Summarize BLS labor statistics programs and cross-program data concepts.
npx skills add https://github.com/lowmason/agent-skills --skill bls-data-context
Please help me install this Agent Skill. Skill: bls-data-context Source: https://github.com/lowmason/agent-skills/tree/main/bls-data-context Command: npx skills add https://github.com/lowmason/agent-skills --skill bls-data-context
This Skill serves as a canonical reference hub for BLS data programs and their interoperability, enabling fast access to program definitions, scope, and data-flow rules. It consolidates cross-program concepts such as universe, reference period, data schemas, and program reconciliation practices to reduce misinterpretation when building pipelines or reasoning about revisions.
Load the BLS Data Context hub to access program definitions, crosswalks, and reference material before building or validating any labor data pipelines.
High-intent search queries and answers about installing and using this skill.
To reconcile BLS data revisions, use this reference hub to reason about vintages, benchmarks, and program reconciliation rules across QCEW, CES, and other labor-market platforms to ensure pipeline accuracy.
The difference between QCEW and CES universes lies in their scope and reference periods. This hub centralizes official program definitions to clarify data schemas and reduce cross-program misinterpretation.
Build a crosswalk between JOLTS and BED by loading on-demand program references and lookup codes provided here, which support robust data pipelines and crosswalk construction for labor statistics.
Yes, you need a canonical reference to validate BLS series IDs. Loading this hub provides unified frontmatter and program scope definitions to prevent data flow errors before pipeline execution.
The best way to embed BLS context into an agent is to load this hub's program references to provide the agent with cross-program scope, data flow rules, and lookup codes for reasoning tasks.
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