Bitbucket Workflow logo

Bitbucket Workflow

Organization
Mindrally
bitbucket-workflow

Bitbucket best practices for pull requests, Pipelines CI/CD, Jira integration, and Atlassian ecosystem workflows

Overview

PublisherMindrally
Repositoryskills
Skill namebitbucket-workflow
Stars
259
Forks
41
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Bitbucket Workflow 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/Mindrally/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/bitbucket-workflow .claude/skills/bitbucket-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bitbucket Workflow 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 Bitbucket Workflow 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 Bitbucket Workflow 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.

Bitbucket Workflow Best Practices

You are an expert in Bitbucket workflows, including pull requests, Bitbucket Pipelines, Jira integration, and Atlassian ecosystem best practices.

Core Principles

  • Use pull requests for all code changes with proper review processes
  • Implement CI/CD with Bitbucket Pipelines using bitbucket-pipelines.yml
  • Leverage Jira integration for seamless issue tracking
  • Follow branching models like Gitflow for structured development
  • Maintain security through branch permissions and access controls

Pull Request Best Practices

Creating Effective Pull Requests

  1. Keep PRs focused and reviewable

    • One feature or fix per PR
    • Include context in the description
  2. PR Title Convention

    • Reference Jira issue: PROJ-123: Add user authentication
    • Use conventional format: feat: implement login page
  3. PR Description Template

    markdown
    ## Summary
    Brief description of changes and motivation.
    
    ## Jira Issue
    [PROJ-123](https://your-org.atlassian.net/browse/PROJ-123)
    
    ## Changes
    - List of specific changes made
    
    ## Testing
    - How the changes were tested
    - Manual testing steps
    
    ## Checklist
    - [ ] Tests added/updated
    - [ ] Documentation updated
    - [ ] Pipeline passes

Code Review in Bitbucket

  1. Add reviewers - Select appropriate team members
  2. Use tasks - Create tasks for actionable feedback
  3. Approve or request changes - Clear approval workflow
  4. Resolve discussions - Address all feedback before merge

Merge Strategies

  • Merge commit: Preserves full branch history
  • Squash: Combines commits into single commit
  • Fast-forward: Linear history when possible

Bitbucket Pipelines

Basic Pipeline Configuration

yaml
image: node:20

definitions:
  caches:
    npm: ~/.npm

  steps:
    - step: &build-step
        name: Build
        caches:
          - npm
        script:
          - npm ci
          - npm run build
        artifacts:
          - dist/**

    - step: &test-step
        name: Test
        caches:
          - npm
        script:
          - npm ci
          - npm test

pipelines:
  default:
    - step: *build-step
    - step: *test-step

  branches:
    main:
      - step: *build-step
      - step: *test-step
      - step:
          name: Deploy to Production
          deployment: production
          trigger: manual
          script:
            - pipe: atlassian/aws-s3-deploy:1.1.0
              variables:
                AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID
                AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
                AWS_DEFAULT_REGION: 'us-east-1'
                S3_BUCKET: 'my-bucket'
                LOCAL_PATH: 'dist'

    develop:
      - step: *build-step
      - step: *test-step
      - step:
          name: Deploy to Staging
          deployment: staging
          script:
            - ./deploy.sh staging

Pipeline Features

Parallel Steps
yaml
pipelines:
  default:
    - parallel:
        - step:
            name: Unit Tests
            script:
              - npm test:unit
        - step:
            name: Integration Tests
            script:
              - npm test:integration
        - step:
            name: Lint
            script:
              - npm run lint
Conditional Steps
yaml
pipelines:
  pull-requests:
    '**':
      - step:
          name: Build and Test
          script:
            - npm ci
            - npm test
          condition:
            changesets:
              includePaths:
                - "src/**"
                - "package.json"
Custom Pipes
yaml
pipelines:
  default:
    - step:
        name: Deploy
        script:
          - pipe: atlassian/aws-ecs-deploy:1.6.0
            variables:
              AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID
              AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
              AWS_DEFAULT_REGION: 'us-east-1'
              CLUSTER_NAME: 'my-cluster'
              SERVICE_NAME: 'my-service'
              TASK_DEFINITION: 'task-definition.json'

Services for Testing

yaml
definitions:
  services:
    postgres:
      image: postgres:15
      variables:
        POSTGRES_DB: test_db
        POSTGRES_USER: test_user
        POSTGRES_PASSWORD: test_pass
    redis:
      image: redis:7

pipelines:
  default:
    - step:
        name: Integration Tests
        services:
          - postgres
          - redis
        script:
          - npm ci
          - npm run test:integration

Caching

yaml
definitions:
  caches:
    npm: ~/.npm
    pip: ~/.cache/pip
    gradle: ~/.gradle/caches

pipelines:
  default:
    - step:
        caches:
          - npm
        script:
          - npm ci
          - npm run build

Jira Integration

Smart Commits

Enable smart commits to update Jira issues from commit messages:

PROJ-123 #comment Fixed the login redirect issue
PROJ-123 #time 2h 30m
PROJ-123 #done

Branch Naming

Include Jira issue key in branch names:

  • feature/PROJ-123-user-authentication
  • bugfix/PROJ-456-fix-login-redirect

This automatically links branches to issues.

Automation Rules

Set up Jira automation:

  • Move issue to "In Progress" when branch created
  • Move issue to "In Review" when PR opened
  • Move issue to "Done" when PR merged

Branching Models

Gitflow in Bitbucket

yaml
pipelines:
  branches:
    main:
      - step:
          name: Deploy Production
          deployment: production
          script:
            - ./deploy.sh production

    develop:
      - step:
          name: Deploy Staging
          deployment: staging
          script:
            - ./deploy.sh staging

    'release/*':
      - step:
          name: Release Build
          script:
            - npm run build:release

    'feature/*':
      - step:
          name: Feature Build and Test
          script:
            - npm ci
            - npm test

    'hotfix/*':
      - step:
          name: Hotfix Build
          script:
            - npm ci
            - npm test

Branch Permissions

Configure in Repository settings > Branch permissions:

Main branch:

  • No direct pushes
  • Require pull request
  • Minimum 1 approval
  • Require passing builds
  • Require all tasks resolved

Develop branch:

  • Require pull request
  • Minimum 1 approval
  • Require passing builds

Repository Management

Default Reviewers

Set up default reviewers for consistent code review:

  • Add team leads as default reviewers
  • Use CODEOWNERS-like patterns

Merge Checks

Enable merge checks:

  • Minimum approvals
  • No unresolved tasks
  • Passing builds
  • No changes requested

Access Levels

  • Admin: Full control
  • Write: Push and merge
  • Read: Clone and view

Security Best Practices

Repository Variables

Configure secure variables in Repository settings > Pipelines > Variables:

yaml
# Reference in pipeline
script:
  - echo "Deploying with token"
  - ./deploy.sh --token=$DEPLOY_TOKEN

Variable options:

  • Secured: Masked in logs
  • Required for deployment

IP Allowlisting

Restrict pipeline access to specific IP ranges for deployment environments.

Access Tokens

Use repository or project access tokens instead of personal tokens:

  • Scoped to specific repositories
  • Easier to rotate
  • Better audit trail

Deployment Environments

Environment Configuration

yaml
pipelines:
  branches:
    main:
      - step:
          name: Deploy to Production
          deployment: production
          script:
            - ./deploy.sh

Configure environments in Repository settings > Deployments:

  • Set environment variables per environment
  • Configure deployment permissions
  • View deployment history

Deployment Permissions

  • Require specific user approval for production
  • Set up deployment windows
  • Enable deployment freeze periods

Atlassian Ecosystem Integration

Confluence Integration

  • Link repositories to Confluence spaces
  • Embed code snippets
  • Auto-update documentation from commits

Trello Integration

  • Connect cards to commits
  • Automatic card movement on PR events

Opsgenie Integration

  • Trigger alerts from pipeline failures
  • On-call notifications for deployment issues

Best Practices Summary

  1. Use descriptive branch names with Jira keys
  2. Configure branch permissions for main branches
  3. Implement comprehensive pipelines with proper stages
  4. Use pipes for common tasks (AWS, Docker, etc.)
  5. Enable smart commits for Jira updates
  6. Set up deployment environments with proper permissions
  7. Use repository variables for secrets
  8. Configure merge checks for quality gates
  9. Leverage Atlassian integrations for seamless workflow

Frequently asked questions

What does the Bitbucket Workflow AI skill do?

Bitbucket best practices for pull requests, Pipelines CI/CD, Jira integration, and Atlassian ecosystem workflows

Why use Bitbucket Workflow on TypingMind?

Because you install it once and use it with any model. Bitbucket Workflow 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 Bitbucket Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mindrally/skills/tree/main/bitbucket-workflow. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bitbucket Workflow?

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 Bitbucket Workflow?

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

Is the Bitbucket Workflow AI skill free?

Yes. It is published on GitHub by Mindrally 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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