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Found 1,411 Skills
Security audit tool for CodeIgniter framework. Conducts white-box static audits on CodeIgniter's mechanisms such as CSRF, XSS output filtering, database query construction, routing and validator configuration, session Cookie security, etc., and maps them to a common vulnerability type system (CSRF/AUTH/XSS/SQL/CFG/SESS, etc.).
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.
Static analysis security vulnerability scanner for Ruby on Rails applications. Use when analyzing Rails code for security issues, running security audits, reviewing code for vulnerabilities, setting up security scanning in CI/CD, managing security warnings, or investigating specific vulnerability types (SQL injection, XSS, command injection, etc.). Also use when configuring Brakeman, reducing false positives, or integrating with automated workflows.
Design and architect Goldsky Turbo pipelines. Use this skill for 'should I use X or Y' decisions: kafka source vs dataset source, streaming vs job mode, which resource size (xs/s/m/l/xl/xxl) for my workload, postgres vs clickhouse vs kafka sink, fan-in vs fan-out data flow, one pipeline vs many, dynamic table vs SQL join, how to handle multi-chain deployments. Also use when the user asks 'what's the best way to...' for a pipeline design problem, or is unsure how to structure their pipeline before building it.
Expert knowledge for Azure Backup development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when backing up Azure VMs, AKS, SQL/PostgreSQL/MySQL, SAP HANA, files/disks/blobs, or automating via CLI/PowerShell/REST, and other Azure Backup related development tasks. Not for Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), Azure Blob Storage (use azure-blob-storage), Azure Files (use azure-files).
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
Add a Docker dev service to this project. Supported services: Redis, RabbitMQ, PostgreSQL, MySQL/MariaDB, MongoDB. Writes Docker Compose and Taskfile configs to .devtools/.
Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery. Gathers connection hints from user, discovers existing connections and RDS/Redshift candidates, registers credentials in Secrets Manager or IAM DB auth, configures VPC, and tests. Triggers on: connect to database, set up Glue connection, register data source, connect to Snowflake/BigQuery/RDS, connection timeout, test connection, troubleshoot connection. Do NOT use for moving data (use ingesting-into-data-lake), creating tables (use creating-data-lake-table), queries (use querying-data-lake), catalog exploration (use exploring-data-catalog), or SaaS (Salesforce, ServiceNow, SAP, MongoDB, Kafka).
Salesforce Data Cloud Retrieve phase. Use this skill when the user runs Data Cloud SQL, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use platform-soql-query), segment creation or calculated insight design (use data360-segment), or STDM/session tracing/parquet analysis (use agentforce-observe).
OpenTelemetry in Java — Javaagent zero-code instrumentation, Spring Boot Starter, manual autoconfigure SDK, declarative YAML configuration, BOM dependency management, sensitive-data capture and redaction (url.query, headers, request parameters, SQL sanitization). Use when adding, reviewing, or configuring OpenTelemetry in a Java service. Triggers on "setup otel in java", "java telemetry", "javaagent", "Spring Boot otel", "GlobalOpenTelemetry", "AutoConfiguredOpenTelemetrySdk", "TracerProvider java", "url.query redaction", "capture request headers", or any Java-related OTel question.
Use this skill for Salesforce Data 360 architecture, implementation, troubleshooting, Connect API payloads, metadata discovery, query SQL, calculated insights, semantic layer, search, segmentation, activation, data actions, governance, data spaces, and agentic validation workflows.
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.