explorelisted

Use this whenever you need to know what is actually in a database, warehouse, or DuckDB file before you trust it: ranked inventory of what exists, column profiles, PII detection, grain and data-quality problems, verified join inference, Mermaid ER diagrams, guarded ad-hoc SQL probes, and k-means segmentation, producing a draft map without dumping the whole schema into context. Trigger it on an unmet precondition, not on any particular phrasing: if you are about to write or fix SQL against tables whose columns, types, grain, or join keys you have not verified in this session, use this FIRST. That includes dbt work: building a staging or mart model, fixing a broken model, or debugging wrong numbers, whenever the ticket names source tables without spelling out their schema. It also applies mid-task: if you are partway through and hit a table you have not inspected, stop and use this rather than guessing column names or firing off one-off SELECTs. Also use it for direct questions like "what's in my duckdb", "whic
exmergo/dex · ★ 21 · AI & Automation · score 74

Install: claude install-skill exmergo/dex

# Explore Make sense of a warehouse or a local DuckDB database the way an analytics engineer does: rank what matters, drill selectively, and persist a draft map. This is the flagship, fully read-only skill. It absorbs profiling and relationship inference as capabilities; they are not separate skills. ## How to drive it Run the engine through the wrapper. It prints one sanitized JSON envelope and nothing else; read the envelope and decide the next step. ```bash uv run "${CLAUDE_SKILL_DIR}/scripts/run.py" <subcommand> [flags] ``` Subcommands, in the usual order: 1. `connect test --path <file.duckdb>` confirms a read-only connection and reports capabilities. 2. `explore inventory --rank` returns a ranked object summary (counts and sizes, never rows). 3. `explore profile <objects>` (space- or comma-separated) returns column profiles, PII flags recorded as (column, category, confidence) and never example values, plus candidate keys, the likely grain, and data-quality warnings (e.g. a non-unique id that will fan out on joins). A generic `*_name` flag's confidence is refined by value-shape evidence from the same scan, in both directions: person-shaped values corroborate it, a closed reference vocabulary or long labels de-rate it below the firewall's blocking threshold, and missing evidence changes nothing (the flag itself is never removed). Distinct counts are approximate for scale, but any column that looks unique within approximation noise