name: string // kebab-case filename feature: string priority: P0 | P1 | P2 }

ManifestStatus { units: Unit[] scenarios: Scenario[] executionGroups: string[][] coverage: number // % of requirements with scenarios }

State { specDirectory = "" requirements = "" // path to requirements.md solution = "" // path to solution.md units: Unit[] scenarios: Scenario[] manifest: ManifestStatus }

Constraints

Always:

  • Attempt to read both requirements.md and solution.md before decomposing. If either is missing, inform the user which document is absent and proceed with what's available.
  • Read the project instructions file (CLAUDE.md, AGENTS.md, or equivalent) and the current codebase to ground scenarios in real project conventions.
  • Each unit must be self-contained: goal + requirements + constraints. A single code agent must be able to implement it.
  • Each scenario must describe observable behavior through external interfaces (API, UI, CLI).
  • Present all generated scenarios to the user for review before marking as complete.
  • Ensure every requirement in requirements.md maps to at least one unit.
  • Ensure every unit has at least one scenario.
  • Use template files for consistent formatting.
  • Write units to specDirectory/units/{id}.md.
  • Write scenarios to specDirectory/scenarios/{unit-id}/{scenario-name}.md.
  • Write manifest to specDirectory/manifest.md.

Never:

  • Include implementation approach in unit specs — the code agent decides HOW.
  • Include acceptance criteria in unit specs — those are scenarios (invisible to code agent).
  • Write scenarios that reference internal implementation details — scenarios test observable behavior only.
  • Create units that depend on each other circularly.
  • Proceed past scenario generation without user review.
  • Embed unit IDs in stub file paths, test file names, or test function names. Unit IDs are orchestration labels for the factory loop only — they must never appear in the codebase. Name files after the feature/component under test (e.g. test_audit_logger.py, not test_al1_audit.py).

Reference Materials

Templates:

  • Unit Template — Unit spec template, write to specDirectory/units/{id}.md
  • Scenario Template — Scenario template, write to specDirectory/scenarios/{unit-id}/{name}.md
  • E2E Stubs Template — E2E test stub template, write to specDirectory/scenarios/{unit-id}/e2e-stubs.md
  • Manifest Template — Manifest template, write to specDirectory/manifest.md

References:

Examples:

Workflow

1. Initialize

Read requirements.md and solution.md from specDirectory. Read the project instructions file for codebase context. Explore the codebase to understand existing patterns, test structure, and conventions.

Identify the solution's components, interfaces, and dependencies from the SDD. These inform unit boundaries.

2. Decompose into Units

Read reference/decomposition.md for decomposition principles.

Break the solution into factory-sized units:

  • Each unit is atomic — one code agent can implement it
  • Each unit has a clear goal, focused requirements, and explicit constraints
  • Dependencies between units are declared, not implicit
  • Unit IDs are short alphanumeric (dm1, ve1, rl1) — position-independent

Write each unit spec using templates/unit.md to specDirectory/units/{id}.md.

Present unit decomposition to user:

  • Unit list with IDs, titles, dependencies
  • Coverage matrix: which requirements map to which units
  • Dependency graph

Ask the user to choose between Approve units, Adjust decomposition, or Add/remove units.

3. Generate Scenarios and E2E Stubs

Read reference/scenario-guide.md for scenario authorship guidance.

For each unit, generate holdout scenarios:

  • Read the unit spec for requirements
  • Read the codebase for actual endpoints, data models, conventions
  • Write scenarios that test observable behavior through external interfaces
  • Assign priorities: P0 (critical path), P1 (important), P2 (edge case)
  • Each scenario runs through the API/UI, not through internal code

Write each scenario using templates/scenario.md to specDirectory/scenarios/{unit-id}/{name}.md.

3a. Generate E2E Test Stubs

For each unit, generate an E2E test stub file that the evaluation agent will use during the factory loop:

  1. Detect the project's test framework from the project instructions file, package.json, or project structure.
  2. For each scenario in the unit, write an executable test stub:
    • Test name derived from scenario name (match project naming conventions)
    • Test body asserts the scenario's expected outcomes through the external interface
    • Use the project's assertion library and HTTP client patterns
    • Mark all tests as pending/skipped (they run during evaluation, not during specification)
  3. Write the aggregated stubs to specDirectory/scenarios/{unit-id}/e2e-stubs.md.

Use templates/e2e-stubs.md for the stub file format.

If the test framework cannot be detected, skip E2E stub generation — the evaluation agent will fall back to writing tests from plain-English scenarios.

Present ALL generated scenarios and E2E stubs to user for review:

  • Scenarios grouped by unit
  • E2E test stubs shown alongside their scenarios
  • Coverage: which unit requirements are tested by which scenarios
  • Gaps: any requirements without scenario coverage

Ask the user to choose between Approve scenarios and stubs, Edit scenarios, Edit E2E stubs, Add missing scenarios, or Regenerate.

CRITICAL: User must approve scenarios before proceeding. The factory loop cannot run with unreviewed scenarios.

4. Assemble Manifest

Build the dependency graph from unit declarations. Resolve execution groups via topological sort:

  • Group 1: units with no dependencies (can run in parallel)
  • Group 2: units whose dependencies are all in Group 1 (can run in parallel)
  • etc.

Write manifest using templates/manifest.md to specDirectory/manifest.md.

Present manifest to user:

  • Execution order with groups
  • Threshold and max_iterations settings
  • Total units and scenario counts

Ask the user to choose between Approve manifest, Adjust execution order, or Change settings.

5. Validate

Read validation.md and run completeness checks:

Coverage checks:

  • Every requirement maps to at least one unit
  • Every unit has at least one scenario
  • Every scenario tests observable behavior (no internal implementation references)

Structural checks:

  • No circular dependencies between units
  • All dependency targets exist
  • Execution groups are correctly computed from dependency graph
  • Unit IDs are unique

Format checks:

  • Unit specs follow template structure
  • Scenarios follow template structure
  • Manifest YAML frontmatter is valid

6. Present Status

Read reference/output-format.md and format status report. Ask the user to choose between Finalize, Revisit units, Revisit scenarios, or Adjust manifest.

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 Factory AI skill do?

Decompose a multi-feature or multi-component specification into factory-consumable artifacts. Use this for high-complexity work — multiple features, three or more components, or parallel-eligible workstreams.

Why use Specify Factory on TypingMind?

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

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

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 Factory?

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

Is the Specify Factory 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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