User Story Splitting logo

User Story Splitting

Organization
getcrew44
user-story-splitting

Break a large story or epic into smaller deliverable stories using proven split patterns. Use when backlog items are too big for estimation, sequencing, or independent release.

Overview

Publishergetcrew44
Repositorycrew44
Skill nameuser-story-splitting
Stars
358
Forks
12
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by getcrew44 on GitHub. Read the source before you install it.

Installation

Install the User Story Splitting 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/getcrew44/crew44/tree/main/daemon/internal/presets/defaultcrew/skills/product/user-story-splitting
  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/getcrew44/crew44.git /tmp/crew44
mkdir -p .claude/skills
cp -r /tmp/crew44/daemon/internal/presets/defaultcrew/skills/product/user-story-splitting .claude/skills/user-story-splitting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User Story Splitting 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 User Story Splitting 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 User Story Splitting 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.

Purpose

Break down large user stories, epics, or features into smaller, independently deliverable stories using systematic splitting patterns. Use this to make work more manageable, reduce risk, enable faster feedback cycles, and maintain flow in agile development. This skill applies to user stories, epics, and any work that's too large to complete in a single sprint.

This is not arbitrary slicing—it's strategic decomposition that preserves user value while reducing complexity.

Key Concepts

The Story Splitting Framework

Based on Richard Lawrence and Peter Green's "Humanizing Work Guide to Splitting User Stories," the framework provides 8 systematic patterns for splitting work:

  1. Workflow steps: Split along sequential steps in a user's journey
  2. Business rule variations: Split by different rule scenarios (permissions, calculations, etc.)
  3. Data variations: Split by different data types or inputs
  4. Acceptance criteria complexity: Split when multiple "When" or "Then" statements exist
  5. Major effort: Split by technical milestones or implementation phases
  6. External dependencies: Split along dependency boundaries (APIs, third parties, etc.)
  7. DevOps steps: Split by deployment or infrastructure requirements
  8. Tiny Acts of Discovery (TADs): When none of the above apply, use small experiments to unpack unknowns

Why Split Stories?

  • Faster feedback: Smaller stories ship sooner, allowing earlier validation
  • Reduced risk: Less to build = less that can go wrong
  • Better estimation: Small stories are easier to estimate accurately
  • Maintain flow: Keeps work moving through the sprint without bottlenecks
  • Testability: Smaller scope = easier to write and run tests

Anti-Patterns (What This Is NOT)

  • Not horizontal slicing: Don't split into "front-end story" and "back-end story" (each story should deliver user value)
  • Not task decomposition: Stories aren't tasks ("Set up database," "Write API")
  • Not arbitrary chopping: Don't split "Add user management" into "Add user" and "Management" (meaningless)

When to Use This

  • Story is too large for a single sprint
  • Multiple "When" or "Then" statements in acceptance criteria
  • Epic needs to be broken down into deliverable increments
  • Team can't reach consensus on story size or scope
  • Story has multiple personas or workflows bundled together

When NOT to Use This

  • Story is already small and well-scoped (don't over-split)
  • Splitting would create dependencies that slow delivery
  • The story is a technical task (use engineering task breakdown instead)

Application

Step 1: Identify the Original Story

Start with the story/epic/feature that needs splitting. Ensure it's written using the user story format (reference skills/user-story/SKILL.md or skills/epic-hypothesis/SKILL.md).

markdown
### Original Story:
[Story formatted with use case and acceptance criteria]

Step 2: Apply the Splitting Logic

Use template.md for the full fill-in structure and output format.

Work through the 8 splitting patterns in order. Stop when you find one that applies.

Pattern 1: Workflow Steps

Ask: Does this story contain multiple sequential steps?

Example:

  • Original: "As a user, I want to sign up, verify my email, and complete onboarding"
  • Split:
    1. "As a user, I want to sign up with email/password"
    2. "As a user, I want to verify my email via a confirmation link"
    3. "As a user, I want to complete onboarding by answering 3 profile questions"

Pattern 2: Business Rule Variations

Ask: Does this story have different rules for different scenarios?

Example:

  • Original: "As a user, I want to apply discounts (10% for members, 20% for VIPs, 5% for first-time buyers)"
  • Split:
    1. "As a member, I want to apply a 10% discount at checkout"
    2. "As a VIP, I want to apply a 20% discount at checkout"
    3. "As a first-time buyer, I want to apply a 5% discount at checkout"

Pattern 3: Data Variations

Ask: Does this story handle different types of data or inputs?

Example:

  • Original: "As a user, I want to upload files (images, PDFs, videos)"
  • Split:
    1. "As a user, I want to upload image files (JPG, PNG)"
    2. "As a user, I want to upload PDF documents"
    3. "As a user, I want to upload video files (MP4, MOV)"

Pattern 4: Acceptance Criteria Complexity

Ask: Does this story have multiple "When" or "Then" statements?

Example:

  • Original: "As a user, I want to manage my cart"
    • When I add an item, Then it appears in my cart
    • When I remove an item, Then it disappears from my cart
    • When I update quantity, Then the cart total updates
  • Split:
    1. "As a user, I want to add items to my cart so I can purchase them later"
    2. "As a user, I want to remove items from my cart so I can change my mind"
    3. "As a user, I want to update item quantities so I can buy the right amount"

Note: This is the most common indicator that a story needs splitting. If you see multiple "When/Then" pairs, split along those boundaries.


Pattern 5: Major Effort

Ask: Does this story require significant technical work that can be delivered incrementally?

Example:

  • Original: "As a user, I want real-time collaboration on documents"
  • Split:
    1. "As a user, I want to see who else is viewing the document (read-only presence)"
    2. "As a user, I want to see live cursor positions of other editors"
    3. "As a user, I want to see live edits from other users in real-time"

Pattern 6: External Dependencies

Ask: Does this story depend on multiple external systems or APIs?

Example:

  • Original: "As a user, I want to log in with Google, Facebook, or Twitter"
  • Split:
    1. "As a user, I want to log in with Google OAuth"
    2. "As a user, I want to log in with Facebook OAuth"
    3. "As a user, I want to log in with Twitter OAuth"

Pattern 7: DevOps Steps

Ask: Does this story require complex deployment, infrastructure, or operational work?

Example:

  • Original: "As a user, I want to upload large files to cloud storage"
  • Split:
    1. "As a user, I want to upload small files (<10MB) to cloud storage"
    2. "As a user, I want to upload large files (10MB-1GB) with progress tracking"
    3. "As a user, I want to resume interrupted uploads"

Pattern 8: Tiny Acts of Discovery (TADs)

Ask: If none of the above apply, are there unknowns or assumptions that need unpacking?

Example:

  • Original: "As a user, I want AI-powered recommendations" (too vague, too many unknowns)
  • TADs:
    1. Prototype 3 recommendation algorithms and test with 10 users
    2. Define success criteria (click-through rate, user satisfaction)
    3. Build the simplest recommendation engine (collaborative filtering)
    4. Measure and iterate

Note: TADs aren't stories—they're experiments. Use them to de-risk and clarify before writing stories.


Step 3: Write the Split Stories

For each split, write a complete user story using the format from skills/user-story/SKILL.md:

markdown
### Split 1 using [Pattern Name]:

#### User Story [ID]:
- **Summary:** [Brief title]

**Use Case:**
- **As a** [persona]
- **I want to** [action]
- **so that** [outcome]

**Acceptance Criteria:**
- **Scenario:** [Description]
- **Given:** [Preconditions]
- **When:** [Action]
- **Then:** [Outcome]

Step 4: Validate the Splits

Ask these questions:

  1. Does each split deliver user value? (Not just "front-end done")
  2. Can each split be developed independently? (No hard dependencies)
  3. Can each split be tested independently? (Clear acceptance criteria)
  4. Is each split small enough for a sprint? (1-5 days of work)
  5. Do the splits, when combined, equal the original? (Nothing lost in translation)

If any answer is "no," revise.


Examples

See examples/sample.md for full splitting examples.

Mini example excerpt:

markdown
### Original Story:
As a team admin, I want to manage team members so that I can control access.

### Suggested Splits (Acceptance Criteria Complexity):
1. Invite new team members
2. Remove team members
3. Update team member roles

Common Pitfalls

Pitfall 1: Horizontal Slicing (Technical Layers)

Symptom: "Story 1: Build the API. Story 2: Build the UI."

Consequence: Neither story delivers user value independently.

Fix: Split vertically—each story should include front-end + back-end work to deliver a complete user-facing capability.


Pitfall 2: Over-Splitting

Symptom: "Story 1: Add button. Story 2: Wire button to API. Story 3: Display result."

Consequence: Creates unnecessary overhead and dependencies.

Fix: Only split when the story is too large. A 2-day story doesn't need splitting.


Pitfall 3: Meaningless Splits

Symptom: "Story 1: First half of feature. Story 2: Second half of feature."

Consequence: Arbitrary splits that don't map to user value or workflow.

Fix: Use one of the 8 splitting patterns—each split should have a clear rationale.


Pitfall 4: Creating Hard Dependencies

Symptom: "Story 2 can't start until Story 1 is 100% done, tested, and deployed."

Consequence: No parallelization, slows delivery.

Fix: Split in a way that allows independent development. If dependencies are unavoidable, prioritize Story 1.


Pitfall 5: Ignoring the "So That"

Symptom: Split stories have the same "so that" statement.

Consequence: You've split the action but not the outcome—likely a task decomposition, not a story split.

Fix: Ensure each split has a distinct user outcome. If not, reconsider the split pattern.


References

Related Skills

  • skills/user-story/SKILL.md — Format for writing the split stories
  • skills/epic-hypothesis/SKILL.md — Epics often need splitting before becoming stories
  • skills/jobs-to-be-done/SKILL.md — Helps identify meaningful splits along user jobs

External Frameworks

  • Richard Lawrence & Peter Green, The Humanizing Work Guide to Splitting User Stories — Origin of the 8 splitting patterns
  • Bill Wake, INVEST in Good Stories (2003) — Criteria for well-formed stories (Independent, Negotiable, Valuable, Estimable, Small, Testable)
  • Mike Cohn, User Stories Applied (2004) — Story decomposition techniques

Dean's Work

  • User Story Splitting Prompt Template (based on Humanizing Work framework)

Provenance

  • Adapted from prompts/user-story-splitting-prompt-template.md in the https://github.com/deanpeters/product-manager-prompts repo.

Skill type: Component Suggested filename: user-story-splitting.md Suggested placement: /skills/components/ Dependencies: References skills/user-story/SKILL.md, skills/epic-hypothesis/SKILL.md Applies to: User stories, epics, and any work that's too large to complete in a single sprint

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 User Story Splitting AI skill do?

Break a large story or epic into smaller deliverable stories using proven split patterns. Use when backlog items are too big for estimation, sequencing, or independent release.

Why use User Story Splitting on TypingMind?

Because you install it once and use it with any model. User Story Splitting 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 User Story Splitting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getcrew44/crew44/tree/main/daemon/internal/presets/defaultcrew/skills/product/user-story-splitting. 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 User Story Splitting?

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 User Story Splitting?

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

Is the User Story Splitting AI skill free?

Yes. It is published on GitHub by getcrew44 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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