# Sl Capture

How to capture new reusable patterns into ktx's semantic layer - when a measure...

- Repository: https://github.com/Kaelio/ktx (1627 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Kaelio/ktx --skill sl_capture -g -a claude-code -y`
Codex: `npx skills add Kaelio/ktx --skill sl_capture -g -a codex -y`
Cursor: `npx skills add Kaelio/ktx --skill sl_capture -g -a cursor -y`
Gemini CLI: `npx skills add Kaelio/ktx --skill sl_capture -g -a gemini-cli -y`

## Description

How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Semantic Layer - Capture

This skill covers **when** and **how** to capture new patterns into the semantic layer. For schema reference and query grammar, load the `sl` skill first.

When the current turn produces a reusable pattern (business metric, derived view, join pattern, computed dimension), capture it so future queries can reach for it instead of rediscovering it.

## SQL dialect

The user-facing prompt includes a `Warehouse:` line under the SL Sources index
(e.g. `Warehouse: BIGQUERY`). All `expr` strings - measure expressions, segment
predicates, computed-column SQL - execute on that warehouse and must use its
syntax. Date arithmetic in particular varies by dialect:

- **BigQuery**: `transaction_date >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 DAY)` (when the column is `TIMESTAMP`); `event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)` (when `DATE`).
- **Postgres / Redshift**: `transaction_date >= current_date - interval '90 days'`.
- **Snowflake**: `transaction_date >= dateadd(day, -90, current_timestamp())`.

Match the column's manifest type (`type: time` → TIMESTAMP/DATETIME on the
warehouse) - comparing TIMESTAMP to a DATE-arithmetic result fails on
BigQuery. After every `sl_edit_source`/`sl_write_source`, the inline validator runs a
`LIMIT 1` warehouse probe per measure and surfaces dialect mismatches; if
you see an error trailer, fix the expression and retry rather than leaving
the source for the post-squash gate to revert.

## What's worth capturing

- Business metric aggregations (ARR, MRR, revenue, churn, retention, conversion, LTV, CAC).
- Derived calculations combining multiple signals (risk scores, health scores, composite KPIs).
- Multi-table join patterns producing a reusable analytical view.
- Computed categories or flags useful as reusable dimensions (`case when num_protocols >= 3 then 'power' else 'regular' end`).
- Missing joins between two sources that both exist but aren't connected in the join graph.

The rest: https://github.com/Kaelio/ktx/blob/HEAD/packages/cli/src/skills/sl_capture/SKILL.md