data-inspect

name

data-inspect

description

Show the active dataset's schema — tables, columns, row counts, and relationships. Optionally drill into a specific table. Use this skill whenever the user invokes `/data` or `/data {table}`, or asks questions like "what tables do I have?", "show me the schema", "what's in this dataset?", "what columns are in the users table?", "show me table structure", "what data is available?", "list tables", "describe the data", "what's in my database?", or any request to inspect, browse, or understand the structure of the active dataset. Also trigger when users mention "schema", "columns", "tables", "data dictionary", "data structure", or when they need to understand what data they're working with before starting an analysis. This is a foundational command that should be offered proactively when users seem unsure about available data or table structure. DISAMBIGUATION: this is the `/data` SCHEMA inspector (structure — tables, columns, types, row counts). For a dataset-wide health + relationships overview ("tell me about this data"), use `data-map`; for interactive browsing/sampling, use `/explore`; for deep statistical profiling (distributions, anomalies), use `data-profiling`.

Skill: Data Inspect

Purpose

Show the active dataset's schema — tables, columns, row counts, and relationships. Optionally drill into a specific table.

When to Use

Invoke as /data to see the full schema summary, or /data {table} to see column details for a specific table.

Instructions

CRITICAL: Always Start Here

Before doing ANYTHING else:

1. Read .knowledge/active.yaml to determine the active dataset name
2. If no active dataset exists, jump to Mode 3 (No Active Dataset)
3. Otherwise, read .knowledge/datasets/{active}/schema.md for schema information

Why this matters: Users often have multiple datasets connected. You MUST use the active one from the config file, never guess or use a different dataset.

---

Mode 1: /data (full schema overview)

When: User invokes /data or asks "what tables do I have?" / "show me the schema"

Steps:

1. ✅ Confirm you've already read .knowledge/active.yaml and schema.md (see above)
2. Extract from schema.md:
   • Dataset display name
   • Connection type and location
   • Table list with: name, row count, column count, primary key
3. Display in this condensed format:

Active Dataset: {display_name}
Connection: {type} ({database}.{schema} or file path)

Tables:
  users          ~50,000 rows   8 columns   user_id (PK)
  products           500 rows   7 columns   product_id (PK)
  events        ~6.5M rows     9 columns   event_id (PK)
  sessions       ~1.4M rows    8 columns   session_id (PK)
  orders        ~30-50K rows   6 columns   order_id (PK)
  order_items         — rows   4 columns   order_id + product_id (composite PK)

Use `/data {table}` for column details.

Format notes:

• Left-align table names
• Show approximate row counts (use ~ for estimates)
• Show column count
• Show primary key or composite key
• Keep it visually scannable — this is a quick reference, not exhaustive detail

---

Mode 2: /data {table} (table detail)

When: User invokes /data {table} or asks "what columns are in X?" / "show me the X table structure"

Steps:

1. ✅ Confirm you've already read .knowledge/active.yaml and schema.md (see above)
2. Find the section for the requested table in schema.md
3. If table doesn't exist: Jump to Mode 4 (Table Not Found)
4. If table exists: Display:
   • Table name and description
   • Row count
   • Full column listing: name, type, nullable, description
   • Primary key(s)
   • Foreign key relationships (both FROM this table and TO this table)
   • Any important notes about the table (grain, completeness, quirks)

Format example:

Active Dataset: {dataset_name}
Table: users

Description: User dimension table with demographics and signup info

Row count: ~25,000 rows
Primary Key: user_id

Columns:
  user_id          BIGINT       NOT NULL    Unique user identifier
  email            VARCHAR      NOT NULL    User email address
  signup_date      DATE         NULL        Date user first registered
  country          VARCHAR      NULL        User's country
  membership_tier  VARCHAR      NULL        Premium, Standard, Free

Relationships:
  ← orders.customer_id          (one user, many orders)
  ← events.user_id              (one user, many events)
  → memberships.user_id         (join for membership details)

Use `/data {another_table}` to inspect another table.

---

Mode 3: No Active Dataset

When: .knowledge/active.yaml has no active_dataset field OR the dataset directory doesn't exist

Display:

No active dataset configured.

To get started:
• Run `/connect-data` to connect a new dataset
• Run `/datasets` to see all available datasets
• Run `/switch-dataset {name}` to activate an existing dataset

Do NOT: Try to query databases, load CSV files, or guess which data source to use. Without an active dataset, halt and prompt the user.

---

Mode 4: Table Not Found

When: User requests /data {table} but the table doesn't exist in the active dataset's schema.md

Steps:

1. Confirm the table truly doesn't exist (check schema.md thoroughly, look for typos/case differences)
2. Display a helpful error message:

Table '{table}' not found in {dataset_name}.

Available tables:
  users, orders, products, events, sessions

Did you mean:
• /data {closest_match}
• /switch-dataset {other_dataset} if you're looking for different data
• /connect-data if the table should exist but isn't loaded

Use `/data` to see the full schema.

Do NOT: Query databases or try to load data from other sources. The skill reads from cached schema files only.

Anti-Patterns

1. Never query the database just to show schema — read from the cached schema.md file for speed. Schema files are pre-generated during dataset connection and profiling.

2. Never show the full schema.md raw — always format into the condensed table view. Users want quick scannable reference, not walls of markdown.

3. Never guess which dataset to use — ALWAYS read .knowledge/active.yaml first. Do not load data from other directories like data/sales/ or data/practice/ unless that's explicitly what active.yaml points to.

4. Never query actual data — this skill shows structure only (schema, relationships). For data exploration, use the /explore skill or Data Explorer agent.

5. Never fabricate table information — if schema.md doesn't have row counts, say "~rows not profiled". If descriptions are missing, show what's available. Don't make up details.