Explore

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.

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 is escalated to an exact COUNT(DISTINCT) (distinct_count_exact: true), so uniqueness and grain verdicts rest on proof; a ~ prefix in a warning marks a count that is still approximate. A requested object whose cached profile is still fresh (same connector, schema unchanged, within profile_freshness_hours, default 24) is served from the cache (cache_hit_count) instead of re-scanned, so profiling a table map just wrote costs nothing to spend; pass --refresh to force a re-scan when the source changed in a way the free metadata check cannot see.
4. explore relationships returns inferred and declared joins with confidences, plus notes explaining what the inference examined (so an empty list is meaningful). Add --verify to measure each inferred join with an aggregate overlap probe (orphan fraction, confidence adjusted).
5. explore map writes or updates the .dex/ cache and prints a summary (--verify works here too). Past 50 objects it profiles only the top 25 by rank and says so in notes (with skipped_count); pass --full to profile everything. On a re-map, objects skipped this run keep their prior profiles (carried_forward_count), each stamped with its own profiled_at so staleness is visible instead of column detail silently vanishing. A selected object whose cached profile is still fresh (same connector, schema unchanged, profiled within profile_freshness_hours, default 24) is reused without a re-scan (cache_hit_count), so re-runs cost nothing to spend; pass --refresh to force a full re-profile when the source changed in a way the free metadata check cannot see (e.g. rows changed but the schema did not). explore relationships and the standalone explore profile reuse fresh profiles the same way.
6. explore query "<SELECT ...>" answers an ad-hoc question the fixed commands don't cover: you write the SQL, the engine's query firewall refuses or bounds it. Requires the .dex/ cache (run map first). Results come back columnar and capped; a refusal names the offending column and the fix, so one rewrite is enough. Read ${CLAUDE_SKILL_DIR}/references/probe-playbook.md before writing a probe: it maps common questions to effective probe shapes.
7. explore cluster <object> [--features a,b,c] [-k N] runs k-means over a bounded sample of the object's numeric columns and returns the segment structure: per-cluster sizes and fractions, centroids (each coordinate is a cluster's mean of that feature, an aggregate), the silhouette score, and, when -k is omitted, the k it picked plus the silhouette sweep it chose from. Requires the .dex/ cache (run map/profile first) so features can be auto-selected from profiled numeric, non-PII, non-key columns; pass --features to choose them yourself (naming a PII column, or a key, opts it in deliberately, and only its mean is ever reported). A key is never a feature: its mean is meaningless, and a fact table is mostly keys plus a handful of measures, so clustering on them just partitions surrogate ranges. Keys are the unique columns, the columns that join out (from the joins map inferred), and the columns named like one; prefer map over a bare profile here, because without inferred joins a foreign key is caught only if its name gives it away. The notes name every excluded column, so check them before trusting a result. Two things the silhouette alone will not tell you, both of which the notes will. A cluster holding under 1% of the sample is an outlier pocket, not a segment, and it pushes the score up precisely because it sits so far out: report that as outlier detection, or re-run with -k to split the bulk. And on connectors that cannot seed a sample the draw changes per run, so two runs can disagree on k; the envelope's sample_repeatable says which case you are in, and comparing runs across different draws is meaningless. Only aggregates cross the boundary: the sample rows are clustered in-process and never enter context. On a metered connector it takes the same cost handshake as the scanning commands below (only the feature columns are scanned, and a dialect-aware sample clause reads a fraction), so surface the estimate and get a budget first. Needs the [cluster] extra (scikit-learn); the wrapper installs it automatically for this subcommand.
8. explore semantic list and explore semantic query reach the dbt semantic layer (metrics, dimensions, entities). list is discovery: which metrics exist and which dimensions each can be grouped by. query takes a --metric and a --group-by <entity__dim> (plus optional --where, --grain, and --limit) and returns a metric's values as a capped, columnar result. Two backends answer these, chosen by .dex/config.yml semantic.backend and overridable with --local / --api. --local renders the SQL with MetricFlow and executes it through dex's own connector and cost handshake, so cost is surfaced before spend (needs a dbt project and, for query, the [semantic] extra; list is a manifest read-view that needs neither). --api sends the query to a hosted dbt Cloud deployment (needs only a host, an environment id, and a DBT_SL_TOKEN, plus the [semantic-api] extra, no local project). The hosted backend is the one place the cost guard cannot apply: dbt Cloud executes server-side, so the result carries an explicit warning that spend is governed there, not by dex, and no --confirm is asked. Either way a PII-shaped grouped or filtered dimension (e.g. user__email) is refused before the query runs. This queries the layer; authoring it is transform's job.

Rules of engagement for query: prefer the fixed commands when they answer the question; one probe answers one question; batch related measures into a single query rather than issuing many; aggregates over PII-flagged columns must be measuring (COUNT, APPROX_COUNT_DISTINCT, AVG(LENGTH(...))), never value-carrying (MIN, ANY_VALUE, STRING_AGG). The FROM clause may unnest JSON and array columns in the connector's native idiom, which is the right way to explore schemaless data (for example "which keys appear across every row of this JSON column"): BigQuery t, UNNEST(JSON_KEYS(doc)) AS k, Snowflake t, LATERAL FLATTEN(input => doc) f, Databricks t LATERAL VIEW EXPLODE(json_object_keys(doc)) x AS k, Postgres t, jsonb_object_keys(doc) AS k, Redshift t, UNPIVOT t.doc AS v AT k, DuckDB t, UNNEST(json_keys(doc)) AS u(k). The unnested value must come from a column of a table in the query (bare, or through a JSON/array function); unnesting a subquery, another table, a literal, or a generator is refused, and the unnest's outputs inherit the source column's PII flags. A column whose flag was de-rated below the 0.5 blocking threshold projects normally, with an envelope warning naming it; treat the warning as information for the user, not an error to fix. If the user says a refused column is not personal data, recommend a pii_overrides entry in .dex/config.yml (fully qualified column, optional reason): it unblocks querying immediately, survives re-profiles, and is reviewable in git. Never hand-edit .dex/cache.json to clear a flag. Never fall back to raw Python or a database CLI to run SQL; the firewall path is the only sanctioned one.

Cloud and database targets (BigQuery, Snowflake, Databricks, Postgres, Redshift)

A remote warehouse or database replaces --path with connector config. Start with connect test --connector <name> (or set connector: plus the matching block in .dex/config.yml: bigquery: with project and a datasets allowlist, snowflake: with the pinned warehouse and a databases allowlist, databricks: with the pinned SQL warehouse and a catalogs allowlist, postgres: with a schemas allowlist, redshift: with the Serverless workgroup and a schemas allowlist). Credentials are discovered, never asked for: if the envelope reports missing or expired credentials, relay the fix it names (for BigQuery gcloud auth application-default login; for Snowflake a connections.toml entry or SNOWFLAKE_* env; for Databricks databricks auth login or DATABRICKS_* env; for Postgres DATABASE_URL, PG* env, or a pg_service.conf entry; for Redshift the AWS credential chain (aws configure, AWS_* env) or REDSHIFT_* env) and never ask the user to paste a key, token, or password.

On a metered connector, scanning commands (profile, map, relationships, query) run a two-step handshake. The first call returns needs_confirmation with an estimate in cost.estimate (and a per-table breakdown where relevant): an exact dry-run byte figure on BigQuery, a heuristic labeled estimate_quality: "heuristic" in warehouse-seconds on Snowflake (credits alongside), a floor labeled estimate_quality: "low" in warehouse-seconds on Databricks (DBUs alongside; it sharpens itself inside the confirmed budget), a heuristic in compute-seconds on Redshift (RPU-hours alongside; Serverless estimates carry the 60-second wake minimum once), and database-seconds on Postgres (no dollars; the guarded quantity is load on the operational database). Surface the estimate to the user in human units, get an explicit budget from them, and re-issue the same command with --confirm and --budget <magnitude> in the paradigm's unit. Never invent a budget the user did not agree to, and never retry with a raised budget on an over-ceiling refusal without asking. Metadata is free (connect test, inventory run immediately), and OK envelopes report actual spend under data.spend.

When an estimate is larger than the work deserves, narrow the scope rather than raise the budget. --scope (repeatable) bounds a command to part of the configured source allowlist, in the connector's own vocabulary: a dataset on BigQuery, a schema or database.schema on Snowflake, a catalog.schema on Databricks, a schema on Postgres or Redshift. It is free to resolve, it can only narrow what .dex/config.yml already allows, and a scope that names nothing is refused with the schemas that do exist listed. So explore map --scope <schema> is the first thing to reach for on a warehouse whose full map would be expensive.

Guardrails (enforced in the engine, not here)

• Read-only against data. The connection is opened read-only and generated SQL is SELECT-only. Never propose a write to source data.
• Sense-making, not enumeration. Rank and drill selectively; never paste a full schema into context.
• Profile, don't exfiltrate. Understanding comes from aggregates. PII is flagged, never surfaced, and the query firewall enforces it on your own SQL: values cross the envelope only from profiled columns whose flag is absent or below the blocking threshold, bounded and capped. Only a human's pii_overrides entry clears a flag entirely; never suggest weakening the detection.