ktx AI Data Agents Context

Skill by ara.so — AI Agent Skills collection.

ktx is an executable context layer that teaches AI agents (Claude Code, Codex, Cursor, OpenCode) how to query your data warehouse accurately. It automatically builds and maintains a semantic layer from your warehouse metadata, dbt projects, LookML, Looker, Metabase, and wiki content - then serves it to agents through CLI and MCP tools with full-text and semantic search.

What ktx Does

Learns from company knowledge: Ingests wiki content, dbt docs, Looker, Metabase - organizes it, removes duplicates, flags contradictions
Maps the data stack: Samples tables, detects joinable columns, annotates sources so agents write better queries
Builds a semantic layer: Combines raw tables and metrics through a join graph that resolves chasm and fan traps automatically
Serves agents at execution: Exposes CLI and MCP tools for Claude Code, Codex, Cursor, OpenCode to query context

Installation

Global Installation

bash

npm install -g @kaelio/ktx

Project-Specific Installation

bash

npm install --save-dev @kaelio/ktx

Or with pnpm:

bash

pnpm add -D @kaelio/ktx

Initial Setup

Interactive Setup (Recommended)

bash

ktx setup

This command:

Creates or resumes a local ktx project
Configures LLM and embedding providers
Configures database connections
Configures context sources (dbt, Looker, Metabase, Notion)
Builds initial context
Installs agent integration (MCP or Skills)

Check Project Status

bash

ktx status

Example output:

text

ktx project: /home/user/analytics
Project ready: yes
LLM ready: yes (claude-sonnet-4-6)
Embeddings ready: yes (text-embedding-3-small)
Databases configured: yes (warehouse)
Context sources configured: yes (dbt_main)
ktx context built: yes
Agent integration ready: yes (codex:project)

Configuration

Project Structure

text

my-project/
├── ktx.yaml                         # Project configuration
├── semantic-layer/<connection-id>/  # YAML semantic sources
├── wiki/global/                     # Shared business context
├── wiki/user/<user-id>/             # User-scoped notes
├── raw-sources/<connection-id>/     # Ingest artifacts and reports
└── .ktx/                            # Local state and secrets (git-ignored)

ktx.yaml Example

yaml

version: 1
project_id: analytics-warehouse
llm:
  provider: anthropic
  model: claude-sonnet-4-6
embeddings:
  provider: openai
  model: text-embedding-3-small

databases:
  warehouse:
    type: postgres
    host: localhost
    port: 5432
    database: analytics
    user: readonly_user
    # Password stored in .ktx/secrets.yaml or env var

context_sources:
  dbt_main:
    type: dbt
    project_dir: ./dbt
    profiles_dir: ~/.dbt
  
  notion_docs:
    type: notion
    # Token stored in .ktx/secrets.yaml or env var NOTION_TOKEN

Environment Variables

ktx respects these environment variables:

bash

# LLM configuration
export ANTHROPIC_API_KEY=your_key_here
export OPENAI_API_KEY=your_key_here

# Database credentials
export DB_PASSWORD=your_db_password

# Context sources
export NOTION_TOKEN=your_notion_token
export DBT_PROFILES_DIR=~/.dbt

# Project resolution
export KTX_PROJECT_DIR=/path/to/project

Key Commands

Building Context

bash

# Build context from all configured sources
ktx ingest

# Build from specific connection
ktx ingest --connection warehouse

# Rebuild everything from scratch
ktx ingest --rebuild

Searching Context

bash

# Search semantic layer (metrics, dimensions, sources)
ktx sl "revenue"
ktx sl "customer lifetime value"

# Search wiki content
ktx wiki "refund policy"
ktx wiki "revenue recognition rules"

# Combined search
ktx search "monthly recurring revenue"

MCP Server (for AI Agents)

bash

# Start MCP server for agent integration
ktx mcp start

# Start with custom project directory
ktx mcp start --project-dir /path/to/project

# List available MCP tools
ktx mcp tools

Managing Semantic Layer

bash

# Validate semantic layer YAML
ktx sl validate

# List all semantic sources
ktx sl list

# Show specific metric details
ktx sl show monthly_revenue

Database Connection Examples

PostgreSQL

yaml

databases:
  warehouse:
    type: postgres
    host: localhost
    port: 5432
    database: analytics
    user: readonly_user
    ssl: true

Snowflake

yaml

databases:
  snowflake_prod:
    type: snowflake
    account: xy12345.us-east-1
    warehouse: COMPUTE_WH
    database: ANALYTICS
    schema: PUBLIC
    user: readonly_user
    role: ANALYST

BigQuery

yaml

databases:
  bigquery_prod:
    type: bigquery
    project_id: my-gcp-project
    dataset: analytics
    credentials_file: /path/to/service-account.json

ClickHouse

yaml

databases:
  clickhouse:
    type: clickhouse
    host: localhost
    port: 9000
    database: analytics
    user: readonly

Context Source Examples

dbt

yaml

context_sources:
  dbt_main:
    type: dbt
    project_dir: ./dbt
    profiles_dir: ~/.dbt
    target: prod

LookML

yaml

context_sources:
  looker:
    type: lookml
    project_dir: ./looker-project
    models:
      - sales
      - marketing

Metabase

yaml

context_sources:
  metabase:
    type: metabase
    url: https://metabase.company.com
    # Token in .ktx/secrets.yaml or METABASE_TOKEN env var
    collections:
      - Sales Dashboards
      - Marketing Analytics

Notion

yaml

context_sources:
  notion_wiki:
    type: notion
    # Token in .ktx/secrets.yaml or NOTION_TOKEN env var
    page_ids:
      - abc123def456
      - ghi789jkl012

Semantic Layer YAML

Defining a Metric

yaml

# semantic-layer/warehouse/metrics/revenue.yaml
type: metric
name: monthly_revenue
description: Total revenue aggregated by month
sql: |
  SELECT 
    DATE_TRUNC('month', order_date) as month,
    SUM(amount) as revenue
  FROM orders
  WHERE status = 'completed'
  GROUP BY 1
dimensions:
  - month
measures:
  - revenue
tags:
  - finance
  - reporting

Defining a Dimension

yaml

# semantic-layer/warehouse/dimensions/customer_segment.yaml
type: dimension
name: customer_segment
description: Customer segmentation based on lifetime value
sql: |
  CASE
    WHEN lifetime_value >= 10000 THEN 'Enterprise'
    WHEN lifetime_value >= 1000 THEN 'Mid-Market'
    ELSE 'SMB'
  END
source_table: customers

Defining a Joinable Source

yaml

# semantic-layer/warehouse/sources/orders.yaml
type: source
name: orders
description: All customer orders
table: public.orders
primary_key: order_id
joins:
  - to: customers
    type: many_to_one
    on: orders.customer_id = customers.customer_id
columns:
  - name: order_id
    type: integer
    description: Unique order identifier
  - name: customer_id
    type: integer
    description: Foreign key to customers
  - name: amount
    type: decimal
    description: Order total in USD
  - name: order_date
    type: timestamp
    description: When order was placed

Agent Integration

Using with Claude Code

After running

ktx setup

, ktx installs MCP integration automatically. The MCP server provides these tools:

```
ktx_search_semantic_layer
```
: Search metrics, dimensions, sources
```
ktx_search_wiki
```
: Search wiki content
```
ktx_get_metric
```
: Get full metric definition
```
ktx_query_preview
```
: Preview SQL for a semantic query

In Claude Code:

text

Use ktx to find our revenue metrics
What wiki pages discuss refund policies?
Show me the SQL for monthly_recurring_revenue metric

Using with Codex

bash

# Install ktx skill
npx skills add Kaelio/ktx --skill ktx

# Then in your project
ktx setup

Codex can now use ktx tools:

text

Query our warehouse for Q1 2024 revenue by customer segment
Find documentation about our churn calculation

Using with Cursor or OpenCode

Start the MCP server manually:

bash

ktx mcp start

Then configure your agent's MCP settings to connect to the ktx server.

Common Workflows

Setting Up a New Analytics Project

bash

# 1. Install ktx
npm install -g @kaelio/ktx

# 2. Navigate to your project
cd /path/to/analytics-project

# 3. Run interactive setup
ktx setup

# 4. Verify configuration
ktx status

# 5. Build initial context
ktx ingest

# 6. Test search
ktx sl "revenue"
ktx wiki "metric definitions"

Updating Context After Schema Changes

bash

# Rebuild context for specific connection
ktx ingest --connection warehouse --rebuild

# Or rebuild everything
ktx ingest --rebuild

Adding a New Metric

bash

# 1. Create metric YAML file
cat > semantic-layer/warehouse/metrics/arr.yaml <<EOF
type: metric
name: annual_recurring_revenue
description: ARR calculated from active subscriptions
sql: |
  SELECT 
    DATE_TRUNC('year', subscription_start) as year,
    SUM(monthly_amount * 12) as arr
  FROM subscriptions
  WHERE status = 'active'
  GROUP BY 1
dimensions:
  - year
measures:
  - arr
tags:
  - finance
  - saas
EOF

# 2. Validate
ktx sl validate

# 3. Rebuild context
ktx ingest --connection warehouse

# 4. Test search
ktx sl "annual recurring revenue"

Writing Wiki Documentation

bash

# Create a wiki page
mkdir -p wiki/global/metrics
cat > wiki/global/metrics/revenue-recognition.md <<EOF
# Revenue Recognition

## Overview
Our revenue recognition follows ASC 606 guidelines.

## Key Rules
1. Revenue is recognized when service is delivered
2. Refunds are deducted in the month issued
3. Deferred revenue is amortized monthly

## Related Metrics
- monthly_revenue
- deferred_revenue
- recognized_revenue
EOF

# Rebuild wiki index
ktx ingest

# Search it
ktx wiki "revenue recognition"

Troubleshooting

ktx status shows "LLM ready: no"

Configure LLM provider:

bash

ktx setup
# Follow prompts to configure Anthropic, OpenAI, or Vertex AI

Or set environment variable:

bash

export ANTHROPIC_API_KEY=your_key_here
ktx status

Database connection fails

Test connection:

bash

# Check credentials in ktx.yaml or .ktx/secrets.yaml
ktx ingest --connection warehouse --verbose

Common fixes:

Verify user has SELECT permissions on all tables
Check firewall/network access to database host
Ensure SSL settings match your database requirements
For Snowflake: verify warehouse is running

Semantic layer validation errors

bash

# Validate specific file
ktx sl validate semantic-layer/warehouse/metrics/revenue.yaml

# Common issues:
# - Invalid YAML syntax
# - Missing required fields (name, type, sql)
# - Referenced tables don't exist
# - Invalid join definitions

MCP server won't start

bash

# Check if project is properly initialized
ktx status

# Ensure context is built
ktx ingest

# Start with verbose logging
ktx mcp start --verbose

# Check for port conflicts (default: 3000)
ktx mcp start --port 3001

Search returns no results

bash

# Rebuild search indexes
ktx ingest --rebuild

# Check if context sources are configured
cat ktx.yaml

# Verify semantic layer files exist
ls -la semantic-layer/

# Verify wiki files exist
ls -la wiki/global/

Agent can't access ktx tools

Verify MCP server is running:
```
ktx status
```
Check agent's MCP configuration points to correct ktx server
Restart agent after starting MCP server
Check MCP server logs:
```
ktx mcp start --verbose
```

Best Practices

1. Organize Semantic Layer by Domain

text

semantic-layer/
  warehouse/
    metrics/
      finance/
        revenue.yaml
        arr.yaml
      product/
        dau.yaml
        retention.yaml
    dimensions/
      time.yaml
      geography.yaml
    sources/
      orders.yaml
      customers.yaml

2. Document Metric Business Logic in Wiki

Link semantic layer metrics to wiki documentation:

yaml

# semantic-layer/warehouse/metrics/revenue.yaml
type: metric
name: monthly_revenue
description: See wiki/global/metrics/revenue-recognition.md for full details
# ... rest of definition

3. Use Tags for Discovery

yaml

type: metric
name: customer_churn_rate
tags:
  - saas
  - retention
  - executive-dashboard
  - monthly-reporting

4. Version Control Everything

bash

# .gitignore
.ktx/secrets.yaml
.ktx/cache/
.ktx/*.db

Commit:

```
ktx.yaml
```
```
semantic-layer/
```
```
wiki/global/
```

5. Set Up Read-Only Database Users

ktx never writes to your database, but use dedicated read-only credentials:

sql

-- PostgreSQL example
CREATE USER ktx_readonly WITH PASSWORD 'secure_password';
GRANT CONNECT ON DATABASE analytics TO ktx_readonly;
GRANT USAGE ON SCHEMA public TO ktx_readonly;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO ktx_readonly;
ALTER DEFAULT PRIVILEGES IN SCHEMA public 
  GRANT SELECT ON TABLES TO ktx_readonly;

6. Regular Context Updates

Set up a cron job or CI workflow:

bash

#!/bin/bash
# daily-context-update.sh
cd /path/to/project
ktx ingest --connection warehouse
ktx sl validate

Advanced Usage

Custom LLM Configuration

yaml

llm:
  provider: vertex
  model: claude-3-5-sonnet-v2@20250219
  project_id: my-gcp-project
  region: us-central1
  temperature: 0.1
  max_tokens: 4096

Multiple Database Connections

yaml

databases:
  prod_warehouse:
    type: snowflake
    account: prod.us-east-1
    database: ANALYTICS_PROD
  
  staging_warehouse:
    type: snowflake
    account: staging.us-east-1
    database: ANALYTICS_STAGING
  
  postgres_app:
    type: postgres
    host: app-db.internal
    database: application

Programmatic Usage (TypeScript)

typescript

import { KtxContext } from '@kaelio/ktx';

const ctx = await KtxContext.load('/path/to/project');

// Search semantic layer
const results = await ctx.searchSemanticLayer('revenue');
console.log(results);

// Get metric definition
const metric = await ctx.getMetric('monthly_revenue');
console.log(metric.sql);

// Search wiki
const wikiResults = await ctx.searchWiki('refund policy');
console.log(wikiResults);

Resources

Documentation: https://docs.kaelio.com/ktx
GitHub: https://github.com/Kaelio/ktx
Slack Community: https://join.slack.com/t/ktxcommunity/shared_invite/zt-3y9b44m1x-LVyNNJD5nwaZHq4XS29LMQ
CLI Reference: https://docs.kaelio.com/ktx/docs/cli-reference/ktx
Agent Setup Guide: https://docs.kaelio.com/ktx/docs/ai-resources/agent-quickstart

ktx-ai-data-agents-context

NPX Install

Tags

SKILL.md Content

ktx AI Data Agents Context

What ktx Does

Installation

Global Installation

Project-Specific Installation

Initial Setup

Interactive Setup (Recommended)

Check Project Status

Configuration

Project Structure

ktx.yaml Example

Environment Variables

Key Commands

Building Context

Searching Context

MCP Server (for AI Agents)

Managing Semantic Layer

Database Connection Examples

PostgreSQL

Snowflake

BigQuery

ClickHouse

Context Source Examples

dbt

LookML

Metabase

Notion

Semantic Layer YAML

Defining a Metric

Defining a Dimension

Defining a Joinable Source

Agent Integration

Using with Claude Code

Using with Codex

Using with Cursor or OpenCode

Common Workflows

Setting Up a New Analytics Project

Updating Context After Schema Changes

Adding a New Metric

Writing Wiki Documentation

Troubleshooting

ktx status shows "LLM ready: no"

Database connection fails

Semantic layer validation errors

MCP server won't start

Search returns no results

Agent can't access ktx tools

Best Practices

1. Organize Semantic Layer by Domain

2. Document Metric Business Logic in Wiki

3. Use Tags for Discovery

4. Version Control Everything

5. Set Up Read-Only Database Users

6. Regular Context Updates

Advanced Usage

Custom LLM Configuration

Multiple Database Connections

Programmatic Usage (TypeScript)

Resources