Requirement Quality logo

Requirement Quality

Community
techygarg
requirement-quality

Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).

Overview

Publishertechygarg
Repositorylattice
Skill namerequirement-quality
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 Quality 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-quality
  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-quality .claude/skills/requirement-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirement Quality 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 Quality 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 Quality 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 Quality

Config Resolution

Skill supports project-specific standards. Order:

  1. Look for .lattice/config.yaml in the repo root
  2. If found, check paths.requirement_standards for a custom document path
  3. If a custom document exists at that path, read it and check its YAML frontmatter for mode:
    • mode: override: the custom document has full precedence. Use it instead of the embedded defaults. It must be comprehensive — treat it as the sole reference.
    • mode: overlay (or no mode field): read the embedded ./references/defaults.md first, then apply the custom document's sections on top. A custom section replaces the matching default section (matched by exact heading); new sections append after the defaults.
  4. If a custom path is configured but no document exists at it → tell the user which configured path is missing, then fall back to ./references/defaults.md
  5. If there is no config file or no paths.requirement_standards key → read ./references/defaults.md

Custom standards produced by requirement-forge-refiner → consumed by this atom → composed by requirement-forge molecule.

Self-Validation Checklist

STOP: Before writing any feature file, verify ALL checks. If a check clearly fails → fix before writing. If judgment call (see Ambiguity Signals) → flag and surface options.

If validating an existing spec (not generating), same checks apply — "fix before writing" means "fix before marking approved." Present findings as a quality report with severity.

Draft vs approved enforcement: For status: draft — items 1, 2, 10 are required. Items 3–9, 11, 12 are advisories: flag findings but do not block write. For status: approved — all items required, no exceptions.

  1. PROBLEM STATEMENT: Names a specific user need or pain — not a solution in disguise, not a vague improvement? Identifies WHO has the problem (specific user type or role, not "users")?
  2. SCOPE: Has explicit out-of-scope items — not just in-scope?
  3. BOUNDARY CONDITIONS: Feature-wide edge cases, system limits, and constraints documented?
  4. ASSUMPTIONS: Statements the team proceeds with as true are explicit — not buried in ACs or unstated? If an assumption proves wrong, affected scenarios are identifiable?
  5. SCENARIO NAMES: Each scenario has a verb-phrase name (sentence case) that describes the situation — not a feature name, not an AC?
  6. AC FORMAT: Each AC follows the agreed format (default: Given/When/Then)? Each has a clear pass/fail condition — a tester can write an automated check without asking a clarifying question?
  7. FAILURE COVERAGE: At least one scenario covers a failure, error, or edge case?
  8. SCENARIO COUNT: Feature has no more than the agreed max (default: 5) scenarios? If at or over → challenge whether this is one feature or two.
  9. AC COUNT: Each scenario has no more than the agreed max (default: 6) ACs? If at or over → challenge whether this scenario is too broad.
  10. INDEPENDENCE: Feature is self-contained — no unresolved external unknowns required before design-blueprint can begin? Any unresolved Open Questions affecting scope, behavior, or ACs are blockers unless each is marked non-blocking with a stated reason.
  11. IMPLEMENTATION NOTES: Slices ordered chronologically, at the "what" level — no technical implementation specifics?
  12. COHERENCE: All scenarios address the same user need in the Problem Statement? If a scenario serves a different need, it belongs in a separate feature.

Project-specific checks: if loaded doc contains a validation checklist section, apply those after base checklist.

When all checks pass: output "Spec passes requirement-quality — ready for write." (pre-write mode) or "Spec passes requirement-quality — status: approved." (validation mode).

Active Anti-Pattern Scan

STOP: After checklist, scan for these. If found → fix or challenge before writing.

  • Solution as problem: Problem statement says "we need X" instead of "users cannot do Y" → ask what user need X addresses; rewrite around the need
  • Vague problem: "improve the experience", "make it faster", "better UX" → no verifiable outcome; push for specific, observable user impact
  • Persona-less problem: Problem statement says "users" without identifying which user type or role → push for specificity; different personas produce different ACs
  • Hidden assumption: AC or scenario relies on an unstated assumption ("assumes user is logged in" but no Assumptions section records this) → make the assumption explicit or add a scenario covering the case where it doesn't hold
  • Boundaryless scope: scope section lists only what is in scope, nothing explicitly out → force at least 3 explicit exclusions; undefined scope = infinite scope
  • Happy-path-only spec: every scenario is a success path, no failure or error scenario → add at least one failure scenario before feature is complete
  • AC sprawl: single scenario accumulates 7+ ACs → scenario too broad; propose split into two named scenarios
  • Scenario sprawl: feature has 6+ scenarios → feature may be two; pause and challenge scope before adding more
  • Vague AC: "the system should handle errors gracefully", "response should be fast", "it should work correctly" → no pass/fail condition; rewrite as concrete Given/When/Then
  • Implementation AC: AC specifies a technical approach ("system shall use Redis", "shall call the /api/v2 endpoint") → rewrite as observable outcome
  • Orphaned feature: depends_on is empty but feature references another feature's data or behavior in its scenarios → flag missing dependency
  • Cross-epic feature undocumented: feature scenarios reference behaviors from a different epic but no cross-epic dependency is recorded → place feature in its primary epic, add the other epic as a cross-reference, record the dependency in depends_on frontmatter
  • Technical task as feature: feature name or problem statement describes infrastructure, tooling, or engineering work ("Set up database schema", "Configure CI/CD", "Write unit tests") → redirect to implementation layer, challenge what user need it serves
  • Wrong granularity — too fine: feature is actually a single acceptance criterion or a micro-behavior ("Show error on wrong password") → merge into a larger feature representing the complete user-facing behavior
  • Wrong granularity — too coarse: feature encompasses an entire product area with 10+ implicit behaviors ("User management") → decompose into discrete independently-implementable features
  • Generic slices: Implementation notes use placeholder labels that apply to any feature ("Core functionality", "Error handling", "Edge cases") → each slice must name the specific behavior being built for THIS feature
  • Undefined domain terms: Scenarios use specialized/domain terms not defined anywhere in the spec → add inline definitions or a Glossary section

Ambiguity Signals

Flag these — present options and reasoning. If framework:collaborative-judgment is loaded, use it to structure the presentation.

  • Feature boundary: two related behaviors — one feature or two? If Behavior A requires knowing Behavior B's design to spec its own ACs, they are one feature.
  • Scenario granularity: one scenario with more ACs, or two separate scenarios? If both situations share the same precondition and trigger, group. If they differ in either → separate scenarios.
  • Priority: feature serves multiple user types or epics with different urgency → surface for product decision; do not silently assign.
  • Independence borderline: feature depends on another feature but the dependency is well-understood and stable → judgment call whether to mark dependent or proceed as independent.
  • Assumption vs. requirement: a statement reads as both an assumption and a requirement → surface for product decision.

See ./references/defaults.md for epic/feature/scenario definitions, AC format examples, priority notation, status workflow, naming conventions, and implementation slice guidance.

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 Quality AI skill do?

Apply requirement quality principles when generating or validating feature specifications. Enforces feature completeness, scenario structure, AC verifiability, feature independence, and implementation slice quality. Use when writing feature specs, validating existing requirements, or when the user mentions 'validate this spec', 'check this feature', 'requirement quality', 'is this spec complete', or 'requirement-quality'. This skill governs the craft of writing individual feature specifications — not technical design (see design-blueprint), not implementation (see code-forge).

Why use Requirement Quality on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techygarg/lattice/tree/main/skills/requirement-quality. 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 Quality?

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 Quality?

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

Is the Requirement Quality 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇