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User Stories

Every story names its user (A job seeker/student, B SME owner/sales, C economic- development analyst, D recruiter/training provider), the module that serves it, and the decision it supports. Stories marked [fixture-gated] depend on posting-level job data and run on labelled fixtures until a permitted live source is cleared (ADR-008).

Module 1 — Jobs & Skills Intelligence

  • US-01 (A): As a student in Ingolstadt, I see the top requested skills for data and software roles in the region, with source counts and coverage dates, so I can choose electives and certifications. Decision: what to learn next. [fixture-gated]
  • US-02 (A): As a career changer, I see which skills co-occur with my existing skills so I can identify the shortest credible upskilling path. Decision: which adjacent skill closes the biggest gap. [fixture-gated]
  • US-03 (D): As a training provider, I see emerging vs declining skills over time, labelled observed-vs-inferred, so I can plan course portfolios. Decision: which courses to launch or retire. [fixture-gated]
  • US-04 (D): As a recruiter, I see which employers are hiring in which occupation groups and locations. Decision: where to source and place candidates. [fixture-gated]
  • US-05 (C): As an analyst, I see official labour-market indicators for Region 10 (employment, unemployment, sector structure) as time series with source lineage. Decision: baseline for any transformation claim. — runs on real official aggregates from day one.

Module 2 — Company & Transformation Monitor

  • US-06 (B): As an SME sales team, I browse company profiles with industry, themes, locations, and evidence links, filtered to my target segment. Decision: which companies to approach.
  • US-07 (B): As an SME owner, I see change events since the last run (new companies, new themes, hiring shifts) so my market picture stays current without re-research. Decision: where to act this week.
  • US-08 (C): As an analyst, I see how many active companies are associated with traditional-automotive vs emerging themes, with the classification rules published. Decision: whether diversification programs are working.
  • US-09 (C): As an analyst, I can open any company record and audit every merge and every field back to its source evidence. Decision: whether to trust the number in a report to my Stadtrat.

Module 3 — Tender & Research Radar

  • US-10 (B): As an SME, I see active tenders relevant to my themes with deadlines and issuers, newest changes first. Decision: which tenders to pursue.
  • US-11 (B): As an SME, I see which issuers repeatedly publish in my categories. Decision: which buyers to build relationships with.
  • US-12 (C): As an analyst, I see which research projects, institutions, and companies in the region work on which technology themes, over time. Decision: where cluster potential is forming.
  • US-13 (C/D): As an analyst or training provider, I see the network between companies, institutions, projects, and themes to spot collaboration gaps. Decision: whom to connect.

Platform stories

  • US-14 (all): As any user, I see on every page the data sources, retrieval dates, coverage period, known gaps, and whether synthetic data is present. Decision: how much to trust what I'm seeing.
  • US-15 (C): As an analyst, I can export any filtered view to Excel/CSV/Parquet with the lineage metadata included. Decision-support artifact for my own reports.
  • US-16 (developer): As a new developer, I can clone the repo and run the documented demo commands to a working dashboard. Decision: whether this system is credible enough to pay for.