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/prompts/configure-matching.prompt.md

@@ -0,0 +1,49 @@
+---
+description: Help configure the Teamlandkarte matching system — weights, thresholds, similarity strategy, caching, and Azure OpenAI settings.
+---
+
+$ARGUMENTS
+
+## Configuration Guide
+
+All runtime configuration lives in `config.toml`. See `config.toml.example` for the full schema.
+
+### Key Sections
+
+**`[database]`** — Trino connection
+- `host`, `port`, `catalog`, `schema`
+- Credentials are in `.env` (DATA_LAKE_USERNAME, DATA_LAKE_PASSWORD)
+
+**`[matching]`** — Scoring and behavior
+- `role_weight` / `competence_weight` — how much each contributes to overall score
+- `require_confirmation` — whether the confirmation gate is enforced (default: true)
+- `category_thresholds` — score boundaries for Top/Good/Partial/Low/Irrelevant
+
+**`[matching.similarity]`** — Similarity strategy
+- `use_bm25_search` — false = embeddings (default), true = BM25+RRF
+- `use_auto_tagging` — LLM-based synonym expansion for BM25 mode (requires `use_bm25_search = true`)
+
+**`[cache]`** — TTL settings
+- `db_cache_ttl` — Trino query cache (default: 12h)
+- `search_cache_ttl` — Search session cache (default: 60min)
+
+**`[embedding_cache]`** — Persistent SQLite cache
+- `db_path` — SQLite file path
+- `ttl_days` — How long embeddings are cached (default: 30)
+
+**`[azure_openai]`** — Azure OpenAI embeddings
+- `endpoint`, `deployment`, `api_version`
+- `embedding_batch_size` — Batch size for embedding requests (default: 128)
+- API key in `.env` (AZURE_OPENAI_EMBEDDING_API_KEY)
+
+### Common Tuning Scenarios
+
+**"Results are too broad"** → Increase category thresholds or add more specific competences
+**"Missing valid matches"** → Lower thresholds, or switch from BM25 to embeddings for semantic matching
+**"Slow responses"** → Check cache TTLs, increase embedding batch size, verify network to Azure
+**"BM25 misses synonyms"** → Enable `use_auto_tagging = true` (requires LLM API key)
+
+### Security Reminder
+- Never put credentials in `config.toml`
+- All secrets go in `.env` only
+- `config.toml` is not in version control (see `.gitignore`)