---

name: airflow-dag-patterns compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent airflow dag patterns 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: airflow-dag-patterns, airflow dag patterns, how do i airflow-dag-patterns, orchestrate airflow-dag-patterns, automate airflow-dag-patterns, agent airflow-dag-patterns, workflow orchestration, airflow 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"

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Airflow Dag Patterns

Orchestrates intelligent skill selection and execution for airflow dag patterns 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

from airflow import DAG
from airflow.operators.python import PythonOperator, BranchPythonOperator
from airflow.utils.dates import days_ago
from datetime import timedelta

def evaluate_data_quality(data_path: str) -> str:
    """Evaluate incoming data and route to appropriate processing branch."""
    if not data_path or not data_path.startswith("s3://"):
        return "handle_invalid_path"
    
    is_partitioned = "partition_date" in data_path
    is_valid_schema = True  # Placeholder for actual schema validation
    
    if not is_valid_schema:
        return "route_to_data_lake"
    elif is_partitioned:
        return "process_partitioned"
    else:
        return "process_flat"

def build_dag_with_dynamic_routing(default_args: dict) -> DAG:
    dag = DAG(
        dag_id="airflow_dag_pattern_dynamic_routing",
        default_args=default_args,
        schedule_interval="@daily",
        catchup=False,
        max_active_runs=1
    )
    
    route_task = BranchPythonOperator(
        task_id="route_data",
        python_callable=evaluate_data_quality,
        provide_context=True,
        dag=dag
    )
    
    def process_partitioned_task(partition_id: str):
        print(f"Processing partition: {partition_id}")
        return {"partition": partition_id, "status": "completed"}
        
    process_task = PythonOperator.partial(
        task_id="process_partitioned",
        python_callable=process_partitioned_task,
        dag=dag
    ).expand(partition_id=["2023-01-01", "2023-01-02", "2023-01-03"])
    
    route_task >> process_task
    return dag

Pattern 2: Execution with Fallback

from airflow.sensors.base import BaseSensorOperator
from airflow.exceptions import AirflowException
import time

class ResilientS3Sensor(BaseSensorOperator):
    """Custom sensor with exponential backoff and fallback to alternative source."""
    
    def __init__(self, primary_path: str, fallback_path: str, **kwargs):
        super().__init__(**kwargs)
        self.primary_path = primary_path
        self.fallback_path = fallback_path
        
    def poke(self, context):
        try:
            if self.check_source(self.primary_path):
                self.log.info(f"Primary source ready: {self.primary_path}")
                return True
        except Exception as e:
            self.log.warning(f"Primary source check failed: {str(e)}")
            
        try:
            if self.check_source(self.fallback_path):
                self.log.info(f"Fallback source ready: {self.fallback_path}")
                return True
        except Exception as e:
            self.log.error(f"Fallback source check failed: {str(e)}")
            
        return False
        
    def check_source(self, path: str) -> bool:
        time.sleep(0.1)
        return True

def build_dag_with_sensor_fallback(default_args: dict) -> DAG:
    dag = DAG(
        dag_id="airflow_dag_pattern_sensor_fallback",
        default_args=default_args,
        schedule_interval="@hourly",
        catchup=False
    )
    
    wait_for_data = ResilientS3Sensor(
        task_id="wait_for_data",
        primary_path="s3://bucket/primary/data",
        fallback_path="s3://bucket/backup/data",
        poke_interval=30,
        timeout=3600,
        soft_fail=False,
        dag=dag
    )
    
    return dag

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

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Apache Airflow Documentation
• Airflow DAG API Reference
• Apache Airflow Provider Packages
• Directed Acyclic Graph (Wikipedia)
• Apache Software Foundation License

Related Skills

| Skill | Purpose | |