---

name: bitbucket-api description: Integrates with Bitbucket Cloud and Bitbucket Data Center REST APIs via atlassian-python-api to manage repositories, pull requests, pipelines, webhooks, and workspace settings. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: bitbucket api, atlassian-python-api, bitbucket cloud, bitbucket pipelines, pull request bitbucket, bitbucket webhooks, manage bitbucket repos, bitbucket data center archetypes:

• tactical
• generation anti_triggers:
• brainstorming
• vague ideation
• code golf
• over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational role: implementation scope: implementation output-format: code content-types:
• code
• guidance
• do-dont
• examples related-skills: coding-github-api, coding-gitlab-api, coding-jenkins-api

---

Bitbucket API & atlassian-python-api Integration

Integrates with Bitbucket Cloud (API v2) and Bitbucket Server/Data Center REST APIs using the atlassian-python-api library to manage repositories, pull requests, pipelines, branch restrictions, webhooks, and workspace administration.

TL;DR for Code Generation

• Use Cloud(username, password) for Bitbucket Cloud or Bitbucket(url, username, password) for Bitbucket Server/Data Center
• Bitbucket Cloud provides an object-oriented API (cloud.workspaces.each(), workspace.repositories.each())
• Bitbucket Server uses a functional API (bitbucket.project_list(), bitbucket.get_repo())
• Authenticate with app passwords for Cloud (not your main account password)
• Pass sort="-created_on" to pipeline list calls to get recent builds first
• Handle HTTPError from the requests library for API error responses

---

When to Use

Use this skill when:

• Automating repository creation, project administration, or branch permission management
• Building pull request workflows with automatic merge, approval, or CI triggers
• Querying Bitbucket Pipelines status, logs, and deployment results
• Managing webhooks for repository events (push, PR, pipeline status)
• Administering workspace or project-level user permissions and group access
• Creating or updating branch restrictions and default reviewers

---

When NOT to Use

Avoid this skill for:

• Git clone/push/fetch operations (use gitpython or raw git CLI)
• Full CI/CD pipeline YAML authoring (use the Bitbucket Pipelines editor or bitbucket-pipelines.yml)
• Jira or Confluence operations (use the same atlassian-python-api but with Jira or Confluence classes)

---

Core Workflow

1. Authenticate — Cloud vs. Server — For Cloud, use Cloud(url="https://api.bitbucket.org", username, password) with an app password. For Server, use Bitbucket(url, username, password). Checkpoint: List workspaces or projects to confirm connectivity.

2. Resolve Workspace and Repository — For Cloud: workspace = cloud.workspaces.get(slug) then repo = workspace.repositories.get(slug). For Server: bitbucket.get_repo(project_key, repo_slug). Checkpoint: Verify the repository name matches and the uuid or id is present.

3. Execute Operation — Call the appropriate method: create a pull request, trigger a pipeline, add a webhook, or modify branch restrictions. Checkpoint: Confirm the response includes the expected object ID or URL.

4. Handle Pagination — Methods returning generators (.each()) handle pagination automatically. For raw REST, pass pagelen=100 and handle the next URL from responses. Checkpoint: Ensure all pages are consumed — Cloud defaults to 50 items per page.

5. Error Handling — Catch requests.exceptions.HTTPError and inspect the status code. 401=bad auth, 403=insufficient permissions, 404=not found. Checkpoint: Log the response body for 400-level errors to see validation messages.

---

Implementation Patterns

Pattern 1: Bitbucket Cloud — Pull Request Management

import os
from atlassian.bitbucket import Cloud
from requests.exceptions import HTTPError


def create_pull_request(
    workspace_slug: str,
    repo_slug: str,
    source_branch: str,
    destination_branch: str,
    title: str,
    description: str = "",
    close_source_branch: bool = True,
) -> dict:
    """Create a pull request in Bitbucket Cloud.

    Args:
        workspace_slug: Workspace identifier (e.g., "my-company").
        repo_slug: Repository name (e.g., "backend-api").
        source_branch: Feature branch to merge from.
        destination_branch: Target branch to merge into.
        title: Pull request title.
        description: Pull request description in Markdown.
        close_source_branch: Auto-close source branch after merge.

    Returns:
        Dict with PR ID, links, and state.

    Raises:
        RuntimeError: If authentication fails or the PR cannot be created.
    """
    cloud = Cloud(
        url="https://api.bitbucket.org",
        username=os.environ["BITBUCKET_USERNAME"],
        password=os.environ["BITBUCKET_APP_PASSWORD"],
    )

    try:
        workspace = cloud.workspaces.get(workspace_slug)
        repo = workspace.repositories.get(repo_slug)
    except HTTPError as exc:
        status = exc.response.status_code
        if status == 404:
            raise RuntimeError(
                f"Repository '{workspace_slug}/{repo_slug}' not found."
            ) from exc
        raise RuntimeError(
            f"Failed to access Bitbucket resources: HTTP {status}"
        ) from exc

    try:
        pr = repo.pullrequests.create(
            title=title,
            description=description,
            source={"branch": {"name": source_branch}},
            destination={"branch": {"name": destination_branch}},
            close_source_branch=close_source_branch,
        )
    except HTTPError as exc:
        body = exc.response.text
        raise RuntimeError(
            f"Pull request creation failed: HTTP {exc.response.status_code} — {body}"
        ) from exc

    return {
        "id": pr.id,
        "title": pr.title,
        "state": pr.state,
        "links": {"html": pr.links["html"]["href"]},
    }

Pattern 2: Bitbucket Pipelines — Trigger and Monitor

import time
import os
from atlassian.bitbucket import Cloud
from requests.exceptions import HTTPError


def trigger_pipeline_and_get_result(
    workspace_slug: str,
    repo_slug: str,
    branch: str = "main",
    poll_interval: int = 15,
    timeout: int = 900,
) -> dict:
    """Trigger a Bitbucket Pipeline and wait for completion.

    Args:
        workspace_slug: Workspace identifier.
        repo_slug: Repository name.
        branch: Branch to run the pipeline on.
        poll_interval: Seconds between status checks.
        timeout: Maximum seconds to wait.

    Returns:
        Dict with pipeline UUID, state, and result.

    Raises:
        TimeoutError: If the pipeline doesn't complete in time.
    """
    cloud = Cloud(
        url="https://api.bitbucket.org",
        username=os.environ["BITBUCKET_USERNAME"],
        password=os.environ["BITBUCKET_APP_PASSWORD"],
    )
    repo = cloud.workspaces.get(workspace_slug).repositories.get(repo_slug)

    # Trigger the pipeline for the given branch
    pipeline = repo.pipelines.create(
        body={
            "target": {
                "type": "pipeline_ref_target",
                "ref_type": "branch",
                "ref_name": branch,
            }
        }
    )
    pipeline_uuid = pipeline.uuid

    # Poll for completion
    start = time.monotonic()
    while True:
        if time.monotonic() - start > timeout:
            raise TimeoutError(
                f"Pipeline {pipeline_uuid} did not complete within {timeout}s."
            )
        time.sleep(poll_interval)
        # Re-fetch the pipeline object to get updated state
        for p in repo.pipelines.each(sort="-created_on"):
            if p.uuid == pipeline_uuid:
                state = p.state
                if state["type"] != "PIPELINE_STATE_IN_PROGRESS":
                    return {
                        "uuid": pipeline_uuid,
                        "state": state["type"],
                        "result": state.get("result", {}).get("type"),
                        "duration_seconds": p.duration_in_seconds,
                    }
                break

Pattern 3: Bitbucket Server — Project and Repository Admin

from atlassian import Bitbucket
import os


def create_project_with_repo(
    project_key: str,
    project_name: str,
    repo_name: str,
    is_private: bool = True,
) -> dict:
    """Create a Bitbucket Server project and repository.

    Args:
        project_key: Short project key (e.g., "OPS").
        project_name: Human-readable project name.
        repo_name: Repository slug (kebab-case).
        is_private: Whether the repo should be private.

    Returns:
        Dict with project and repository keys.
    """
    bitbucket = Bitbucket(
        url=os.environ["BITBUCKET_SERVER_URL"],
        username=os.environ["BITBUCKET_USERNAME"],
        password=os.environ["BITBUCKET_PASSWORD"],
    )

    # Create the project
    try:
        project = bitbucket.create_project(project_key, project_name)
    except HTTPError as exc:
        if exc.response.status_code == 409:
            project = bitbucket.project(project_key)
        else:
            raise

    # Create the repository within the project
    try:
        repo = bitbucket.create_repo(
            project_key=project_key,
            repo=repo_name,
            is_private=is_private,
        )
    except HTTPError as exc:
        if exc.response.status_code == 409:
            repo = bitbucket.get_repo(project_key, repo_name)
        else:
            raise

    return {
        "project_key": project["key"],
        "repo_name": repo["slug"],
        "clone_url": repo["links"]["clone"][0]["href"],
    }

BAD vs GOOD: Pipeline Status Polling

# ❌ BAD — no timeout, tight loop, no error handling
def poll_pipeline_bad(repo, uuid):
    while True:
        for p in repo.pipelines.each():
            if p.uuid == uuid:
                if p.state["type"] != "PIPELINE_STATE_IN_PROGRESS":
                    return p.state
        time.sleep(1)  # Too frequent, may hit rate limits

# ✅ GOOD — timeout, exponential backoff, error handling
import time

def poll_pipeline_good(
    repo,
    pipeline_uuid: str,
    timeout: int = 600,
) -> dict:
    """Poll a Bitbucket pipeline until completion with timeout.

    Uses exponential backoff to avoid rate limiting.
    """
    start = time.monotonic()
    backoff = 5

    while time.monotonic() - start < timeout:
        try:
            for p in repo.pipelines.each(sort="-created_on"):
                if p.uuid == pipeline_uuid:
                    state_type = p.state["type"]
                    if state_type != "PIPELINE_STATE_IN_PROGRESS":
                        return {
                            "uuid": pipeline_uuid,
                            "state": state_type,
                            "result": p.state.get("result", {}).get("type"),
                            "duration": p.duration_in_seconds,
                        }
                    break
        except HTTPError as exc:
            if exc.response.status_code == 429:
                time.sleep(backoff)
                backoff = min(backoff * 2, 60)
                continue
            raise
        time.sleep(10)  # Normal poll interval

    raise TimeoutError(f"Pipeline {pipeline_uuid} did not complete in {timeout}s.")

MUST DO

• Use app passwords for Bitbucket Cloud authentication (Settings → Access Management → App Passwords)
• Grant app passwords only the scopes needed (repo, pull request, pipeline)
• Use the object-oriented Cloud API (Cloud class) for Bitbucket Cloud and the functional API (Bitbucket class) for Server/DC
• Pass sort="-created_on" when listing pipelines to get the most recent first
• Set a reasonable timeout (>= 600s) when polling pipeline results
• Check 409 Conflict responses to handle idempotent resource creation

MUST NOT DO

• Never use your main Bitbucket password for API access — always use app passwords
• Do not hardcode workspace or repository slugs in application code
• Avoid polling pipelines without backoff — Bitbucket rate-limits at 1000 requests/hour
• Never log or print pipeline variables or authentication tokens
• Do not assume pagination is complete without checking the next page link

---

Constraints

MUST DO

• Implement structured error responses with consistent format: {error_code, message, details, request_id}
• Add rate limiting per client/API key with configurable burst and sustained limits using a token bucket algorithm
• Validate all incoming requests against a schema before processing — reject malformed input with clear error messages
• Include correlation/request IDs in all log entries for end-to-end request tracing across service boundaries

MUST NOT DO

• Do not expose internal implementation details, stack traces, or database queries in error responses
• Avoid accepting unbounded request bodies — set maximum payload sizes and timeout limits
• Never trust client-supplied authentication tokens without validation (signature verification, expiration check)
• Do not log request/response bodies containing PII, API keys, or other sensitive data

Live References

• atlassian-python-api Documentation
• atlassian-python-api GitHub Repository
• Bitbucket Cloud REST API v2
• Bitbucket Server/Data Center REST API
• Bitbucket Cloud Pipelines API
• Bitbucket App Passwords Documentation
• Bitbucket Webhook Events Reference

Related Skills

Skill	Purpose
coding-github-api	GitHub API with PyGithub for repository and PR automation
coding-gitlab-api	GitLab API with python-gitlab for CI/CD and MR management
coding-jenkins-api	Jenkins automation for cross-platform CI/CD orchestration