Documentation Criteria logo

Documentation Criteria

Community
shinpr
documentation-criteria

Determines which of PRD, ADR, UI Spec, Design Doc, and Work Plan a change requires, and where each is stored. Use when deciding documentation scope, or when creating or reviewing a technical document.

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namedocumentation-criteria
Stars
682
Forks
103
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Documentation Criteria 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.
  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/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-skills/skills/documentation-criteria .claude/skills/documentation-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Documentation Criteria 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 Documentation Criteria 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 Documentation Criteria 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.

Documentation Creation Criteria

This file holds the routing decision: which documents a change requires and where they live. What to write inside one is defined by its template, linked from Storage Locations.

What Each Document Fixes

Each document fixes one class of decision that the repository alone cannot supply. An unfilled section becomes a guess made later by the consumer named below, with no record of what was assumed.

  • PRD — Fixes the business outcome and the acceptance criteria later work traces to. Its AC IDs are the traceability keys that the Design Doc, UI Spec, and test selection reuse; without them each consumer re-derives requirements from prose and the link between a test and the value it protects is lost. Implementation details belong to the Design Doc, selection rationale to an ADR, phases and task breakdown to the Work Plan.

  • ADR — Records one durable technical choice and the options it beat, so later work can tell a deliberate decision from an accident. Without it a future change either re-runs the same comparison or silently reverses it. Accepted records the currently selected means, not an obligation to retain it: when later evidence supports a smaller sufficient choice, update or supersede the decision while the confirmed outcome, desired-future requirements, and non-goals remain true. End-to-end implementation design belongs to the Design Doc, schedule and repository tasks to the Work Plan.

  • UI Spec — Records screen structure, transitions, component/state contracts, and visual acceptance before components exist, so decomposition is decided before implementation instead of per-component during it. Create one when those decisions remain open; reuse an approved UI Spec or go straight to the Design Doc when one evident repository-supported pattern already determines them. Technical implementation and API contracts belong to the Design Doc.

  • Design Doc — Records the complete implementation design for the confirmed scope: flows, contracts, change impact, and verification strategy. Task execution treats it as the primary technical baseline, so implementation does not silently invent missing How. When repository evidence invalidates technical How while confirmed outcome, desired-future requirements, and non-goals remain true, correct the implementation and the affected technical artifact through their owning workflow without reopening product requirements. Technology selection rationale belongs to an ADR, schedule and assignments to the Work Plan.

  • Work Plan — Fixes task order, dependencies, executable verification, and the earliest vertical proof point. Without it task order follows file layout rather than dependency, and integration risk moves to the end of the work. Design detail is referenced from the Design Doc rather than restated.

Creation Decision Matrix

Structural ScaleBase DocumentsCreation Order
SmallNoneDirect implementation
MediumDesign Doc → Work PlanStart with Design Doc
LargePRD → Design Doc → Work PlanContinue after PRD approval

Build one path in this order:

  1. Select the base path from Structural Scale.
  2. Insert an applicable UI Spec immediately before the Design Doc.
  3. One or more qualifying ADR decision points insert an ADR batch immediately before the Design Doc. A qualifying decision point sets the scale floor to Medium.

Structural Scale

Classify the decision burden, not repository layout. File count is supporting evidence only.

ScaleStructural condition
SmallOne coherent outcome has one evident repository-supported implementation within one responsibility boundary and no unresolved durable choice
MediumOne coherent outcome coordinates across a boundary or requires investigation of a potentially durable choice
LargeMultiple independently valuable outcomes require separate design decisions

A qualifying ADR decision point sets the floor at Medium because it creates a durable decision. One coherent outcome remains Medium when it crosses multiple layers; Large requires independently valuable outcomes with separate design decisions.

ADR Decision Filters

Apply the Choice filter, then the Durability filter, to each technical topic inside the confirmed implementation scope. Apply them independently from Structural Scale, and check existing ADRs first.

  1. Choice requires judgment — current requirements, accepted decisions, and representative repository evidence support at least two credible, materially distinct options whose selection requires comparison.
  2. Decision is durable — choosing among those options materially changes responsibility, dependency direction, a shared contract, persistence, a technology dependency, reversibility, or lifecycle cost that future work must preserve or understand.

Create one ADR for each topic that passes both filters, and review the complete batch together. Treat choices as one decision point when they must be selected or reconsidered together; separate independently revisitable choices.

Qualifying durable choices include:

  • introducing or replacing a technology, library, platform, storage model, or external dependency;
  • changing ownership, dependency direction, a trust boundary, or a shared public contract when credible alternatives exist;
  • replacing an accepted architecture decision with a different durable choice that passes both filters;
  • choosing an irreversible or high-cost-to-reverse data or compatibility strategy.

A local contract, data-flow, state, or component change belongs in the Design Doc when it follows an accepted design, has one evident repository-supported implementation, or remains cheaply reversible. Counts of files, consumers, nesting levels, states, steps, and asynchronous operations are supporting evidence rather than ADR criteria. Only the qualifying decisions above create ADRs; generic technical concerns, operational possibilities, and rejected activities can only support that determination.

Storage Locations

DocumentPathNaming ConventionTemplate
PRDdocs/prd/[feature-name]-prd.mdprd-template.md
ADRdocs/adr/ADR-[4-digits]-[title].mdadr-template.md
UI Specdocs/ui-spec/[feature-name]-ui-spec.mdui-spec-template.md
UI Spec Assetsdocs/ui-spec/assets/{feature-name}/Prototype code files-
Design Docdocs/design/[feature-name]-design.mddesign-template.md
Work Plandocs/plans/YYYYMMDD-{type}-{description}.mdplan-template.md
Task Filedocs/plans/tasks/{plan-name}-task-{number}.mdtask-template.md

*Note: Work plans are excluded by .gitignore

References

Each template defines the content, structural elements, and diagram criteria for its document: prd-template.md, adr-template.md, ui-spec-template.md, design-template.md, plan-template.md, task-template.md

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 Documentation Criteria AI skill do?

Determines which of PRD, ADR, UI Spec, Design Doc, and Work Plan a change requires, and where each is stored. Use when deciding documentation scope, or when creating or reviewing a technical document.

Why use Documentation Criteria on TypingMind?

Because you install it once and use it with any model. Documentation Criteria 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 Documentation Criteria in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-skills/skills/documentation-criteria. 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 Documentation Criteria?

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 Documentation Criteria?

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

Is the Documentation Criteria AI skill free?

Yes. It is published on GitHub by shinpr 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 👇