Productivity

인터넷등기소(IROS)에서 법인/부동산 등기부등본(등기사항증명서) 발급을 준비하고, 사용자가 직접 로그인·결제한 브라우저 흐름 안에서 장바구니·열람·저장을 안전하게 보조한다.

Use Twill Cloud Coding Agent to manage Twill's public v1 API workflows. Create/list/update tasks, stream and cancel jobs, manage scheduled tasks, list repositories, and export Claude teleport sessions.

Provision instant temporary Postgres databases via Claimable Postgres by Neon (neon.new) with no login, signup, or credit card. Supports REST API, CLI, and SDK. Use when users ask for a quick Postgres environment, a throwaway DATABASE_URL for prototyping/tests, or "just give me a DB now". Triggers include: "quick postgres", "temporary postgres", "no signup database", "no credit card database", "instant DATABASE_URL", "npx neon-new", "neon.new", "neon.new API", "claimable postgres API".

Appwrite TypeScript SDK skill. Use when building browser-based JavaScript/TypeScript apps, React Native mobile apps, or server-side Node.js/Deno backends with Appwrite. Covers client-side auth (email, OAuth, anonymous), database queries, file uploads, real-time subscriptions, and server-side admin via API keys for user management, database administration, storage, and functions.

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control. Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions. Do NOT use for: importing files (use ingesting-into-data-lake), vector storage (use storing-and-querying-vectors), querying existing tables (use querying-data-lake), or locating existing table (use finding-data-lake-assets).

LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass techniques.

Orchestrate TestFlight distribution, groups, testers, and What to Test notes using asc. Use when rolling out betas.

Assess APM service health using SLOs, alerts, ML, throughput, latency, error rate, and dependencies. Use when checking service status, performance, or when the user asks about service health.

Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift. Triggers on: find the table, where is our data, which table has, locate dataset, find data for, search catalog, what tables match, Redshift table, lakehouse table, data lake table, warehouse table, reverse lookup S3 path. Do NOT use for: full catalog audits (use exploring-data-catalog), running queries (use querying-data-lake), creating tables (use creating-data-lake-table).

Google Cloud CLI operations and resource management

View and edit cmux settings in ~/.config/cmux/cmux.json. Use when the user wants to change cmux preferences (appearance, sidebar, notifications, automation, browser, shortcuts), set a value by JSON path, validate the file, open it in an editor, or look up which keys cmux recognizes. Triggers on '/cmux-settings', 'change cmux setting', 'set <something> in cmux', 'cmux config', 'cmux.json', or 'rebind a cmux shortcut'.

Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).

Self-evolving AI agent system with 26 tools, three-layer memory, MCP plugins, and 24/7 self-repair in pure Python.

Build MCP servers in Python with FastMCP. Define tools / resources / prompts, build the server, test locally, deploy to FastMCP Cloud or Docker. Use whenever the user mentions building an MCP server, exposing tools to LLMs, FastMCP, building a Claude integration, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

Create, update, and review Terraform provider documentation for Terraform Registry using HashiCorp-recommended patterns, tfplugindocs templates, and schema descriptions. Use when adding or changing provider configuration, resources, data sources, ephemeral resources, list resources, functions, or guides; when validating generated docs; and when troubleshooting missing or incorrect Registry documentation.

markdown-token-optimizer — an installable skill for AI agents.

Diagnoses and resolves Amazon S3 Files issues including mount failures, permission errors, synchronization problems, and performance issues. Use when the user has an S3 file system that is not mounting, returning access denied, not syncing changes to S3, showing files in lost+found, or performing slower than expected.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Work inside the current cmux workspace and terminal. Use for cmux workspace, current workspace, caller surface, panes, surfaces, socket targeting, and non-interfering cmux automation.

Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.

Comprehensive GitLab CLI (glab) command reference and workflows for all GitLab operations via terminal. Use when user mentions GitLab CLI, glab commands, GitLab automation, MR/issue management via CLI, CI/CD pipeline commands, repo operations, authentication setup, or any GitLab terminal operations. Routes to specialized sub-skills for auth, CI, MRs, issues, releases, repos, and 30+ other glab commands. Triggers on glab, GitLab CLI, GitLab commands, GitLab terminal, GitLab automation.