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Seed Capabilities

ONET base: v28.0 | License: CC-BY-4.0 (ONET base); Bukti extensions MIT

The seed set contains approximately 200 capability nodes drawn from the O*NET Content Model (35 cross-occupational skills, 33 knowledge areas, ~20 selected abilities) and domain-specific extensions covering education / pedagogy, software engineering, data science, research, design, and business.

This page lists a representative subset across all domains. All ONET-based nodes reference the ONET 28.0 element IDs; ESCO crosswalks are present on nodes where the mapping was deterministic.

O*NET Base Layer — Cross-Occupational Skills (representative selection)

These are the top-level skill nodes seeded directly from the O*NET Content Model. They have no parent and map to the most relevant cluster.

capability_id label cluster onet_refs esco_refs
cap_s001 Active Learning cluster:default 2.B.1.a S2.1
cap_s002 Active Listening cluster:default 2.B.1.b S2.1
cap_s003 Critical Thinking cluster:default 2.B.1.c S2.1
cap_s004 Complex Problem Solving cluster:default 2.B.3.g S2.1
cap_s006 Instructing cluster:education-pedagogy 2.B.5.b S1.4
cap_s009 Mathematics cluster:foundational-quantitative 2.B.2.a K01
cap_s013 Programming cluster:software-engineering 2.B.3.e S5.6.1
cap_s015 Science cluster:default 2.B.2.c —
cap_s021 Technology Design cluster:software-engineering 2.B.3.c S6.4.1.4
cap_s023 Troubleshooting cluster:software-engineering 2.B.3.f S5.6.1

O*NET Base Layer — Knowledge Areas (representative selection)

capability_id label cluster onet_refs esco_refs
cap_k006 Computers and Electronics cluster:software-engineering 2.C.4.a S5.6.1
cap_k009 Economics and Accounting cluster:default 2.C.6.c K04
cap_k010 Education and Training cluster:education-pedagogy 2.C.7.a S1.4
cap_k011 Engineering and Technology cluster:software-engineering 2.C.4.e S6.4
cap_k018 Law and Government cluster:domain-legal 2.C.10.a K04
cap_k019 Mathematics Knowledge cluster:foundational-quantitative 2.C.3.a K01
cap_k021 Medicine and Dentistry cluster:domain-medical 2.C.8.a K09
cap_k026 Psychology cluster:default 2.C.7.b S1.4

Education / Learning Design Domain

Domain-specific child nodes of cap_k010 (Education and Training Knowledge).

capability_id label cluster onet_refs esco_refs
cap_ed001 Curriculum Design cluster:education-pedagogy 2.C.7.a S1.4
cap_ed002 Instructional Design cluster:education-pedagogy 2.C.7.a S1.4
cap_ed003 Learning Management Systems cluster:education-pedagogy 2.C.4.a S5.6.1
cap_ed004 eLearning Development cluster:education-pedagogy 2.C.4.a S5.6.1
cap_ed005 Assessment Design cluster:education-pedagogy 2.C.7.a S1.4
cap_ed006 Program Evaluation cluster:education-pedagogy 2.C.7.a S1.4
cap_ed007 Learning Analytics cluster:education-pedagogy 2.C.7.a S1.4
cap_ed008 Coaching and Mentoring cluster:education-pedagogy 2.B.5.b S1.4
cap_ed009 Facilitation cluster:education-pedagogy 2.B.5.b S1.4
cap_ed010 Pedagogy cluster:education-pedagogy 2.C.7.a S1.4
cap_ed011 Adult Learning Theory cluster:education-pedagogy 2.C.7.a S1.4
cap_ed012 Diversity, Equity, and Inclusion in Education cluster:education-pedagogy 2.C.7.a S1.4
cap_ed014 Universal Design for Learning cluster:education-pedagogy 2.C.7.a S1.4
cap_ed018 Instructional Technology cluster:education-pedagogy 2.C.4.a S5.6.1
cap_ed020 Educational Research Methods cluster:education-pedagogy 2.C.7.a S1.4
cap_ed021 Competency-Based Education cluster:education-pedagogy 2.C.7.a S1.4
cap_ed024 Blended and Hybrid Learning Design cluster:education-pedagogy 2.C.7.a S1.4
cap_ed036 Learning Experience Design cluster:education-pedagogy 2.C.7.a S1.4

Software Engineering Domain

Child nodes of cap_s013 (Programming) and related skills.

capability_id label cluster onet_refs esco_refs
cap_se001 Python cluster:software-engineering 2.B.3.e S5.6.1
cap_se002 JavaScript cluster:software-engineering 2.B.3.e S5.6.1
cap_se003 TypeScript cluster:software-engineering 2.B.3.e S5.6.1
cap_se004 Go cluster:software-engineering 2.B.3.e S5.6.1

The software engineering domain covers languages, frameworks, infrastructure, databases, API design, testing, and system design.

Node count summary

Domain ID prefix Approximate count
O*NET skills (cross-occupational) cap_s* 35
O*NET knowledge areas cap_k* 33
O*NET abilities (selected) cap_a* 20
Education / learning design cap_ed* ~40
Software engineering cap_se* ~50
Data science / applied ML cap_ds* ~40
Research methodology cap_re* ~25
Design cap_de* ~20
Business / management cap_bu* ~25

Total: approximately 290 nodes at initial seeding. Growth nodes (growth_{slug}) are created at runtime for capabilities extracted from evidence that do not match the seed under the auto-map embedding-similarity threshold.