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Found 1,195 Skills
Creates and maintains dlt (data load tool) pipelines from APIs, databases, and other sources. Use when the user wants to build or debug pipelines; use verified sources (e.g. Salesforce, GitHub, Stripe) or declarative REST API or custom Python; configure destinations (e.g. DuckDB, BigQuery, Snowflake); implement incremental loading; or edit .dlt config and secrets. Use when the user mentions data ingestion, dlt pipeline, dlt init, rest_api_source, incremental load, or pipeline dashboard.
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
Use when deploying Deno apps to production, asking about Deno Deploy, or working with `deno deploy` CLI commands. Covers deployment workflows, environment variables, KV database access, custom domains, the --tunnel flag for local development, and the `deno deploy` command reference.
Intershop Commerce Management (ICM) backend development best practices. This skill should be used when writing, reviewing, or refactoring ICM Java code to ensure optimal patterns for customization, performance, B2B features, security, testing, and maintainability. Triggers on tasks involving ICM cartridge development, REST API creation, business objects, pipelines, database operations, jobs, events, or search.
Debugs and fixes dbt errors systematically. Use when working with dbt errors for: (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.
Creates custom Docker-based State Transition Functions (STFs) for D6E platform workflows. Use when building containerized business logic for D6E, implementing data processing steps, or creating workflow functions that need database access. Handles JSON input/output, SQL API integration, and multi-language implementations (Python, Node.js, Go).
Develops resources for FiveM using the QBCore Framework. Covers resource creation, Core Object usage, Player management, Callbacks, Events, Items, Jobs, Gangs, Database (oxmysql), and best practices. Use when the user works with FiveM, QBCore, Lua scripts for QBCore servers, or mentions `QBCore.Functions`, `GetCoreObject`, `CitizenID`, or any system of the QBCore Framework.
Develops resources for FiveM using the ESX Framework (Legacy). Covers resource creation, Core Object (ESX), xPlayer functions, Events, Callbacks, Items, Jobs, Database (oxmysql), and best practices. Use when the user works with FiveM, ESX, ESX Legacy, or mentions `ESX.GetCoreObject`, `xPlayer`, `ESX.GetPlayerFromId`, or `esx:`.
Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.
Deploy containerized applications (especially Rails) to VPS using Kamal 2. Covers deploy.yml configuration, accessories (PostgreSQL, Redis, Sidekiq), SSL/TLS, secrets management, CI/CD with GitHub Actions, database backups, server hardening, debugging, and scaling. Use when setting up Kamal, configuring deployments, troubleshooting deploy issues, or managing production infrastructure with Kamal.
Multi-agent swarm coordination for complex tasks. Uses hierarchical topology with specialized agents to break down and execute complex work across multiple files and modules. Use when: 3+ files need changes, new feature implementation, cross-module refactoring, API changes with tests, security-related changes, performance optimization across codebase, database schema changes. Skip when: single file edits, simple bug fixes (1-2 lines), documentation updates, configuration changes, quick exploration.
This skill provides project-specific coding conventions, architectural principles, repository structure standards, testing patterns, and contribution guidelines for the better-chatbot project (https://github.com/cgoinglove/better-chatbot). Use this skill when contributing to or working with better-chatbot to understand the design philosophy and ensure code follows established patterns. Includes: API architecture deep-dive, three-tier tool system (MCP/Workflow/Default), component design patterns, database repository patterns, architectural principles (progressive enhancement, defensive programming, streaming-first), practical templates for adding features (tools, routes, repositories). Use when: working in better-chatbot repository, contributing features/fixes, understanding architectural decisions, following server action validators, implementing tools/workflows, setting up Playwright tests, adding API routes, designing database queries, building UI components, handling multi-AI provider integration Keywords: better-chatbot, chatbot contribution, better-chatbot standards, chatbot development, AI chatbot patterns, API architecture, three-tier tool system, repository pattern, progressive enhancement, defensive programming, streaming-first, compound component pattern, Next.js chatbot, Vercel AI SDK chatbot, MCP tools, workflow builder, server action validators, tool abstraction, DAG workflows, shared business logic, safe() wrapper, tool lifecycle