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Create User Story

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huytieu
create-user-story

Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)

Overview

Publisherhuytieu
RepositoryCOG-second-brain
Skill namecreate-user-story
Stars
1.2K
Forks
138
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Create User Story 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/huytieu/COG-second-brain.git /tmp/COG-second-brain
mkdir -p .claude/skills
cp -r /tmp/COG-second-brain/skills/create-user-story .claude/skills/create-user-story
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create User Story 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 Create User Story 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 Create User Story 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.

COG Create User Story Skill

When to Invoke

  • User wants to create a new user story, feature request, or issue
  • User says "create story", "new story", "add issue", "write user story", "create ticket"
  • User describes a feature or requirement that should be tracked

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If agent_mode: team — use parallel agents to check for duplicates across all active trackers simultaneously, then create the story
  • If agent_mode: solo — check duplicates and create the story sequentially in the main conversation

Command: /create-user-story

Pre-Flight Check

  1. Read 00-inbox/MY-INTEGRATIONS.md to determine which project trackers are active:

    • Linear — use Linear MCP tools
    • GitHub — use gh CLI
    • Jira — use jira CLI or Jira API via WebFetch
    • If NO tracker is active, save the story as a markdown file in 04-projects/[project]/stories/ and inform the user
  2. Read 00-inbox/MY-PROFILE.md to get:

    • Active projects (to determine which project/repo/board to target)
    • User's name (for story author attribution)
  3. Ask the user (if not already provided):

    • What is the feature or requirement? (free-form description)
    • Which project does this belong to? (if multiple active projects)
    • Priority level? (optional — default to medium)

Execution Strategy

Phase 1: Understand the Request

Parse the user's input to extract:

  • Feature description — what the user wants built
  • Target users — who benefits from this
  • Problem statement — what problem this solves
  • Project context — which project this belongs to

If the user provides a brief description, ask clarifying questions before proceeding:

  • Who is the target user for this feature?
  • What problem does this solve?
  • Are there any specific acceptance criteria you have in mind?

Phase 2: Duplicate Check

CRITICAL: Always check for duplicates before creating.

Team Mode (parallel agents)

Launch duplicate-check agents in parallel using the Task tool with run_in_background: true:

Agent: "duplicate-checker-linear" (if Linear is active)

Search Linear for potential duplicate issues.
1. Use ToolSearch to load Linear tools
2. Use mcp__claude_ai_Linear_2__list_issues to search for issues with similar keywords
3. Search across all active projects/teams
4. Return any issues that match by title similarity or description overlap

Search terms: [extracted keywords from user's description]
Return: list of potential duplicates with title, status, URL, and similarity assessment

Agent: "duplicate-checker-github" (if GitHub is active)

Search GitHub Issues for potential duplicates.
Repository: [CUSTOMIZE: your-org/your-repo]

1. gh search issues "[keywords]" --repo [CUSTOMIZE: your-org/your-repo] --json number,title,state,url,body --limit 20
2. Also search closed issues to check if this was already done:
   gh search issues "[keywords]" --repo [CUSTOMIZE: your-org/your-repo] --state closed --json number,title,state,url --limit 10

Return: list of potential duplicates with title, status, URL, and similarity assessment

Agent: "duplicate-checker-jira" (if Jira is active)

Search Jira for potential duplicate issues.
Project: [CUSTOMIZE: YOUR-PROJECT-KEY]

1. Use WebFetch or jira CLI to search:
   JQL: project = "[CUSTOMIZE: YOUR-PROJECT-KEY]" AND text ~ "[keywords]" ORDER BY created DESC
2. Check both open and recently resolved issues

Return: list of potential duplicates with key, title, status, URL, and similarity assessment
Solo Mode (sequential)

Run the same duplicate checks sequentially for whichever tracker is active.

Phase 3: Report Duplicates (if found)

If potential duplicates are found, present them to the user:

I found [N] potential duplicate(s):

1. **[TITLE]** ([STATUS]) — [URL]
   Similarity: [High/Medium/Low] — [reason]

2. **[TITLE]** ([STATUS]) — [URL]
   Similarity: [High/Medium/Low] — [reason]

Options:
a) These are different — proceed with creating the new story
b) This is a duplicate of #[N] — skip creation
c) This is related to #[N] — create and link them

Wait for user confirmation before proceeding.

Phase 4: Format the User Story

Generate the story in standard user story format:

markdown
## Title
[Concise, descriptive title]

## User Story
**As a** [type of user],
**I want** [goal/desire],
**So that** [benefit/value].

## Description
[Expanded description of the feature, including context and background]

## Acceptance Criteria

### Scenario 1: [Happy path scenario name]
- **Given** [precondition]
- **When** [action]
- **Then** [expected outcome]

### Scenario 2: [Alternative scenario name]
- **Given** [precondition]
- **When** [action]
- **Then** [expected outcome]

### Scenario 3: [Edge case or error scenario name]
- **Given** [precondition]
- **When** [action]
- **Then** [expected outcome]

## Technical Notes
[Any implementation hints, constraints, or dependencies — if applicable]

## Out of Scope
[What this story explicitly does NOT cover — helps prevent scope creep]

Present the formatted story to the user for review before creating it.

Phase 5: Create in Tracker

After user approves the story content:

Linear
1. Use ToolSearch to load Linear tools
2. Use mcp__claude_ai_Linear_2__get_team to find the target team
3. Use mcp__claude_ai_Linear_2__list_issue_labels to find appropriate labels
4. Use mcp__claude_ai_Linear_2__save_issue to create the issue with:
   - title: [story title]
   - description: [full story in markdown]
   - team: [target team]
   - priority: [user-specified or default medium]
   - labels: [appropriate labels]
GitHub Issues
gh issue create \
  --repo [CUSTOMIZE: your-org/your-repo] \
  --title "[story title]" \
  --body "[full story in markdown]" \
  --label "[appropriate labels]"
Jira
Use WebFetch to POST to Jira REST API or use jira CLI:
- Project: [CUSTOMIZE: YOUR-PROJECT-KEY]
- Issue Type: Story
- Summary: [story title]
- Description: [full story in markdown/Jira wiki format]
- Priority: [user-specified or default Medium]
- Labels: [appropriate labels]
Vault Fallback (no tracker active)

Save to 04-projects/[project]/stories/story-YYYY-MM-DD-[slug].md

Phase 6: Confirm and Link

  1. Confirm creation with the user:

    Story created: **[TITLE]**
    [URL or file path]
    Priority: [priority]
    Labels: [labels]
  2. If the user indicated this is related to another issue (Phase 3, option c), create a link/relation between them.

  3. Also save a local copy to 04-projects/[project]/stories/ for vault reference.


Fallback Behavior

ScenarioBehavior
No tracker activeSave as markdown in 04-projects/[project]/stories/
Tracker API failsSave locally, warn user, suggest manual creation with the formatted content
Duplicate check failsWarn user that duplicate check was skipped, proceed with creation after confirmation
User doesn't specify projectList active projects from MY-PROFILE.md and ask which one
User provides minimal inputAsk clarifying questions to build a complete story

Error Handling

  • API rate limits: Wait and retry once, then fall back to local save
  • Authentication failures: Inform user their integration may need re-authentication
  • Large search results: Limit duplicate check to top 10 most relevant matches
  • Context overflow: Summarize duplicate results rather than showing full descriptions

Frequently asked questions

What does the Create User Story AI skill do?

Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)

Why use Create User Story on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huytieu/COG-second-brain/tree/main/skills/create-user-story. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create User Story?

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 Create User Story?

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

Is the Create User Story AI skill free?

Yes. It is published on GitHub by huytieu 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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