Deliver Acceptance Criteria logo

Deliver Acceptance Criteria

Organization
product-on-purpose
deliver-acceptance-criteria

Generates structured Given/When/Then acceptance criteria for a user story or feature slice, covering the happy path, key failure scenarios, and non-functional expectations in testable form. Use when turning requirements into verifiable scenarios for engineering handoff and QA sign-off. For a dedicated catalog of boundary conditions, error states, and recovery paths across a feature, use deliver-edge-cases; to write the stories themselves, use deliver-user-stories.

Overview

Publisherproduct-on-purpose
Repositorypm-skills
Skill namedeliver-acceptance-criteria
Stars
717
Forks
92
Bundled files
5
LicenseApache-2.0
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.

  • 5 bundled files

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

  • Open source

    Published by product-on-purpose on GitHub. Read the source before you install it.

Installation

Install the Deliver Acceptance 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.
    https://github.com/product-on-purpose/pm-skills/tree/main/skills/deliver-acceptance-criteria
  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/product-on-purpose/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/skills/deliver-acceptance-criteria .claude/skills/deliver-acceptance-criteria
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deliver Acceptance 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 Deliver Acceptance 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 Deliver Acceptance 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.

Acceptance Criteria

Acceptance criteria define the observable behavior that must be true for a story or feature to be considered done. This skill turns feature context into concise, testable Given/When/Then scenarios that engineers and QA can verify without guessing intent.

When to Use

  • After a user story, PRD section, or feature slice is defined
  • When a team needs clear pass/fail conditions for implementation
  • When writing QA-ready criteria for sprint planning or handoff
  • When a story has edge cases, error paths, or non-functional expectations that should be explicit

When NOT to Use

  • You need the user stories themselves -> use deliver-user-stories; this skill deepens a story that already exists
  • You need systematic failure coverage across a whole feature -> use deliver-edge-cases; this skill stays story-scoped
  • There is no story or slice to bind criteria to yet -> use deliver-prd or deliver-user-stories first
  • You are defining success metrics for an experiment, not done-ness for a story -> use measure-experiment-design

Instructions

When asked to create acceptance criteria, follow these steps:

  1. Confirm the story or feature scope Identify the exact slice of work. If the scope is unclear, ask for the user story, PRD section, or feature description before drafting criteria.

  2. Separate the happy path from exceptions Start with the primary success flow, then add edge cases and error states that are likely or costly if missed.

  3. Write each criterion as an observable scenario Use Given/When/Then language only. Keep each criterion independently testable and avoid implementation details.

  4. Cover recovery and failure behavior Describe what the user sees or can do when validation fails, a dependency is unavailable, or a save action cannot complete.

  5. Include non-functional expectations Add criteria for performance, accessibility, security, reliability, or auditability when they matter to the story.

  6. Avoid duplication and overlap Each criterion should test one outcome. If two criteria describe the same behavior, merge or split them until the intent is clear.

  7. Review for testability Ensure a reviewer can pass or fail each criterion without interpretation. If a statement is subjective, rewrite it into a measurable outcome.

Output Contract

Use references/TEMPLATE.md as the output format. A complete response should:

  • Restate the feature or story context
  • Group criteria into happy path, edge cases, error states, and non-functional criteria
  • Use explicit Given/When/Then statements for each criterion
  • Note assumptions or open questions when context is incomplete

Quality Checklist

Before finalizing, verify:

  • The criteria map to a specific story or feature slice
  • The happy path is covered first
  • Edge cases are explicit, not implied
  • Error states include user-visible recovery behavior
  • Non-functional criteria are included when relevant
  • Each criterion is testable and has one clear outcome
  • No implementation details leak into the acceptance criteria

Examples

See references/EXAMPLE.md for a completed example based on a realistic e-commerce checkout flow.

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 Deliver Acceptance Criteria AI skill do?

Generates structured Given/When/Then acceptance criteria for a user story or feature slice, covering the happy path, key failure scenarios, and non-functional expectations in testable form. Use when turning requirements into verifiable scenarios for engineering handoff and QA sign-off. For a dedicated catalog of boundary conditions, error states, and recovery paths across a feature, use deliver-edge-cases; to write the stories themselves, use deliver-user-stories.

Why use Deliver Acceptance Criteria on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/product-on-purpose/pm-skills/tree/main/skills/deliver-acceptance-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 Deliver Acceptance 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 Deliver Acceptance Criteria?

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

Is the Deliver Acceptance Criteria AI skill free?

Yes. It is published on GitHub by product-on-purpose under the Apache-2.0 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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