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Pipeline planning and task breakdown

Skill vaquarkhan/data-engineering-agent-skills/skills/pipeline-planning-and-task-breakdown

Production-grade Agent Skills for data engineering AI agents: 73 workflows, platform presets, safe backfill/replay, Kafka & Spark reliability, MCP observability, and VS Code/JetBrains installers.

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npx -y skills add vaquarkhan/data-engineering-agent-skills --skill pipeline-planning-and-task-breakdown

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Breaks approved data specifications into safe, verifiable implementation tasks. Use when a data project spans multiple steps, systems, or files and needs dependency-aware sequencing.

SKILL.md

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Pipeline Planning And Task Breakdown

Overview

Turn a validated data specification into a sequence of small tasks that can be implemented and verified independently. A good plan protects the team from wide, risky pipeline edits.

When to Use

  • multi-step ingestion or modeling work
  • changes involving orchestration, infrastructure, and transformations
  • work that needs coordination across contracts, checks, and deployment

Do not use this when the task is a single isolated change with obvious verification.

Workflow

  1. Map the affected layers. Typical layers include:

    • source connections
    • raw landing
    • staging transformations
    • business models
    • orchestration
    • tests and checks
    • documentation and runbooks

  2. Order work by dependency. Good sequence:

    • contracts
    • scaffolding
    • ingestion
    • transformations
    • checks
    • orchestration
    • rollout and recovery notes

  3. Keep tasks small. Each task should:

    • have one clear outcome
    • touch a limited set of files
    • define how it will be verified
    • avoid mixing unrelated concerns

  4. Call out risk points. Always identify:

    • schema evolution risk
    • backfill risk
    • cost or performance risk
    • downstream compatibility risk

  5. Define evidence for every task. Examples:

    • test results
    • contract validation output
    • successful dry run
    • row count reconciliation
    • lineage or docs update

Common Rationalizations

RationalizationReality
"It is faster to change everything in one pass."Wide changes increase blast radius and make rollback harder.
"We can skip task boundaries because the repo is small."Data dependencies, not repo size, create risk.
"The orchestrator wiring can wait until the end."Scheduling and retry behavior often shape the implementation.

Red Flags

  • a single task spans ingestion, modeling, orchestration, and rollout
  • verification says only "run the pipeline"
  • rollback or compatibility concerns are not mentioned
  • the plan does not separate build work from publish work

Verification

  • Tasks are ordered by dependency
  • Each task has acceptance criteria and evidence
  • Risks and rollback concerns are called out
  • Checks and documentation are part of the plan, not afterthoughts

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