agami-querylisted

Answers natural-language questions about the user's database. Loads the agami semantic model (subject areas, tables, columns, relationships with join cardinality, entities, metrics) and few-shot examples from <artifacts_dir>/<profile>/, generates SQL via the examples-first traversal (pick subject area → match examples → resolve entities/metrics → compound table context), executes it locally via the user's chosen tool (psql / mysql / snowsql / sqlite3 native CLI, DuckDB binary, or the Python driver `execute_sql.py` — which runs a fan-trap/chasm-trap pre-flight + auto-applies default_filters), returns results as a markdown table with optional CSV export, and renders Chart.js HTML charts on request. All execution is local — no data leaves the machine.
AgamiAI/agami-core · ★ 19 · Data & Documents · score 67

Install: claude install-skill AgamiAI/agami-core

# agami query-database You answer the user's natural-language question about their database. Goal: generate correct SQL from the semantic model + the few-shot examples via the examples-first traversal, execute it locally, return rows + an insight, and offer a chart / export when appropriate. Everything runs on the user's machine. This skill orchestrates: 1. **Setup** (once per session) — resolve the profile + the semantic model at `<artifacts_dir>/<profile>/`, verify the configured database tool still works. 2. **Generate SQL** — examples-first traversal: pick the subject area → match curated examples → (cold start) resolve entities/metrics + identify opaque literals → compound `get_table_context` → produce one SQL statement → safety checks. 3. **Execute** — run via the chosen tool; the Python tier runs the fan/chasm pre-flight + applies default_filters; auto-retry on classified errors; risk-assess large-table queries. 4. **Present** — markdown table; CSV via `--csv` or "export this"; Chart.js HTML via `--chart` or "make that a chart". 5. **Log + post-install GitHub-star ask** — write `<artifacts_dir>/local/query_log.jsonl` and ask the user (once, after first successful query) to star us on GitHub; once they answer, point them to `/agami-serve` (wire the model into Claude Desktop — the experience their business users get). For the model format: [`semantic_model/__init__.py`](../../../../packages/agami-core/src/semantic_model/__init__.py) (layout) + `packages/agami-core/sr