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Agent skills
mcp-server

Topic: mcp-server

1,273 skills in this topic.

evolve-session-review Automatically triggered by Stop hook. Reviews session for evolution learnings.
Prismer-AI/PrismerCloud 679
plugin-dev Prismer Evolution Plugin 开发指南 — 快速迭代 hook/skill、调试、日志查看、测试、发布全流程
Prismer-AI/PrismerCloud 679
prismer-setup Set up Prismer API key — opens browser, auto-registers, zero copy-paste
Prismer-AI/PrismerCloud 679
prismer-evolve-analyze Query the Prismer Evolution network for known fix strategies. Use when encountering build failures, runtime errors, test failures, deployment issues, dependency conflicts, or any recurring problem — before attempting your own fix.
Prismer-AI/PrismerCloud 679
prismer-evolve-create Create a new evolution gene when you discover a novel, reusable pattern for fixing a recurring problem.
Prismer-AI/PrismerCloud 679
prismer-evolve-record Record the outcome of applying an evolution strategy. Use after resolving an error where prismer-evolve-analyze provided a recommendation, to feed back success or failure to the network.
Prismer-AI/PrismerCloud 679
cupertino This skill should be used when working with Apple APIs, iOS/macOS/visionOS development, or Swift language questions. Covers searching Apple developer documentation, looking up SwiftUI views, finding UIKit APIs, reading Apple docs, browsing Swift Evolution proposals, checking Human Interface Guidelines, and exploring Apple sample code. Supports 300+ frameworks including SwiftUI, UIKit, Foundation, and Combine via offline search of 300,000+ documentation pages.
mihaelamj/cupertino 641
wren-connection-info Reference guide for Wren Engine connection info — explains required fields for all 18 supported data sources (PostgreSQL, MySQL, BigQuery, Snowflake, ClickHouse, Trino, DuckDB, Databricks, Spark, Athena, Redshift, Oracle, SQL Server, Apache Doris, S3, GCS, MinIO, local files). Covers sensitive field handling, Docker host hints, and BigQuery credential encoding. Use when the user asks how to configure a data source connection or what fields to fill in.
Canner/wren-engine 639
wren-generate-mdl Generate a Wren MDL manifest from a database using ibis-server metadata endpoints. Use when a user wants to create or set up a new Wren MDL, scaffold a manifest from an existing database, or onboard a new data source without installing any database drivers locally.
Canner/wren-engine 639
wren-http-api Interact with Wren Engine MCP server via plain HTTP JSON-RPC requests — no MCP client SDK required. Covers session initialization, tool discovery, and calling all 20+ Wren tools (query, deploy, metadata, health check) using standard HTTP POST with JSON-RPC 2.0 payloads. Use when the client cannot or prefers not to use the MCP protocol directly (e.g. OpenClaw, custom HTTP clients, shell scripts, or any environment without an MCP SDK).
Canner/wren-engine 639
wren-mcp-setup Set up Wren Engine MCP server via Docker and register it with an AI agent. Covers pulling the Docker image, running the container with docker run, mounting a workspace, configuring connection info via the Web UI (with Docker host hint), registering the MCP server in Claude Code (or other MCP clients) using streamable-http transport, and starting a new session to interact with Wren MCP. Trigger when a user wants to run Wren MCP in Docker, configure Claude Code MCP, or connect an AI client to a Dockerized Wren Engine.
Canner/wren-engine 639
wren-project Save, load, and build Wren MDL manifests as YAML project directories for version control. Use when a user wants to persist an MDL as human-readable YAML files, load a YAML project back into MDL JSON, or compile a YAML project to a deployable mdl.json file.
Canner/wren-engine 639
wren-quickstart End-to-end quickstart for Wren Engine — create a workspace, generate an MDL from a live database, save it as a versioned project, start the Wren MCP Docker container, and verify the setup with a health check. Trigger when a user wants to set up Wren Engine from scratch, onboard a new data source, or get started with Wren MCP. Requires dependent skills already installed (use /wren-usage to install them first).
Canner/wren-engine 639
wren-sql Write and correct SQL queries targeting Wren Engine — covers MDL query rules, filter strategies, data types (ARRAY, STRUCT, JSON/VARIANT), date/time functions, Calculated Fields, BigQuery dialect quirks, and error diagnosis. Use when generating or debugging SQL for any Wren Engine data source.
Canner/wren-engine 639
wren-usage Wren Engine — semantic SQL engine for AI agents. Query 22+ data sources (PostgreSQL, BigQuery, Snowflake, MySQL, ClickHouse, etc.) through a modeling layer (MDL). This skill is the main entry point: it guides setup, delegates to focused sub-skills for SQL authoring, MDL generation, project management, and MCP server operations. Use when: write SQL, query data, generate or update MDL, change database connection, manage YAML projects, set up or operate MCP server, or get started with Wren Engine for the first time.
Canner/wren-engine 639
wren-dlt-connector Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.
Canner/wren-engine 639
wren-generate-mdl Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.
Canner/wren-engine 639
wren-usage Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Canner/wren-engine 639
create-pr Alias for sentry-skills:pr-writer. Use when users explicitly ask for "create-pr" or reference the legacy skill name. Redirects to the canonical PR writing workflow.
getsentry/sentry-mcp 632
logging-observability Review code for correct logging and error handling patterns. Use when reviewing code that handles errors, uses logging functions, or captures exceptions. Enforces the error hierarchy where 4xx errors are never logged to Sentry and 5xx errors always are. Trigger phrases include "review logging", "check error handling", "audit observability", or verify correct use of logIssue vs logError.
getsentry/sentry-mcp 632
qa QA test changes against the local dev server. Use when explicitly invoked via /qa to verify changes work end-to-end.
getsentry/sentry-mcp 632
skill-creator Alias for sentry-skills:skill-writer. Use when users explicitly ask for "skill-creator" or reference the legacy skill name. Redirects to the canonical skill authoring workflow.
getsentry/sentry-mcp 632
testing-guidelines Guide for writing tests. Use when adding new functionality, fixing bugs, or when tests are needed. Emphasizes integration tests, real-world fixtures, and regression coverage.
getsentry/sentry-mcp 632
brainstorm Run multi-round AI brainstorming debates between multiple LLM providers (GPT, DeepSeek, Groq, Ollama). Claude actively participates as a debater alongside external models. Use when the user wants diverse perspectives, multi-model critiques, or synthesized answers from several AI models working together.
spranab/brainstorm-mcp 54