5c03e07ded
Simpler story (slide decks unread -> three-bullet status email), proper Claude-chat look with avatars and bubbles on both sides. Canonical trigger prompt switched to English everywhere (homepage, architecture, both adapter generators rebuilt) - V7 consistency green, all checks pass. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
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esco_uri, esco_label, relation, onet_soc, source, confidence, qa_count, generator, generated
| esco_uri | esco_label | relation | onet_soc | source | confidence | qa_count | generator | generated |
|---|---|---|---|---|---|---|---|---|
| http://data.europa.eu/esco/skill/a57a54b6-2f2e-43e4-9621-b52f4a63cb08 | LDAP | optional | 15-2051.00 | model-knowledge | high | 0 | gemma3:27b (prompt-designed and spot-checked by Claude) | 2026-07-10 |
LDAP — Data Scientist
As a Data Scientist, 'LDAP' isn’t about writing LDAP queries as an end in itself; it's about understanding where user & system metadata lives and how to access it for feature engineering or data enrichment. Think of scenarios like building fraud detection models – you might need attributes from Active Directory (often accessed via LDAP) like employee role, department, creation date, last login time, etc., to supplement transaction data. Or, in personalization tasks, understanding user groups/permissions can inform recommendation engines. You'll rarely be crafting complex search filters directly; instead, you’ll likely interact with Python libraries (like python-ldap) or APIs that abstract the LDAP interaction.
Weitere Anreicherung
Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).