ktx-ai-data-agents-context

by Kaelioโœ“ Verified

ktx is an executable context layer for data and analytics agents ๐Ÿ™ Allow Claude Code, Codex, and any AI agent to query data accurately through MCP with skills, memory and a semantic layer

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TypeScript
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8/23/2026
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โš ๏ธ Third-Party Software Notice

This skill is third-party open-source software developed and hosted independently on GitHub. SkillTip is an informational directory and does not control or maintain the underlying repository. Any security checks displayed are automated and limited in scope. Review the source code before installing.

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Installation

Add to your Claude Code skills directory:

# Add to your Claude Code skills
git clone https://github.com/Kaelio/ktx-ai-data-agents-context

Getting Started

Guides for using skills like ktx-ai-data-agents-context.

Security Report

Verified

Last scanned: โ€”

{
  "status": "PASSED",
  "issues": []
}

README.md

ktx

The context layer for data agents

npm version Codecov Tests Documentation Join the ktx Slack community License Y Combinator P25

Quickstart ยท CLI Reference ยท Agent Setup ยท Slack

Built and maintained by Kaelio


ktx is a self-improving context layer that teaches agents how to query your warehouse accurately - from approved metric definitions, joinable columns, and business knowledge it builds and maintains for you.

[!NOTE] Run ktx with your own LLM API keys or a local agent sign-in โ€” a Claude Pro/Max subscription through Claude Code, or your local Codex authentication. No extra usage billing from ktx.

Watch the ktx launch video (1:56)

Ingestion: ktx ingests databases, BI tools, modeling code, and docs through its context engine (source connectors, context builder, reconciliation, validation) into wiki Markdown and semantic-layer YAML

Serving: an agent queries ktx through MCP, which searches the wiki and semantic layer, returns approved metrics, and compiles them into read-only SQL run against the warehouse

Why ktx

General-purpose agents struggle on data tasks. They re-explore your warehouse on every question, invent their own metric logic, and return numbers that don't match approved definitions.

Traditional semantic layers don't fix this. They demand constant manual upkeep and don't absorb the rest of your company's knowledge.

ktx does both, automatically:

  • Learns from company knowledge. Ingests wiki content, organizes it, removes duplicates, and flags contradictions for human review.
  • Maps the data stack. Samples tables, captures metadata and usage patterns, detects joinable columns, and annotates sources so agents write better queries.
  • Builds a semantic layer. Combines raw tables and high-level metrics through a join graph that automatically resolves chasm and fan traps, so agents fetch metrics declaratively instead of rewriting canonical SQL each time.
  • Serves agents at execution. Exposes CLI and MCP tools with combined full-text and semantic search across wiki and semantic-layer entities.

How ktx compares

General-purpose agentTraditional semantic layerktx
Builds warehouse context automaticallyโ€”โ€”โœ“
Detects joinable columns + resolves fan/chasm trapsโ€”Manualโœ“
Approved, reusable metric definitionsโ€”โœ“โœ“
Absorbs wiki / Notion / team knowledgeโ€”โ€”โœ“
Flags contradictions across sourcesโ€”โ€”โœ“
Ships CLI + MCP for agent executionPartialโ€”โœ“
Read-only by designn/an/aโœ“

Who is ktx for

Use ktx if you:

  • Want agents like Claude Code, Codex, Cursor, or OpenCode to query your warehouse with approved metric definitions
  • Have business knowledge scattered across dbt, Looker, Metabase, Notion, and team wikis
  • Need agents to reuse canonical SQL instead of inventing it on every prompt

Skip ktx if you:

  • You don't have a SQL warehouse - ktx sits on top of one
  • You only need one ad-hoc query - psql or a notebook will do

Works with PostgreSQL, Snowflake, BigQuery, ClickHouse, MySQL, SQL Server, SQLite, DuckDB, Amazon Athena, and MongoDB. Integrates with dbt, MetricFlow, LookML, Looker, Metabase, Sigma, Notion, and Google Drive.

Quick Start

npm install -g @kaelio/ktx
ktx setup
ktx status

ktx setup creates or resumes a local ktx project, configures providers and connections, builds context, and installs agent integration.

Example ktx status after setup:

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)

[!TIP] Already using an agent? Ask Claude Code, Codex, Cursor, or OpenCode from your project directory:

Run npx skills add Kaelio/ktx --skill ktx and use the ktx skill to install
and configure ktx in this project.

[!IMPORTANT] If ktx status prints ktx mcp start --project-dir ..., run it before opening your agent client.

Upgrading

Re-run the global install with the @latest tag:

npm install -g @kaelio/ktx@latest

First commands

CommandPurpose
ktx setupCreate, resume, or update a ktx project
ktx statusCheck project readiness
ktx ingestBuild context for every configured connection
ktx sl "revenue"Search semantic sources
ktx wiki "refund policy"Search local wiki pages
ktx mcp startStart the MCP server for agent clients

See the CLI Reference for every command, flag, and option.

Project Layout

my-project/
โ”œโ”€โ”€ ktx.yaml                         # Project configuration
โ”œโ”€โ”€ semantic-layer/<connection-id>/  # YAML semantic sources
โ”œโ”€โ”€ wiki/global/                     # Shared business context
โ”œโ”€โ”€ wiki/user/<user-id>/             # User-scoped notes
โ”œโ”€โ”€ raw-sources/<connection-id>/     # Ingest artifacts and reports
โ””โ”€โ”€ .ktx/                            # Local state and secrets, git-ignored

Commit ktx.yaml, semantic-layer/, and wiki/. Keep .ktx/ local.

Project resolution defaults to KTX_PROJECT_DIR, then the nearest ktx.yaml, then the current directory. Pass --project-dir <path> when scripting.

FAQ

  • Does ktx send my schema or query results to a hosted service? No. ktx runs locally. The only data leaving your machine is what you send to the LLM provider you configured.
  • Which LLM backends are supported? Anthropic API, Google Vertex AI, AI Gateway, the local Claude Code session through the Claude Agent SDK, and your local Codex authentication through the Codex SDK. See LLM configuration.
  • How is ktx different from a dbt or MetricFlow semantic layer? ktx ingests those layers and combines them with raw-table introspection and wiki content. Agents get one searchable surface instead of three disconnected ones - and ktx flags contradictions across sources.
  • Does ktx need a running server? There is no hosted service. The local MCP daemon runs on demand via ktx mcp start when an agent client needs it.
  • Is my warehouse safe? Yes. Connections are read-only - ktx never writes to your database.

Docs

Community

  • Slack โ€” ask questions, share what you're building, and chat with maintainers.
  • GitHub Issues โ€” report bugs and request features.
  • Contributing โ€” set up the repo, run tests, and open a PR.

Development

git clone https://github.com/kaelio/ktx.git
cd ktx
pnpm install
uv sync --all-groups
pnpm run build
pnpm run check

ktx is a pnpm + uv workspace:

PathPurpose
packages/cliTypeScript CLI and published npm package source
packages/cli/src/contextCore context engine
packages/cli/src/llmLLM and embedding providers
packages/cli/src/connectorsDatabase scan connectors
python/ktx-slSemantic-layer query planning
python/ktx-daemonPortable compute service

Local development CLI:

pnpm run setup:dev
pnpm run link:dev
ktx-dev --help

Useful checks:

pnpm run type-check
pnpm run test
pnpm run dead-code
uv run pytest -q

Telemetry

ktx collects privacy-conscious usage telemetry to understand installs and improve setup, command reliability, and data-agent workflows. Catalog telemetry events do not record file paths, hostnames, SQL, schema names, table names, column names, error messages, raw environment values, or argv. Error reports use PostHog Error Tracking and can include stack frames and raw error messages, which may contain local file paths or the local username in those paths. ktx redacts secrets, credentials, database URLs, auth headers, argv, raw environment values, SQL text, row data, and user-typed prompt or MCP argument text from the explicit $exception payload. See Telemetry for the event catalog and opt-out options.

License

ktx is licensed under the Apache License, Version 2.0. See LICENSE.

Star History

ktx Star History Chart

Frequently Asked Questions

What is ktx-ai-data-agents-context?โŒ„

ktx-ai-data-agents-context is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Kaelio. ktx is an executable context layer for data and analytics agents ๐Ÿ™ Allow Claude Code, Codex, and any AI agent to query data accurately through MCP with skills, memory and a semantic layer. It has 709 GitHub stars.

Is ktx-ai-data-agents-context safe to use?โŒ„

Yes. ktx-ai-data-agents-context passed SkillsLLM's automated security scan โ€” a dependency vulnerability audit plus prompt-injection heuristics โ€” with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install ktx-ai-data-agents-context?โŒ„

Clone the repository with "git clone https://github.com/Kaelio/ktx-ai-data-agents-context" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is ktx-ai-data-agents-context written in?โŒ„

ktx-ai-data-agents-context is primarily written in TypeScript. It is open-source under Kaelio on GitHub, so you can review or fork the full source.

Are there alternatives to ktx-ai-data-agents-context?โŒ„

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh ktx-ai-data-agents-context against similar tools.

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