data-lineage
Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- data-lineage
- description
- Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.
03 SKILL.md 正文
Data Lineage
Overview
Lineage maps the path from source systems through transformations to reports and models. It is essential for debugging, impact analysis, and auditability.
When to Use
- Diagnosing incorrect dashboard numbers
- Assessing downstream impact of schema or logic changes
- Audit and regulatory traceability needs
- Building trust in complex transformation graphs
Core Practices
- Capture lineage at useful granularity (table, column, or job level as needed)
- Automate extraction from warehouses, ETL/ELT, and BI tools where possible
- Use lineage in change management (who must be notified)
- Combine technical lineage with business explanations of transforms
- Keep lineage current as pipelines evolve
- Prioritize critical metric paths first
Principles
- Partial accurate lineage beats comprehensive stale lineage
- Column-level lineage is high value for contested metrics
- Lineage is a means to faster, safer change — not an end in itself
- Manual documentation alone does not scale
Verification
- [ ] Critical metrics can be traced to sources
- [ ] Change impact can identify key downstream consumers
- [ ] Lineage tooling is maintained, not abandoned
04 同仓库更多 Skills
compliance-monitoring
Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.
complaints-handling
Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.
aml-kyc-basics
Apply AML/KYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.
ai-model-compliance
Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.
data-quality-management
Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.
data-governance
Establish data governance — ownership, policies, decision rights, and accountability — so data is trusted, compliant, and usable across the organization. Use when designing governance operating models, assigning data owners, or fixing chronic trust and access problems.