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Found 1,897 Skills
Nim systems programming with Python-like syntax. Use for .nim files.
Senior-level UI/UX design skill with data-driven architecture for building premium, production-grade interfaces. Includes: BM25 search engine over 1,875+ data rows across 27 CSV databases, 8 Python scripts (search, contrast checker, palette/token/typography generators, design system generator, UI auditor), 16 tech stack guides, 11 reference documents, and 331 lines of intent-first design methodology. Covers: design token architecture, oklch color systems, typography hierarchies, spacing grids, depth strategies, component patterns, animation timing, WCAG 2.2 accessibility, cognitive science principles, and industry-specific reasoning for 30+ industries.
Adds OpenTelemetry-based tracing to applications via TrueFoundry's tracing platform (Traceloop SDK). Creates tracing projects, instruments Python/TypeScript code, and captures LLM calls and custom spans.
Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.
Use this skill whenever working with QuestDB — a high-performance time-series database. Trigger on any mention of QuestDB, time-series SQL with SAMPLE BY, LATEST ON, ASOF JOIN, ILP ingestion, or the questdb Python/Go/Java/Rust/.NET client libraries. Also trigger when writing Grafana queries against QuestDB, creating materialized views for time-series rollups, working with order book or financial market data in QuestDB, or any SQL that involves designated timestamps or time-partitioned tables. QuestDB extends SQL with unique time-series keywords — standard PostgreSQL or MySQL patterns will fail. Always read this skill before writing QuestDB SQL to avoid hallucinating incorrect syntax.
The industry standard library for machine learning in Python. Provides simple and efficient tools for predictive data analysis, covering classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
QML and Qt Quick — declarative UI language for modern Qt applications. Use when building a QML-based UI, embedding QML in a Python/C++ app, exposing Python/C++ objects to QML, creating QML components, or choosing between QML and widgets. Trigger phrases: "QML", "Qt Quick", "declarative UI", "QQmlApplicationEngine", "expose to QML", "QML component", "QML signal", "pyqtProperty", "QML vs widgets", "QtQuick.Controls", "Item", "Rectangle"
Sinch SDK installation and client initialization for Node.js, Python, Java, and .NET. Use when installing a Sinch SDK, initializing SinchClient, setting up SDK credentials, configuring conversation region in SDK, or building a multi-product SDK client. For In-App Calling SDKs, see sinch-in-app-calling.
Use when running an annual SaaS audit, doing category-level spend review, or rationalizing the supplier base — when the user needs to do a spend audit, spend categorization (UNSPSC-aligned), purchasing-cycle analysis, or risk-balanced supplier consolidation. Triggers on "spend audit", "SaaS audit", "spend categorization", "supplier rationalization", "supplier consolidation", "purchasing cycle", "procurement review", "category strategy", "duplicate SaaS", "renewal cluster". Ships 3 stdlib-only Python tools (UNSPSC-aligned spend categorizer with Pareto breakdown and industry profiles, purchasing-cycle analyzer that surfaces bottleneck categories per Goldratt's Theory of Constraints, supplier-consolidation planner that refuses single-source recommendations for tier-1 categories without a documented break-glass plan), 3 reference docs each citing 7+ authoritative sources (A.T. Kearney / Hackett / Spend Matters / UNSPSC / Productiv / Vendr / Tropic / IACCM / ISM / BCG), and a 20-minute spend-intake template. Distinct from sibling vendor-management (performance scoring of vendors you keep paying), finance/financial-analysis (close + report, not category strategy), and c-level-advisor/general-counsel-advisor (contract law, not category rationalization).
Build Slack apps using the Bolt framework across Python, JavaScript, and Java. Covers Block Kit for rich UIs, interactive components, slash commands, event handling, OAuth installation flows, and Workflow Builder integration. Focus on best practices for production-ready Slack apps. Use when: slack bot, slack app, bolt framework, block kit, slash command.