architecture-compass
Architectural thinking partner for an existing repository — scans the codebase, conducts a structured interview, agrees on…
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
Claude Code
$ npx -y skills add techygarg/lattice --skill requirement-forge-refiner --agent claude-code
Codex
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a codex -y
Cursor
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a cursor -y
Gemini CLI
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a gemini-cli -y
GitHub Copilot
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a github-copilot -y
OpenCode
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a opencode -y
Windsurf
$ npx skills add techygarg/lattice --skill requirement-forge-refiner -a windsurf -y
name: requirement-forge-refiner description: "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'."
This refiner defines how requirements are *structured and expressed* for this project. It does not define:
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.
1. Read `.lattice/config.yaml` — check `paths.requirement_standards`. 2. If the path exists, read that file. Ask the user:
3. If no config or no existing document, proceed with the full interview.
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.
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:
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.
Every section gets attention and appears in the output.
1. Walk through every section
Composable AI skills that teach assistants structured thinking — design-first, context-aware, and architecture-guided.
Architectural thinking partner for an existing repository — scans the codebase, conducts a structured interview, agrees on…
Facilitate a structured conversation to define architecture principles for a repository. Supports multiple architecture styles:…
Enforce architectural rules when generating or modifying code, and validate proposed designs before approval (design mode).…
Investigate, reproduce, and safely fix a bug with regression protection. Composes context, diagnosis, architecture, code quality,…
Facilitate a structured conversation to define clean code principles for a repository. Produces a formal clean-code.md document…
Apply clean code principles when generating or modifying implementation code. Enforces function focus, naming clarity, complexity…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.