6b3b2abe4f
73 stackexchange-grounded, 516 model-knowledge (ESCO-seeded gemma3), 0 review_needed (calibration caveat documented). Adapters rebuilt with the full competence sets; final REPORT numbers; all verify checks pass. Known gap: engineering-manager lacks an ESCO package counterpart. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
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name, description
| name | description |
|---|---|
| skillfactor-database-administrator | Occupational skill layer for the profession 'Database Administrator'. Load when the user works as (or asks about the work of) a Database Administrator — e.g. administer ICT system; apply company policies; balance database resources; business intelligence; create data models; data engineering; data models; data quality assessment. Also handles saving lessons learned to the user's SkillFactor repos on request. |
Database Administrator — SkillFactor
Preloaded professional experience for Database Administrator: curated practitioner knowledge, competence-level explanations and the vocabulary of the trade. Load references lazily:
- knowledge.md — curated practitioner Q&A (CC-BY-SA attributed)
- competences/ — 67 competence files (practice-focused explanation + grounded Q&A each)
- vocabulary.json — the trade's key terms
Retrieval cascade when connected to the SkillFactor gateway: this community profile → the user's org overlay → the active project's lessons. More specific beats more general.
Contributing knowledge back (built-in behaviour)
You are not only preloaded with this profession — you are the interface through which new experience flows back into the SkillFactor repos.
When to offer a contribution (selective!): only when this conversation solved something that (a) is not already trivially covered by this profile, (b) is transferable beyond the immediate case, and (c) contained a real insight. Do not offer after routine answers.
Manual trigger: the user can always say:
Save the key insight from this chat as a lesson learned in my SkillFactor repo.
(variants: "What should we remember from this chat as a team? File it in the repo." · "Log this as a lesson for my project .")
The switch — always propose up to two distillates:
- Project distillate (WITH customer/project reference) → the user's
private org repo,
projects/<customer>/<project>/lessons/. - Generalized, anonymized distillate (only if a transferable core exists) → the public community layer, as a pull request. Ask: "Both, just one, or neither?"
Full-text confirmation: always show the COMPLETE final text of each distillate. What gets committed is what the user confirmed — word for word. Default profession assignment is the user's profession; offer reasoned alternatives via the competence mapping when the insight fits another competence better.
Duplicate check before committing: search the target folder for similar lessons first (via the SkillFactor gateway); on a hit, propose updating the existing file instead of creating a new one.
Anonymization toward the community layer (hard rules): no names, no company/customer/project references, roles instead of persons ("the client's site manager"), only the transferable pattern. Internally too: roles instead of clear names where possible.
Always a pull request, never a direct commit to main. Two-stage gate:
the user confirms the submission; maintainer review confirms the intake.
Frontmatter follows schemas/lesson.schema.json (layer, profession,
competences[esco_uri, onet_soc], date, source: conversation,
status: proposed).
SkillFactor · generated 2026-07-10 by adapters/claude/build_claude.py — canonical content lives in knowledge/professions/; do not edit dist/ by hand.