인터페이스는 현지화되어 있으며, Skill 소개와 설명 문서는 선택한 언어로 AI가 자동 번역합니다. 코드·명령어·경로·전문 용어는 원문을 유지하며 원작자 문서가 우선합니다.
Skills 둘러보기
데이터 및 연구 · SKILL.md

airflow-dag-patterns

airflow-dag-patterns

Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: airflow-dag-patterns description: "Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task."

Airflow Dag Patterns

Use this Skill to produce a bounded, verifiable Airflow Dag Patterns outcome. Preserve the user's chosen stack, source material, and authorization boundaries.

Read the SandBase API map only when the task genuinely needs an external data source or generative model.

Workflow

  1. Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change.
  2. Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result.
  3. Produce the smallest complete implementation, analysis, or artifact that satisfies those checks.
  4. Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone.
  5. Return the deliverable, evidence of validation, material assumptions, and unresolved limitations.

Quality gates

  • Inspect shapes, types, units, missing values, sampling, target leakage, and train/test boundaries before modeling or transformation.
  • Pin or record relevant library versions, random seeds, parameters, and environment assumptions for reproducibility.
  • Validate against a baseline or independent calculation and report diagnostics, uncertainty, failure modes, and resource use.

Focus checks

  • Make tasks idempotent, set retries and timeouts deliberately, define data intervals and time zones, and test backfill, catchup, and partial-failure behavior.

SandBase boundary

Keep the core Airflow Dag Patterns work local. Use SandBase only for an explicitly requested external dataset or model inference step that is not part of the local analysis.

  1. Call sandbase_discover with a short capability query.
  2. Call sandbase_inspect for viable candidates and compare the live schema, coverage, limits, output, execution mode, and price.
  3. Prefer a dedicated tool or API the user already has. Send only the minimum necessary data.
  4. Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation.
  5. Use sandbase_account before an approved multi-call batch and call sandbase_run only with current schema-defined arguments.
  6. Poll asynchronous work with sandbase_run_get using the same run ID; never resubmit merely because it is pending.
  7. Use sandbase_runs only to recover status or reconcile observed cost.

If SandBase is unavailable, continue with local work and authorized sources when possible. Do not silently switch providers, fabricate external results, or claim a generation or retrieval succeeded.

Handoff

Provide the completed artifact or findings, concise reproduction steps, checks actually run, source or asset provenance, SandBase endpoint and run IDs when used, observed cost when available, and any follow-up that still requires user action.

저장한 Skills