Lagscope
A methodology-first data product for learning and curriculum leaders that normalizes permitted learning-interest and job-posting sources by skill, region, seniority, and quarter, publishes uncertainty and revisions, and supports planning without predicting hiring or individual career outcomes.
Learning teams and technical programs need to decide which skills to teach before slow annual reports reveal a mismatch. Lagscope builds a quarterly index from sources the product is authorized to use, maps both sides to a versioned skills taxonomy, and exposes coverage, sample changes, uncertainty, and revisions. The input confirms accessible job-posting and skills-taxonomy sources but does not verify equivalent access to every named learning platform. Search, stars, courses, enrollments, completions, job postings, employer demand, hires, wages, curriculum decisions, and learner outcomes are different signals. A rising index is not a forecast, causal claim, hiring guarantee, or recommendation for an individual.
An L&D director, workforce-planning analyst, bootcamp curriculum committee, technical academy, training provider, or research team that needs a repeatable market-timing view.
The source describes a current learning-versus-posting mismatch, but the underlying headline percentages are not independently verified in the authoring input.
L&D, workforce, and curriculum leaders are concrete, though their purchasing and planning cadences differ.
Three inbound and three direct connections provide moderate convergence despite no cross-reference count.
A clear institutional buyer, three inbound and three direct links, two confirmed job APIs, an attributable open skills taxonomy, and no identified recurring developer-specific lag index support a testable data-product thesis.
Learning-side data access is not verified, source coverage and taxonomies can bias the index, free and annual reports are substitutes, willingness-to-pay is unproven, and observed interest cannot be treated as labor demand or outcome evidence.
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