data-pipeline-builder

Included with Lifetime

$97 forever

Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.

Backend & APIs

What this skill does

# Data Pipeline Builder

Design and implement production-grade ETL/ELT data pipelines: take data sources, a destination, and transformation requirements, then produce a complete pipeline specification plus all implementation files needed to run it.

## Contents

- `references/project-structure.md` -- output file layout, architecture pattern selection, component selection.
- `references/python-patterns.md` -- Python code standards and base extractor/transformer/loader/retry patterns.
- `references/quality-checks.md` -- composable data quality check framework and built-in checks.
- `references/orchestration-config.md` -- Airflow DAG, pipeline config YAML, and monitoring/alerting patterns.
- `references/spec-template.md` -- the `data-pipeline-spec.md` output template.

## Workflow

1. Gather requirements. If the user gave clear requirements, proceed to design. Otherwise ask targeted questions: data sources (databases, APIs, files, streams); destination (warehouse, lake, database); transformations (joins, aggregations, filters, business rules); freshness requirement (real-time, hourly, daily); technology preferences (Airflow, dbt, Spark, cloud provider); data quality and compliance requirements.

2. Analyze and design. Catalog each source (connection type, auth, schema, volume, CDC availability, rate limits). Define the destination (platform, schema design, partitioning, clustering, access patterns). Map transformations (field mappings, business logic, type conversions, joins, aggregations, deduplication, SCD handling, derived fields). Establish non-functional requirements (freshness SLA, processing window, failure tolerance, retention, compliance). Select an architecture pattern and components per `references/project-structure.md`.

3. Present the design before generating code. Confirm architecture, sources, destination, schedule, key transformations, and quality gates with the user, then proceed on approval.

4. Generate implementation. Produce all files following the layout in `references/project-structure.md`, customized to the specific pipeline with no placeholder code requiring manual editing:
- For each source, generate a concrete extractor inheriting from `BaseExtractor` (see `references/python-patterns.md`).
- For each transformation, generate a concrete transformer class or SQL file.
- For each destination, generate a concrete loader inheriting from `BaseLoader`.
- Generate the Airflow DAG with all task dependencies wired up and the pipeline config YAML (see `references/orchestration-config.md`).
- Generate quality checks tailored to the data and monitoring config with appropriate alert thresholds (see `references/quality-checks.md` and `references/orchestration-config.md`).
- Generate tests for all custom business logic.

5. Generate the specification last. Produce `data-pipeline-spec.md` using `references/spec-template.md`, referencing all implementation files and incorporating design decisions made during the process.

## Operating Rules

- Design for idempotency -- make every step safely re-runnable.
- Include watermark/checkpoint tracking for incremental pipelines.
- Include dead letter handling for records that fail processing.
- Include schema evolution handling -- sources will change their schemas.
- Never hardcode credentials -- use environment variables or secret managers.
- Never skip quality checks -- they are the first line of defense against bad data.
- Prefer SQL for transformations expressible in SQL; use Python for complex logic that does not map cleanly to SQL.
- Include a backfill strategy and an operational runbook covering common failure scenarios in the spec.
- Use structured logging throughout and track data lineage at every transformation step.

Files: 6

Size: 42.8 KB

Complexity: 54/100

Category: Backend & APIs

Source: https://github.com/onewave-ai/claude-skills/tree/main/data-pipeline-builder

Related in Backend & APIs

jfrog

Included

Interact with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.

Backend & APIsscripts

cupynumeric-migration-readiness

Included

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Backend & APIsscripts

alibabacloud-data-agent-skill

Included

Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".

Backend & APIsscripts

token-optimizer

Included

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Backend & APIsscripts

resend-cli

Included

Use this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.

Backend & APIsscripts

alibabacloud-odps-maxframe-coding

Included

Use this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.

Backend & APIsscripts