paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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
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