Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Propose disabling modules in src/modules.ts that the current standalone project does not actually use. Intended to be offered after the user adds a new custom module, because a fresh create-mercato-app scaffold enables every built-in module (classic mode) and that is rarely what the project actually needs in production.
Capture semantic-layer and knowledge updates from a live database schema snapshot.
Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.
Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.
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.
Use when the user asks to prepare, submit, publish, or optimize a pull request for the rocketmq-rust project, especially when the PR title or commit message must follow the `[ISSUE
Generate idiomatic, production-grade Rust comments and documentation strictly following official Rustdoc and Rust API documentation conventions. Designed for real-world Rust projects with zero AI-identifiable markers.
ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).
Machine learning models training and inference using GoMLX for Go. It provides an abstraction to create vectorized computation graphs, that can then be JIT-compiled (Just-In-Time) and executed very fast, with backends using XLA (for CPU/CUDA/TPU), Go and others. Includes a reach set of vector (tensors) operations on the graph, a rich ML library with various type of layers, support for training variables, optimizers, training loops, dataset iterators and more. Apply this skill when working ML projects, or needing to do very efficient vectorized (tensor) computations, like image processing, physics or chemestry simulation, etc.
Generate a Python code skeleton from an experiment blueprint