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Found 10,562 Skills
Guide the design and implementation of automated pre-trade compliance systems that validate orders before execution. Use when building a compliance rule engine for an RIA or broker-dealer, configuring hard blocks and soft blocks, maintaining restricted and watch lists including MNPI-driven restrictions, setting concentration limits at security/sector/issuer level, implementing position limits or short selling controls, enforcing wash sale detection or free-riding prevention or pattern day trader identification, applying client-specific ESG screens or legal constraints, designing compliance override workflows with authorization and documentation, backtesting compliance rules, or evaluating compliance check latency impact on execution quality.
Thepeer integration. Manage data, records, and automate workflows. Use when the user wants to interact with Thepeer data.
Rslib best practices for config, CLI workflow, output, declaration files, dependency handling, build optimization and toolchain integration. Use when writing, reviewing, or troubleshooting Rslib projects.
Production-safe Drizzle migration workflow for schema changes that require data backfills or constraint tightening. Use when changing enums/check constraints/defaults, removing status values, or sequencing custom and generated migrations in Drizzle. Trigger on requests about Drizzle migration safety, deployment-safe backfills, migration ordering, and rollback planning.
Venly integration. Manage data, records, and automate workflows. Use when the user wants to interact with Venly data.
Directus integration. Manage Collections, Users, Presets, Dashboards, Flows. Use when the user wants to interact with Directus data.
CodeREADr integration. Manage data, records, and automate workflows. Use when the user wants to interact with CodeREADr data.
Coupontools integration. Manage data, records, and automate workflows. Use when the user wants to interact with Coupontools data.
Amazon EKS integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon EKS data.
Complete skill for the Analyzify Shopify analytics and tracking app. Covers all Analyzify MCP features and workflows. Trigger when the user wants to: check store info, view workspace details, query Google Analytics 4 data, run GA4 reports, check GA4 traffic, query Google Search Console data, view search performance, top queries, query Google Ads campaigns and performance, view ad spend and ROAS, access Shopify store data via Admin API, list products, collections, orders, query historical analytics reports, campaign attribution, traffic trends, check connected accounts, view API key capabilities, or any Analyzify-related task. Covers questions like: "what is my store", "what is my space ID", "show my GA4 traffic", "top search queries this week", "how are my Google Ads performing", "list my products", "show campaign attribution", "compare organic vs paid traffic", "what accounts are connected", "what plan am I on", "show my store dashboard", "daily sessions this month". All operations use MCP tools: execute_graphql, execute_report_graphql, introspect_schema, introspect_report_schema.
Guided, interactive exploration of statistical data via SDMX providers (Eurostat, OECD, ECB, World Bank, ISTAT, and others) using the opensdmx CLI. Use this skill whenever the user asks ANY question about statistics or data that could be answered with SDMX data — even if they don't mention SDMX, Eurostat, or any provider by name. Topics include demographics, economy, employment, births, deaths, population, prices, trade, health, agriculture, GDP, inflation, unemployment, fertility rates, migration, energy, education, poverty, housing, and any other statistical topic. Also use it when the user mentions a specific dataflow ID they want to explore. Trigger this skill even for implicit questions like "how many births were there in Italy last year?" or "I need EU unemployment data by age group" — these clearly need SDMX data even if the user doesn't say so. The skill guides the user step by step: discovers relevant datasets, proposes the most meaningful candidates, explores the schema using real constraints (not codelists), explains the dataset structure, and invites the user to make informed filter choices before fetching any data.
Best practices for using Claude Code in team environments. Covers skill management, knowledge capture, version control, and collaborative workflows.