Migrate all repos into monorepo context folders

Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
      Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
      Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)

Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.

bahn/teamlandkarte-mcp/.github/skills/scoring-interpretation/SKILL.md

@@ -0,0 +1,73 @@
+---
+name: scoring-interpretation
+description: >
+  Interpret and explain matching scores from capacity and task searches.
+  Use this skill when the user asks why a result scored high or low,
+  what the scores mean, or how to improve matching results.
+---
+
+# Scoring Interpretation
+
+## Score Components
+
+Every matching result has three scores:
+- **Role Score** (0.0–1.0): Cosine similarity between required role and candidate's role embedding
+- **Competence Score** (0.0–1.0): Aggregated similarity across all required competences
+- **Overall Score**: `role_weight × role_score + competence_weight × competence_score`
+
+Weights are configured in `config.toml` under `[matching]`.
+
+## Result Categories
+
+Results are grouped by overall score into:
+- **Top**: Highest scoring matches
+- **Good**: Strong matches
+- **Partial**: Some overlap but gaps
+- **Low**: Weak matches
+- **Irrelevant**: Very low or no meaningful overlap
+
+Thresholds are configurable via `matching.category_thresholds`.
+
+## Availability
+
+Availability is shown as **overlap percentage** against the provided date range:
+- 100% = candidate fully covers the requested period
+- <100% = partial overlap
+- `is_fully_available=true` filter excludes partial matches
+
+## Embedding Mode (default)
+
+- Cosine similarity between embedding vectors
+- Semantic: "ML" and "Machine Learning" will have high similarity
+- Range: 0.0 (unrelated) to 1.0 (identical meaning)
+
+## BM25 + RRF Mode (`use_bm25_search = true`)
+
+- Lexical token matching, NOT semantic
+- Score of 0.0 = no shared tokens (not a semantic distance)
+- "Python" matches "Python" perfectly
+- "ML" does NOT match "Machine Learning" (no shared tokens)
+- Eliminates false positives but may miss valid synonyms
+
+### With Auto-Tagging (`use_auto_tagging = true`)
+
+- LLM expands candidate competences with canonical equivalents
+- "ML" → "Machine Learning" expansion happens before BM25 scoring
+- Positive scores may reflect LLM-inferred equivalence
+- If LLM fails, scoring continues on unexpanded list (no failure)
+
+## Common Questions
+
+**"Why did this person score 0.0 on competences?"**
+- BM25 mode: no lexical overlap between required and candidate competences
+- Embedding mode: very different semantic meaning (rare for 0.0)
+
+**"Why is a good match ranked Low?"**
+- Check if role mismatch is dragging down overall score
+- Check weights: high role_weight penalizes role mismatches heavily
+
+**"How to get better results?"**
+- Add more specific competences
+- Use canonical terms (full names, not abbreviations in BM25 mode)
+- Broaden the time range if too restrictive
+- Consider enabling auto-tagging for synonym coverage