Data Review Skill

A multi-agent data platform review system that audits pipelines, warehouses, analytics infrastructure, and produces a prioritized health report with actionable recommendations.

Prerequisites

None required. Works with any data platform that provides:

• Pipeline configuration files (Airflow DAGs, dbt models, etc.)

• Query logs or analytics code

• Infrastructure-as-code (Terraform, CloudFormation)

• Data quality metrics or monitoring dashboards

Inputs

The user provides:

• Platform description (required) — Overview of the data platform architecture

• OR: Path to configuration directory (e.g., dbt/ , airflow/dags/ , terraform/ )

• Key data flows (optional) — Critical pipelines to prioritize, e.g. "user events → warehouse → BI dashboards"

• Known pain points (optional) — Current issues, slow queries, quality problems

• Compliance requirements (optional) — GDPR, HIPAA, SOC2, etc.

• Context file (optional) — Markdown file with platform details, constraints, team size

If the user doesn't provide optional inputs, use reasonable defaults and note assumptions.

Agent Roster

Each agent has a specialized domain. All agents read the same platform artifacts in parallel.

Agent Focus Reference

1 Data Engineer Pipeline reliability, orchestration, dependencies, error handling, monitoring agents/data-engineer.md

2 Data Scientist Analytics quality, model pipelines, feature engineering, reproducibility agents/data-scientist.md

3 Performance Analyst Query optimization, indexing, partitioning, compute costs, bottlenecks agents/performance-analyst.md

4 Security Auditor Data governance, access controls, PII handling, compliance, lineage agents/security-auditor.md

5 Synthesizer Reads all agent reports → produces prioritized action plan with trade-offs Built-in coordinator role

Workflow

Phase 1: Discovery

• Confirm the platform description or config path

• Identify platform type (dbt, Airflow, Databricks, Snowflake, custom, etc.)

• Scan for key artifacts:

• Pipeline definitions (DAGs, models, workflows)

• Query files (SQL, notebooks)

• Infrastructure code (Terraform, YAML configs)

• Data quality tests or schema definitions

• Monitoring/alerting configurations

• Build a platform inventory listing:

• Pipeline count and types

• Data sources and destinations

• Compute/storage components

• Orchestration tools

• Save discovery results to workspace/discovery.md

This discovery output is shared with all review agents as context.

Phase 2: Parallel Review (Sub-agents)

Spawn agents 1-4 in parallel using the Task tool. Each agent receives:

• The platform description and discovery file

• Their specific agent instructions (from agents/*.md )

• The review checklists and scoring rubric (REFERENCE.md )

• An output file path for their findings

Each agent:

• Reads relevant platform artifacts (configs, code, schemas)

• Applies their domain-specific audit checklist

• Scores each dimension (1-5 scale)

• Documents findings with file/line references

• Writes prioritized recommendations to their output file

Agent output files:

• workspace/agents/data-engineer.md

• workspace/agents/data-scientist.md

• workspace/agents/performance-analyst.md

• workspace/agents/security-auditor.md

Phase 3: Architecture Debate

After all agents complete their audits, spawn a debate session:

• Create a debate prompt with all agent findings

• Agents discuss conflicting recommendations (e.g., performance vs. cost)

• Identify trade-offs and prioritization criteria

• Build consensus on critical vs. nice-to-have improvements

• Save debate transcript to workspace/debate.md

Phase 4: Synthesis

The coordinator (you) acts as the Synthesizer:

• Read all 4 agent reports and debate transcript

• Deduplicate overlapping findings

• Categorize by severity (Critical / High / Medium / Low)

• Rank by impact-vs-effort for small teams

• Produce the final health report

Phase 5: Output

Generate the final deliverable using the report template in REFERENCE.md .

Save to workspace/data-platform-health-report.md and present to the user.

Output Structure

workspace/ ├── discovery.md # Platform inventory from Phase 1 ├── agents/ │ ├── data-engineer.md │ ├── data-scientist.md │ ├── performance-analyst.md │ └── security-auditor.md ├── debate.md # Architecture trade-offs discussion └── data-platform-health-report.md # Final synthesized report

Coordinator Responsibilities

• Run the discovery phase to build platform inventory

• Spawn review agents in parallel with Task tool

• Ensure each agent has access to the discovery file and reference materials

• Collect all agent reports

• Facilitate the architecture debate (spawn debate agents or synthesize manually)

• Produce the final health report with scoring and prioritized recommendations

• Present the report to the user with executive summary

Customization

The user can customize the review by:

• Skipping agents: "Skip data science review, focus on infrastructure and performance"

• Focusing on specific pipelines: "Only review the user_events ETL and downstream models"

• Prioritizing dimensions: "I care most about compliance, less about performance"

• Adding comparisons: "Compare our approach to industry best practices for event streaming"

• Specifying constraints: "We're a 2-person team, recommend low-maintenance solutions only"

• Setting compliance scope: "Audit for GDPR compliance specifically"

Adapt the agent roster and instructions accordingly.

Scoring System

Each agent rates their domain on a 1-5 scale:

• 5 (Excellent): Industry best practices, fully automated, no issues

• 4 (Good): Minor improvements possible, well-maintained

• 3 (Adequate): Functional but needs attention, some technical debt

• 2 (Poor): Significant issues, requires immediate action

• 1 (Critical): Broken or severely compromised, blocking business value

The final report includes:

• Overall platform health score (average across domains)

• Per-domain scores with justification

• Critical findings (score ≤ 2)

• Quick wins (high impact, low effort)

• Strategic improvements (high impact, high effort)

Common Review Scenarios

Scenario 1: New Team Inheriting a Data Platform

/data-review "Inherited a Snowflake + dbt + Airflow stack. Need to understand health and risks."

Scenario 2: Pre-Migration Assessment

/data-review "Planning to migrate from on-prem Postgres to BigQuery. Audit current state."

Scenario 3: Performance Investigation

/data-review "Dashboard queries taking 2+ minutes. Focus on query optimization and indexing."

Scenario 4: Compliance Audit

/data-review "Need HIPAA compliance audit of our analytics platform. Check PII handling and access controls."

Scenario 5: Cost Optimization

/data-review "Warehouse costs doubled this quarter. Identify waste and optimization opportunities."

Agent Invocation Pattern

Example internal workflow

use Task tool to spawn:

• data-engineer with discovery.md + REFERENCE.md → workspace/agents/data-engineer.md
• data-scientist with discovery.md + REFERENCE.md → workspace/agents/data-scientist.md
• performance-analyst with discovery.md + REFERENCE.md → workspace/agents/performance-analyst.md
• security-auditor with discovery.md + REFERENCE.md → workspace/agents/security-auditor.md

Wait for all agents to complete

Spawn debate session (optional)

use Task tool to spawn debate with all agent findings

Synthesize final report

Read all outputs + debate transcript Apply report template from REFERENCE.md Generate data-platform-health-report.md

References

• REFERENCE.md — Audit checklists, scoring rubric, common issues and fixes

• agents/data-engineer.md — Pipeline reliability and orchestration focus

• agents/data-scientist.md — Analytics quality and reproducibility focus

• agents/performance-analyst.md — Query optimization and cost efficiency focus

• agents/security-auditor.md — Governance, compliance, and access control focus