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Requirement Forge Refiner

Community
techygarg
requirement-forge-refiner

Facilitate a structured conversation to define requirement standards for a project — epic and feature definitions, scenario structure, AC format, priority notation, status workflow, and naming conventions. Produces a formal requirement-standards.md that the requirement-quality atom reads via config resolution, customising its embedded defaults for the team's product process. Use when setting up a new project, defining product standards, or when the user says 'set up requirement standards', 'define feature standards', 'configure requirement forge', 'define how features should be structured', or 'requirement forge refiner'.

Overview

Publishertechygarg
Repositorylattice
Skill namerequirement-forge-refiner
Stars
208
Forks
16
Bundled files
1
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.

  • 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 techygarg on GitHub. Read the source before you install it.

Installation

Install the Requirement Forge Refiner 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/techygarg/lattice/tree/main/skills/requirement-forge-refiner
  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/techygarg/lattice.git /tmp/lattice
mkdir -p .claude/skills
cp -r /tmp/lattice/skills/requirement-forge-refiner .claude/skills/requirement-forge-refiner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirement Forge Refiner 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 Requirement Forge Refiner 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 Requirement Forge Refiner 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.

Requirement Forge Refiner

What This Produces

  • Output: .lattice/standards/requirement-standards.md (or custom path from .lattice/config.yaml → paths.requirement_standards)
  • Two modes:
    • Overlay (mode: overlay): A slim document containing only sections that differ from the built-in defaults. The requirement-quality atom reads its embedded defaults.md first, then applies this document's sections on top. This is the expected common case.
    • Override (mode: override): A comprehensive standalone document that fully replaces the atom's embedded defaults. For teams whose product process differs fundamentally from the defaults.
  • Default mode: Overlay — produces only what the team wants to change
  • Config key: paths.requirement_standards in .lattice/config.yaml
  • Consumed by: requirement-quality atom (via config resolution) → requirement-forge molecule (composes the atom)
  • Template: Read ./assets/template.md for the full document structure, default content, and interview guidance comments

Scope Clarification

This refiner defines how requirements are structured and expressed for this project. It does not define:

  • What to build (that is the requirement-forge molecule's job)
  • Architecture or technical design (that is the architecture-refiner's job)
  • Domain modeling patterns (that is the ddd-refiner's job)

The standards produced here answer: what is an epic, what is a feature, what is a scenario, how are ACs written, how are features named and prioritized. These are the rules the requirement-quality atom enforces — the molecule composes the atom and inherits those rules automatically.

Before You Begin

Check for an existing standards document

  1. Read .lattice/config.yaml — check paths.requirement_standards.
  2. If the path exists, read that file. Ask the user:
    • "You already have a requirement standards document. Would you like to revise it (update specific sections), start fresh (new interview), or add to it (add new sections)?"
    • Revise: load the existing document, walk through only the sections the user wants to change, update in place.
    • Start fresh: proceed with the full interview flow below.
    • Add to it: skip to the "New Sections" part of the interview.
  3. If no config or no existing document, proceed with the full interview.

Ask two orienting questions first

Before the formal interview, ask:

  1. "Does your team already have a way of writing requirements — any existing PRDs, Confluence templates, or Jira conventions I should be aware of?"
  2. "Is there a specific product domain or terminology I should know before we define the standards?"

These two questions are the only free-form listening before the structured interview begins. Synthesize what you hear and carry it forward — do not ask follow-up questions at this stage.

Choosing the Mode

Present the three options:

"How would you like to define your requirement standards?

  1. Customize specific sections (overlay) — Keep the built-in defaults and change only what differs for your project. This produces a slim document. Most teams choose this.
  2. Define everything from scratch (override) — Walk through all sections and produce a comprehensive standalone document.
  3. Add project-specific sections only (overlay with additions) — Keep all defaults as-is and add new sections, such as domain terminology or custom status workflows.

The built-in defaults cover standard product spec practices well. Option 1 is recommended unless your team's conventions are fundamentally different."

Map the choice:

  • Options 1 and 3 → mode: overlay
  • Option 2 → mode: override

Facilitation Approach

Conversation style

  • One section at a time. Do not present all questions at once. Walk through the template sequentially.
  • Defaults-first. For each section, briefly summarize the default, then ask if it matches. Do not read defaults verbatim — summarize key points and ask.
  • Propose, don't just ask. When the user's answer is ambiguous, propose the most reasonable interpretation and ask them to confirm or correct. "It sounds like you want MoSCoW priorities — so 'Must', 'Should', 'Could', 'Won't'. Is that right?"
  • Record decisions, not discussion. The output document reads as a specification. "We discussed X and decided Y" is wrong. "Y" is right.
  • Challenge weak definitions. If the user defines a "feature" so broadly it would encompass an entire epic, push back: "That scope sounds like an epic — a feature should be independently designable in one design-blueprint session. Can we tighten the definition?"

For overlay mode

This should be fast. Many sections will be "keep as-is."

  1. Present each section's default in 2–3 sentences.
  2. Ask: "Does this match your project, or would you like to change it?"
  3. If matches → skip it (section will NOT appear in the output).
  4. If changes wanted → discuss specifics, record the changes.
  5. After all sections, ask: "Anything to add that isn't covered — domain-specific terminology, custom fields, team conventions?"
  6. Only changed or added sections appear in the output document.

For override mode

Every section gets attention and appears in the output.

  1. Walk through every section in full detail.
  2. User confirms, modifies, or replaces each section.
  3. All sections appear in the output.

Common scenarios

  • "I agree with everything" → No custom document needed. "The embedded defaults are already active. No custom document is needed — requirement-forge will use the defaults automatically."
  • "We use MoSCoW priorities" → Overlay §6 only.
  • "We call them 'use cases' not 'scenarios'" → Overlay §4 (rename + update any description that references "scenario").
  • "We have a longer status workflow" → Overlay §7.
  • "We have domain-specific terms that should be consistent" → Overlay with additions — add §10 Domain Terminology.
  • "Our features tend to be larger" → Overlay §2 and §4 — adjust the size signals and max scenario count.

Section-by-Section Interview Guide

Read ./assets/template.md and follow the <!-- INTERVIEW GUIDANCE: --> comments for each section. Those comments contain specific questions, probing questions, and what is customizable vs. fixed.

Cross-section dependency table

Decision inAffectsHow
§2 — Feature size definition§4 scenario countLarger features tolerate more scenarios; tighter features need a lower cap
§4 — Scenario nomenclature§8 naming conventionsIf "scenario" is renamed, naming conventions must use the new term
§4 — Max scenarios per feature§2 feature definitionThese two must be consistent — the split signal in §2 should align with the cap in §4
§5 — AC format§4 scenario structureAC format determines what each scenario's criteria look like
§6 — Priority notationfeature file frontmatterPriority field format used in every generated feature file
§7 — Status workflowfeature file frontmatterStatus field used in every generated feature file
§8 — Naming conventionsall file generationFeature file names and display names generated by the molecule

When a dependency is triggered, inform the user: "Since you changed [X], we should also review [Y] — it's affected by that decision."

Overlay-specific section flow

For each of the 9 default sections:

  1. Summarize the section's key points in 2–3 sentences.
  2. Ask: "Does this match your project?"
  3. Yes → Move to the next section. This section will not appear in the output.
  4. No → Dive into the details using the template guidance. Produce the user's version.
  5. After all 9 sections, ask about additions.

Override-specific section flow

For each of the 9 default sections:

  1. Present the section's full content.
  2. Ask: "Does this work as-is, or would you like to modify it?"
  3. As-is → Include the default content unchanged.
  4. Modify → Discuss changes, produce the modified version.
  5. All sections go in the output.

Output Assembly

For overlay mode

  1. YAML frontmatter: mode: overlay
  2. Overlay preamble (from template)
  3. Table of contents listing only included sections
  4. Only the sections the user changed or added — each must be self-contained and complete
  5. Section headings must match template.md exactly (the molecule matches sections by heading)
  6. New sections (§10+) included after the default sections
  7. Footer with project name, date, mode

For override mode

  1. YAML frontmatter: mode: override
  2. Override preamble (from template)
  3. Full table of contents (all 9+ sections)
  4. All sections: defaults for unchanged, user's version for changed, new sections at end
  5. Footer with project name, date, mode

For both modes

Strip all <!-- INTERVIEW GUIDANCE: --> comments from the output. The final document is a clean specification.

Determine output path:

  1. If .lattice/config.yaml exists and has paths.requirement_standards, use that path.
  2. Otherwise, default to .lattice/standards/requirement-standards.md.

Write the document:

  1. Create .lattice/standards/ directory (and .lattice/ parent) if it does not exist.
  2. Write the document to the determined path.

Update config:

  1. If .lattice/config.yaml does not exist, create it with:
    yaml
    paths:
      requirement_standards: .lattice/standards/requirement-standards.md
  2. If .lattice/config.yaml exists but has no paths.requirement_standards, add the key. Preserve all existing content.
  3. If the key already exists, no config change needed.

Confirm to user: "Your requirement standards have been written to [PATH] in [overlay|override] mode. The requirement-forge molecule will now use these standards and will not re-ask structural questions covered here."

Document Quality Checks

Before writing the final document, verify:

Overlay mode checks

  • Each included section is self-contained and complete (not a diff or partial section)
  • Section headings match template.md exactly
  • No <!-- INTERVIEW GUIDANCE: --> comments remain
  • Frontmatter has mode: overlay
  • Only changed or added sections are included

Override mode checks

  • All 9 default sections are present (plus any additions)
  • §2 feature size signal is consistent with §4 max scenario count
  • §4 scenario nomenclature is consistent with §8 naming conventions
  • §5 AC format is consistent with how §4 describes scenario criteria
  • §6 priority values and §7 status values match the frontmatter field descriptions in §8
  • No <!-- INTERVIEW GUIDANCE: --> comments remain
  • Frontmatter has mode: override
  • Document is readable as a standalone specification

Both modes

  • Frontmatter is valid YAML with correct mode value
  • Document is well-formatted markdown
  • Config file (.lattice/config.yaml) is correctly updated
  • Output path exists and is writable

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 Requirement Forge Refiner AI skill do?

Facilitate a structured conversation to define requirement standards for a project — epic and feature definitions, scenario structure, AC format, priority notation, status workflow, and naming conventions. Produces a formal requirement-standards.md that the requirement-quality atom reads via config resolution, customising its embedded defaults for the team's product process. Use when setting up a new project, defining product standards, or when the user says 'set up requirement standards', 'define feature standards', 'configure requirement forge', 'define how features should be structured',...

Why use Requirement Forge Refiner on TypingMind?

Because you install it once and use it with any model. Requirement Forge Refiner 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 Requirement Forge Refiner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techygarg/lattice/tree/main/skills/requirement-forge-refiner. 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 Requirement Forge Refiner?

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 Requirement Forge Refiner?

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

Is the Requirement Forge Refiner AI skill free?

Yes. It is published on GitHub by techygarg 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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