Competencies are distributed.
Profiles, learning paths, courses and evidence sit in separate systems. Decisions then rely on incomplete data.
Transparent skill profiles and reviewable evidence are needed.Open Skills Consortium
Offene Kompetenzdaten für nachvollziehbare Skills-Entscheidungen.
OSC bündelt Arbeitsstände für Skill-Profile, Kompetenznachweise und interoperable Skills-Daten. Ziel ist ein belastbares Referenzmuster für HR, Bildung, Technik und Produktteams.
- Skill-Profile
- Kompetenznachweise
- Offene APIs
- Skills Graph
Working status of the Open Skills Consortium
Working draft
Reference patterns for skill profiles, evidence and skills graph.
Interoperabel
IDs, versioning and export profiles are considered from the start.
Begrenzbar
Start with clearly bounded use cases in HR, education, engineering and product teams.
Ausgangslage
Skills often exist, but they are not reliably usable.
Many organizations work with course data, job profiles, certificates and internal terms. Without a shared structure, matching, learning, product features and evidence remain hard to review.
Terms are not stable.
Skill names change. IDs are missing. Versions and exports are not clear enough for reliable integrations.
Clear data contracts, IDs and export profiles are needed.Rollout questions arrive too late.
Governance, audiences and interoperability often become visible only after the pilot. That makes scaling harder.
Bounded pilots with clear acceptance criteria are needed.Warum OSC
Der USP: strukturierte Skills-Daten plus prüfbare KI-Signale.
Der OSC-Ansatz verbindet Knowledge graph, Ontologie-Regeln, Evidenceobjekte und optionale Embeddings. So bleiben Skill-Definitionen für HR verständlich, für Entwicklungsteams umsetzbar und für Produktentscheidungen steuerbar.
Von Einzelbegriffen zu Skill-Knoten mit Kontext.
Ein Knowledge graph verknüpft Skill-Instanzen mit Roles, Lernangeboten, Evidenceobjekten, Quellen und Relationen. Die Ontologie definiert die Begriffe, Klassen, Relationen, Regeln und Semantics dahinter.
Ähnlichkeitssignale nutzen, ohne sie mit Nachweisen zu verwechseln.
Embeddings und LLM-generierte Vektoren können Suche, Matching, Clustering und Empfehlungsvorschläge unterstützen. Sie bleiben abgeleitete Hilfssignale und beweisen keine Kompetenz einer Person.
Verhaltensmuster erst prüfbar machen, bevor sie genutzt werden.
Beobachtete oder abgeleitete Verhaltensindikatoren brauchen Quelle, Zeitbezug, Kontext und Unsicherheit. Sie können Skill-Definitionen vorbereiten, aber finale Kompetenzaussagen brauchen Nachweise, Assessments, Credentials oder dokumentierte Projekte.
Value
OSC describes a shared foundation for skills data.
The consortium works on reference patterns that help organizations describe, exchange and review competency data. Value emerges where data from HR, education, engineering, product and evidence systems has to be connected.
Transparency about abilities.
Skill profiles should show which competencies exist, which evidence belongs to them and where learning or recognition can start usefully.
Reliable integrations.
A skills graph needs unambiguous nodes, stable relationships and traceable changes. OSC outlines contract surfaces for that work.
Planbare Piloten.
Product teams can start with bounded data spaces, define acceptance criteria and clarify connection points for later rollouts.
Next step
Start with a reviewable skills data space.
A bounded pilot shows which data is already usable and which standards are still missing.
Mechanics
From term to usable competency record.
OSC does not describe a loose taxonomy. The focus is on data, evidence and exchange formats that remain readable to domain experts and can be technically integrated into existing systems.
- Capture skill terms, normalize them and assign stable IDs.
- Connect competency evidence, micro-credentials and sources in a traceable way.
- Model relationships in the skills graph and update them with versions.
- Define export profiles and API contracts for HR, learning and product systems.
- Document quality, provenance and changes for audits and governance.
- Test pilot data against real matching, learning or evidence processes.
Governance
Open skills data needs rules, not only interfaces.
A shared standard must be readable to domain experts and technically reviewable. OSC therefore separates the data model, evidence logic and operating questions.
Traceable.
Every skill record needs provenance, meaning and validity. Without these details, matching remains unreliable.
Interoperable.
OSC works toward open standards, APIs and export profiles. Existing HR and learning systems should not have to be replaced.
Gradually adoptable.
Organizations can start with a clear use case and derive requirements for data, roles and operations.
Reviewable.
Competency evidence and micro-credentials must be machine-readable, while still being understandable to HR and audit teams.
Pilot patterns
Concrete entry points for HR, engineering and product.
The following patterns describe possible pilots for HR, education, engineering and product teams. They are intentionally bounded so value, data quality and interoperability become visible early.
Skill matching for internal roles.
- Problem
- Role requirements and existing competencies are not described consistently.
- OSC response
- Skill profiles with IDs, evidence and defined rating levels are modeled as a pilot data set.
- Reviewable result
- HR can explain matching rules, see deviations and derive learning needs.
Micro-credentials in learning paths.
- Problem
- Courses produce attendance confirmations, but rarely interoperable competency evidence.
- OSC response
- Evidence is connected with skills, validity, issuer and evaluation logic.
- Reviewable result
- Education and L&D teams can align learning paths more closely with roles, projects and audit requirements.
API profile for skills data.
- Problem
- HR, learning and product systems use different terms and exchange formats.
- OSC response
- An export profile describes fields, IDs, versions and minimum quality for data exchange.
- Reviewable result
- Engineering and development teams get a clear contract surface for integration, testing and operation.
AI support for skill definitions.
- Problem
- Teams leiten Skill-Begriffe, Synonyme und Relationen oft aus Rolesprofilen, Lerninhalten oder Projektnachweisen ab.
- OSC response
- KI kann Entwürfe für Definitionen, Labels und Relationen vorbereiten. Quelle, Bedeutung, Version und Freigabestand bleiben fachlich zu prüfen.
- Reviewable result
- Skill-Definitionen entstehen schneller als Working draft, ohne Modell-Outputs als Nachweis oder finalen Standard zu behandeln.
Chatbots brauchen ebenfalls Skill-Definitionen.
- Problem
- Chatbots beantworten Fragen zu Roles, Lernpfaden oder Nachweisen. Ohne definierte Skills können sie Begriffe, Quellen und Geltung vermischen.
- OSC response
- Ein gepflegtes Skills-Data model liefert Begriffe, IDs, Relationen, Quellen und Freigabestand als nutzbaren Kontext für Chatbot-Antworten.
- Reviewable result
- Antworten können auf gepflegte Definitionen verweisen und bleiben Hilfestellung, nicht Kompetenznachweis.
Pilot review
Which data scope is suitable for the start?
A good pilot starts with a few skill profiles, clear evidence and a concretely reviewable process.
Working method
From reference pattern to reliable pilot.
OSC formulates statements as working drafts. This creates room for tests without turning early patterns into finished standards.
Fachlich lesbar.
HR, education and domain teams need to understand why a skill, evidence item or matching result is used.
Technically reviewable.
Data model, IDs, versions and interfaces are described so development teams can test them.
Governance-ready.
Roles, approvals, sources and change processes are documented early so a pilot does not fail in operations.
Contact
Review an OSC pilot for your skills data.
Briefly describe which competency data, evidence or interfaces matter. We will assess whether a bounded pilot or an expert workshop is useful.