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ktx-analytics
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of", finding records by value, exploring tables, comparing periods, explaining metrics, or any data-analysis request. Triggers even when the user does not say "analytics"; if the answer requires querying a configured ktx connection, this skill applies.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
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 — a Claude Pro/Max subscription through Claude Code, or your local Codex authentication. No extra usage billing from ktx.
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 agent | Traditional semantic layer | ktx | |
|---|---|---|---|
| Builds warehouse context automatically | — | — | ✓ |
| Detects joinable columns + resolves fan/chasm traps | — | Manual | ✓ |
| Approved, reusable metric definitions | — | ✓ | ✓ |
| Absorbs wiki / Notion / team knowledge | — | — | ✓ |
| Flags contradictions across sources | — | — | ✓ |
| Ships CLI + MCP for agent execution | Partial | — | ✓ |
| Read-only by design | n/a | n/a | ✓ |
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 -
psqlor 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 statusprintsktx 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
| Command | Purpose |
|---|---|
ktx setup | Create, resume, or update a ktx project |
ktx status | Check project readiness |
ktx ingest | Build context for every configured connection |
ktx sl "revenue" | Search semantic sources |
ktx wiki "refund policy" | Search local wiki pages |
ktx mcp start | Start the MCP server for agent clients |
See the CLI Reference for every command, flag, and option.
Project Layout
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
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 C
// HOW IT'S BUILT
KEY FILES