de-lead-routing

Mandatory delegation directive (R010 bypassPermissions)

When this skill spawns a subagent via the Agent tool, the spawned prompt MUST include this directive verbatim (or equivalent):

이 스킬이 Agent tool로 서브에이전트를 스폰할 때 mode: "bypassPermissions"를 반드시 포함한다. 서브에이전트는 .claude/ 경로에 직접 Write/Edit/Bash를 사용한다(/tmp 우회 불필요, CC v2.1.121+). See R010.

This directive is preserved inline because Agent-tool prompt synthesis can drop SKILL.md notes; inline mandatory directives survive (#1046 lesson).

DE Lead Routing Skill

Purpose

Routes data engineering tasks to appropriate DE expert agents. This skill contains the coordination logic for orchestrating data engineering agents across orchestration, modeling, processing, streaming, and warehouse specializations.

Engineers Under Management

Type	Agents	Purpose
de/orchestration	de-airflow-expert	DAG authoring, scheduling, testing
de/modeling	de-dbt-expert	SQL modeling, testing, documentation
de/processing	de-spark-expert	Distributed data processing
de/streaming	de-kafka-expert	Event streaming, topic design
de/warehouse	de-snowflake-expert	Cloud DWH, query optimization
de/architecture	de-pipeline-expert	Pipeline design, cross-tool patterns

Tool/Framework Detection

Keyword Mapping

Keyword	Agent
"airflow", "dag", "scheduling", "orchestration"	de-airflow-expert
"dbt", "modeling", "sql model", "analytics engineering"	de-dbt-expert
"spark", "pyspark", "distributed processing", "distributed"	de-spark-expert
"kafka", "streaming", "event", "consumer", "producer"	de-kafka-expert
"snowflake", "warehouse", "clustering key"	de-snowflake-expert
"pipeline", "ETL", "ELT", "data quality", "lineage"	de-pipeline-expert
"iceberg", "table format"	de-snowflake-expert or de-pipeline-expert

File Pattern Mapping

Pattern	Agent
dags/*.py, airflow.cfg, airflow_settings.yaml	de-airflow-expert
models/**/*.sql, dbt_project.yml, schema.yml	de-dbt-expert
Spark job files, spark-submit configs	de-spark-expert
Kafka configs, *.properties (Kafka), streams/*.java	de-kafka-expert
Snowflake SQL, warehouse DDL	de-snowflake-expert

Routing Decision (Priority Order)

Before routing via Agent tool, evaluate in this order:

Step 1: Agent Teams Eligibility (R018)

Check if Agent Teams is available — CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 AND TeamCreate present in the tool list. The env var alone is NOT sufficient, and SendMessage presence is not evidence of Teams (see R018 Detection).

Scenario	Preferred
Single-tool DE task	Agent Tool
Multi-tool pipeline design (3+ tools)	Agent Teams
Cross-tool data quality analysis	Agent Teams
Quick DAG/model validation	Agent Tool

When Detection resolves to No, skip this step — route via the Agent tool under R009/R010.

Step 2: Expert Selection

Route to appropriate DE expert based on tool/framework detection.

Permission Mode: When spawning agents via Agent tool, always pass mode: "bypassPermissions". The Agent tool default (acceptEdits) overrides agent frontmatter permissionMode, causing permission prompts during unattended execution.

Step 4: Ontology-RAG Enrichment (R019)

If get_agent_for_task MCP tool is available, call it with the original query and inject suggested_skills into the agent prompt. Skip silently on failure.

Step 4b: Wiki-RAG Enrichment

For ambiguous routing (confidence < 90%), query the wiki for context:

1. Search wiki/index.yaml for DE-related pages matching the request
2. Inject relevant skill/guide suggestions into the spawned agent's prompt
3. Particularly useful for cross-domain DE tasks (e.g., Kafka + Spark)

Advisory only — skip silently if wiki unavailable.

Step 5: Soul Injection (R006)

If the selected agent has soul: true in frontmatter, read and prepend .claude/agents/souls/{agent-name}.soul.md content to the prompt. Skip silently if file doesn't exist.

Command Routing

DE Request → Detection → Expert Agent

Airflow DAG → de-airflow-expert
dbt model   → de-dbt-expert
Spark job   → de-spark-expert
Kafka topic → de-kafka-expert
Snowflake   → de-snowflake-expert
Pipeline    → de-pipeline-expert
Multi-tool  → Multiple experts (parallel)

Routing Rules

1. Pipeline Development Workflow

1. Receive pipeline task request
2. Identify tools and components:
   - DAG orchestration → de-airflow-expert
   - SQL transformations → de-dbt-expert
   - Distributed processing → de-spark-expert
   - Event streaming → de-kafka-expert
   - Warehouse operations → de-snowflake-expert
   - Architecture decisions → de-pipeline-expert
3. Select appropriate experts
4. Distribute tasks (parallel if 2+ tools)
5. Aggregate results
6. Present unified report

Example:

User: "Design a pipeline that runs dbt models from Airflow and loads into Snowflake"

Detection:
  - Airflow DAG → de-airflow-expert
  - dbt model → de-dbt-expert
  - Snowflake loading → de-snowflake-expert
  - Pipeline architecture → de-pipeline-expert

Route (parallel where independent):
  Agent(de-pipeline-expert → overall architecture design)
  Agent(de-airflow-expert → DAG structure)
  Agent(de-dbt-expert → model design)
  Agent(de-snowflake-expert → warehouse setup)

Aggregate:
  Pipeline architecture defined
  Airflow DAG: 5 tasks designed
  dbt: 12 models structured
  Snowflake: warehouse + schema configured

2. Data Quality Workflow

1. Analyze data quality requirements
2. Route to appropriate experts:
   - dbt tests → de-dbt-expert
   - Pipeline validation → de-pipeline-expert
   - Source freshness → de-airflow-expert
3. Coordinate cross-tool quality strategy

3. Multi-Tool Projects

For projects spanning multiple DE tools:

1. Detect all DE tools in project
2. Identify primary tool (most files/configs)
3. Route to appropriate experts:
   - If task spans multiple tools → parallel experts
   - If task is tool-specific → single expert
4. Coordinate cross-tool consistency

Sub-agent Model Selection

Model Mapping by Task Type

Task Type	Recommended Model	Reason
Pipeline architecture	opus	Deep reasoning required
DAG/model review	sonnet	Balanced quality judgment
Implementation	sonnet	Standard code generation
Quick validation	haiku	Fast response

Model Mapping by Agent

Agent	Default Model	Alternative
de-pipeline-expert	sonnet	opus for architecture
de-airflow-expert	sonnet	haiku for DAG validation
de-dbt-expert	sonnet	haiku for test checks
de-spark-expert	sonnet	opus for optimization
de-kafka-expert	sonnet	opus for topology design
de-snowflake-expert	sonnet	opus for warehouse design

No Match Fallback

When a data engineering tool is detected but no matching agent exists:

User Input → No matching DE agent
  ↓
Detect: DE tool keyword or config file pattern
  ↓
Delegate to mgr-creator with context:
  domain: detected DE tool
  type: de-engineer
  keywords: extracted tool names
  file_patterns: detected config patterns
  skills: auto-discover from .claude/skills/
  guides: auto-discover from templates/guides/

Examples of dynamic creation triggers:

• New data tools (e.g., "Dagster DAG 만들어줘", "Flink 스트리밍 설정해줘")
• Unfamiliar data formats or connectors
• Data tool detected in project but no specialist agent

Usage

This skill is NOT user-invocable. It should be automatically triggered when the main conversation detects data engineering intent.

Detection criteria:

• User requests pipeline design or data engineering
• User mentions DE tool names (Airflow, dbt, Spark, Kafka, Snowflake)
• User provides DE-related file paths (dags/, models/, etc.)
• User requests data quality or lineage work