doc-pipeline
SolidChain document operations into reusable pipelines
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Quality Score: 94/100
Skill Content
Details
- Author
- majiayu000
- Repository
- majiayu000/claude-skill-registry
- Created
- 5 months ago
- Last Updated
- today
- Language
- HTML
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
pipeline-design
Design ETL/ELT pipelines end-to-end — source connectors, extraction strategies, transform logic, load patterns, idempotency, scheduling, and error handling. Use this skill whenever the user is starting a new ingestion job, planning how data moves from a source (REST API, database, file, webhook, message queue) into a data warehouse or data lake. Also trigger when the user asks about pipeline architecture, incremental vs. full loads, backfill strategies, CDC, retry logic, or orchestration choices (Airflow, Prefect, dbt). This skill should feel like pairing with a senior data engineer on day one of a new pipeline project.
ln-100-documents-pipeline
Creates complete project documentation system (project docs, reference, tasks, tests). Use when bootstrapping docs from scratch or regenerating all.
content-pipeline
Orchestration plan for a five-stage long-form content pipeline (raw capture → theme extraction → research → draft → quality gate and fan-out). Defines each stage's input, output, success criteria, and retry rules so a content-marketer agent can chain the existing writing, research, and distribution skills deterministically. Use when user asks about content pipeline, production workflow, blog production line, multichannel fan-out, editorial workflow, 콘텐츠 파이프라인, 장문 워크플로, 블로그 제작 라인, or 멀티채널 확산.
doc-writing
Generate API documentation from a route manifest. Use when you have a list of discovered routes and need to produce markdown documentation.
data-pipelines
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.