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Found 1,159 Skills
Preview an existing saved CARTO Builder map inline in the chat via the CARTO MCP server's load_builder_map tool. Use whenever the user references a saved Builder map — by URL, by ID, or by name (resolved via list_maps first). Renders a lightweight read-only preview (layers, basemap, viewport, popups, legend). Widgets, SQL parameters, map description, and other Builder-only features are NOT included; the user can click "Open in Builder" for the full experience. Triggers on "show me the X map", "open the Y map", "preview the Z map", and post-CLI-creation inline previews of a freshly-created map. Distinct from carto-create-builder-maps (CLI authoring), carto-render-inline-map (ad-hoc deck.gl spec), and carto-develop-app (developer app).
Explore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what tables or resources a dataset contains, look up column names and descriptions, surface usage warnings embedded in metadata, or understand how to load data from Parquet files, DuckDB or SQLite databases, or CSV files described by a datapackage.json. Also use when the user has a datapackage.json and wants to know what's in it, how to query it efficiently, or how to connect its metadata to actual data files. Pairs well with dataset-specific skills (like `pudl`) that layer domain knowledge on top.
AI SDLC backend validation workflow. Use when an AI assistant needs to validate Go, SQL, API, provider integration, SDD, or documentation changes in this repository and choose focused deterministic checks without running unrelated expensive tests by default. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
SmartACE (Agentic Context Engineering) workflow engine with MCP-B (Master Client Bridge) and AMUM-QCI-ETHIC module. Dual database architecture using DuckDB (analytics) + SurrealDB (graph). Uses Blender 5.0 (bpy) and UE5 Remote Control. Use when (1) MCP-B agent-to-agent communication (INQC protocol), (2) AMUM 3→6→9 progressive alignment, (3) QCI quantum coherence states, (4) ETHIC principles enforcement (Marcel/Anthropic/EU AI Act), (5) SurrealDB graph relationships, (6) DuckDB SQL workflows, (7) ML inference with infera/vss, (8) Blender 5.0 headless processing, (9) UE5 scene control, (10) DuckLake time travel.
Run gosec SAST scans on Go code. Detects SQL injection, hardcoded credentials, insecure TLS, command injection, and other Go security issues.
Identify security vulnerabilities and anti-patterns providing feedback on security issues a senior developer would catch. Use when user mentions security/vulnerability/safety concerns, code involves user input/authentication/data access, working with sensitive data (passwords/PII/financial), code includes SQL queries/file operations/external API calls, user asks about security best practices, or security-sensitive files are being modified (auth, payment, data access).
Comprehensive ABAP development skill for SAP systems. Use when writing ABAP code, working with internal tables, structures, ABAP SQL, object-oriented programming, RAP (RESTful Application Programming Model), CDS views, EML statements, ABAP Cloud development, string processing, dynamic programming, RTTI/RTTC, field symbols, data references, exception handling, or ABAP unit testing. Covers both classic ABAP and modern ABAP for Cloud Development patterns.
Use this agent when you need to perform security audits, vulnerability assessments, or security reviews of code. This includes checking for common security vulnerabilities, validating input handling, reviewing authentication/authorization implementations, scanning for hardcoded secrets, and ensuring OWASP compliance. <example>Context: The user wants to ensure their newly implemented API endpoints are secure before deployment.\nuser: "I've just finished implementing the user authentication endpoints. Can you check them for security issues?"\nassistant: "I'll use the security-sentinel agent to perform a comprehensive security review of your authentication endpoints."\n<commentary>Since the user is asking for a security review of authentication code, use the security-sentinel agent to scan for vulnerabilities and ensure secure implementation.</commentary></example> <example>Context: The user is concerned about potential SQL injection vulnerabilities in their database queries.\nuser: "I'm worried about SQL inj...
Search data using vector similarity, full-text keywords, or hybrid methods with Reciprocal Rank Fusion (RRF). Use when setting up embeddings for search, configuring full-text indexing, writing vector_search/text_search/rrf SQL queries, using the /v1/search HTTP API, or configuring vector engines like S3 Vectors.
Cal.com self-hosted deployment to GCP Cloud Run with Supabase PostgreSQL. Docker Compose for local dev. TRIGGERS - deploy calcom, cloud run, self-hosted, docker compose, supabase, gcp deploy, infrastructure, cal.com hosting.