---

name: bitbucket-automation compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent bitbucket automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: bitbucket-automation, bitbucket automation, how do i bitbucket-automation, orchestrate bitbucket-automation, automate bitbucket-automation, agent bitbucket-automation archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Bitbucket Automation

Orchestrates intelligent skill selection and execution for bitbucket automation workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

import requests
from typing import Dict, List, Optional
import time

BITBUCKET_API_BASE = "https://api.bitbucket.org/2.0"

def create_or_update_pr(
    workspace: str,
    repo_slug: str,
    source_branch: str,
    target_branch: str,
    title: str,
    description: str,
    bitbucket_auth: Dict[str, str]
) -> Dict:
    """Orchestrates Bitbucket PR creation/update with domain-specific fallback logic.
    
    Implements Law 1 (Early Exit) and Law 4 (Fail Fast) for API interactions.
    Handles merge conflicts and pipeline waits as domain-specific fallbacks.
    """
    # Law 1: Early exit on invalid inputs
    if not all([workspace, repo_slug, source_branch, target_branch, title]):
        raise ValueError("Missing required Bitbucket parameters")
        
    headers = {
        "Authorization": f"Bearer {bitbucket_auth.get('token')}",
        "Content-Type": "application/json"
    }
    
    # Law 2: Parse/validate state before mutation
    pr_payload = {
        "title": title,
        "description": description,
        "source": {"branch": {"name": source_branch}},
        "destination": {"branch": {"name": target_branch}}
    }
    
    # Check for existing PR to avoid duplicates (Law 3: Atomic Predictability)
    existing_prs = requests.get(
        f"{BITBUCKET_API_BASE}/repositories/{workspace}/{repo_slug}/pullrequests",
        headers=headers,
        params={"state": "OPEN", "source": source_branch}
    ).json()
    
    if existing_prs.get("values"):
        pr_id = existing_prs["values"][0]["id"]
        update_url = f"{BITBUCKET_API_BASE}/repositories/{workspace}/{repo_slug}/pullrequests/{pr_id}"
        requests.put(update_url, headers=headers, json=pr_payload)
        return {"action": "updated", "pr_id": pr_id, "url": existing_prs["values"][0]["links"]["html"]["href"]}
    
    # Create new PR with domain-specific retry logic
    max_retries = 3
    for attempt in range(max_retries):
        try:
            response = requests.post(
                f"{BITBUCKET_API_BASE}/repositories/{workspace}/{repo_slug}/pullrequests",
                headers=headers,
                json=pr_payload
            )
            response.raise_for_status()
            pr_data = response.json()
            
            # Law 4: Fail fast on merge conflicts
            if pr_data.get("merge_conflicts"):
                raise MergeConflictError("Target branch has unresolvable conflicts")
                
            return {
                "action": "created",
                "pr_id": pr_data["id"],
                "url": pr_data["links"]["html"]["href"],
                "pipeline_status": "pending"
            }
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429:
                time.sleep(2 ** attempt)  # Exponential backoff
                continue
            raise
            
    raise RuntimeError("Failed to create PR after retries")

Pattern 2: Execution with Fallback

def sync_branch_and_wait_for_pipelines(
    workspace: str,
    repo_slug: str,
    branch_name: str,
    commit_sha: str,
    bitbucket_auth: Dict[str, str],
    timeout_minutes: int = 30
) -> Dict:
    """Automates branch sync and pipeline monitoring for Bitbucket repositories.
    
    Demonstrates domain-specific fallback: if pipeline fails, triggers manual review flag.
    """
    headers = {"Authorization": f"Bearer {bitbucket_auth.get('token')}"}
    pipeline_url = f"{BITBUCKET_API_BASE}/repositories/{workspace}/{repo_slug}/pipelines"
    
    # Push commit if needed
    if commit_sha:
        requests.post(
            f"{BITBUCKET_API_BASE}/repositories/{workspace}/{repo_slug}/refs/branches/{branch_name}",
            headers=headers,
            json={"name": branch_name, "target": {"hash": commit_sha}}
        )
        
    # Monitor pipeline status with domain-specific fallback
    start_time = time.time()
    while time.time() - start_time < timeout_minutes * 60:
        pipelines = requests.get(pipeline_url, headers=headers, params={"branch": branch_name}).json()
        if not pipelines.get("values"):
            break
            
        current_pipeline = pipelines["values"][0]
        status = current_pipeline["state"]["name"]
        
        if status == "COMPLETED":
            if current_pipeline["result"] == "SUCCESSFUL":
                return {"status": "passed", "pipeline_id": current_pipeline["uuid"]}
            else:
                # Domain fallback: flag for manual review instead of silent failure
                return {
                    "status": "failed",
                    "pipeline_id": current_pipeline["uuid"],
                    "fallback_action": "trigger_manual_review",
                    "error_details": current_pipeline.get("error", {})
                }
        elif status in ("STOPPED", "ERROR"):
            return {"status": "terminated", "pipeline_id": current_pipeline["uuid"]}
            
        time.sleep(15)  # Poll interval
        
    return {"status": "timeout", "fallback_action": "notify_team_channel"}

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

---

Constraints

MUST DO

• Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions
• Validate all trigger conditions with explicit allowlists before executing automated actions
• Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability
• Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging

MUST NOT DO

• Do not create circular automation loops where trigger A causes action B which triggers A again
• Avoid using automations that modify production data without explicit human approval gates
• Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation
• Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues

Related Skills

| Skill | Purpose | |