bitbucket-automation

# 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 ```python 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 ```python 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 | |