Published from the platform vendors’ own GitHub organizations (anthropics, openai, google, google-gemini, googleapis, googleworkspace, microsoft, github, awslabs, cloudflare, huggingface, stripe, supabase, vercel-labs, netlify, expo, flutter, mongodb, neondatabase, duckdb, getsentry, datadog-labs, apollographql, sanity-io, makenotion, greensock, replicate, firecrawl, brave, coinbase, typefully, coderabbitai, callstackincubator). Same catalog, same install command, first-party provenance.
318 artifacts from 33 vendor organizations
318 artifacts
Triage a Playwright bug report by reproducing it from the information in the issue. Use when asked to triage, reproduce, or verify a GitHub issue (a new bug report, or an existing report with a new comment).
Query Playwright CI test results from the aggregated DuckDB database. Answers questions about flaky tests, failure rates, slow tests, and per-run/SHA/PR results without hunting through GitHub artifacts.
Explains how to develop Playwright - add APIs, MCP tools, CLI commands, and vendor dependencies.
Browser automation CLI for AI agents
Playwright Tools for MCP
Provision direct cloud access (Vertex AI, Bedrock, or LLM gateway) for the Claude Office add-in. Generates the customized add-in manifest, walks through Azure admin consent, and writes per-user config via Microsoft Graph extension attributes.
S&P Global - Financial data and analytics skills including company tearsheets, earnings previews, and transaction summaries
Investment banking productivity tools: client and market insights, deck creation, financial analysis, and transaction management
Core financial modeling and analysis tools: DCF, comps, LBO, 3-statement models, competitive analysis, and deck QC
Private equity deal sourcing and workflow tools: company discovery, CRM integration, and founder outreach
Price bonds, analyze yield curves, evaluate FX carry trades, value options, and build macro dashboards using LSEG financial data and analytics.
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
Semantic code analysis MCP server providing intelligent code understanding, refactoring suggestions, and codebase navigation through language server protocol integration.
Seamless onboarding for the Code-with-Claude Makers Cardputer: one /maker-setup command clones the build-with-claude repo, flashes UIFlow firmware, and installs the Claude Buddy app bundle onto a freshly-plugged-in M5Stack Cardputer-Adv.
Browser automation and end-to-end testing MCP server by Microsoft. Enables Claude to interact with web pages, take screenshots, fill forms, click elements, and perform automated browser testing workflows.
Asana project management integration. Create and manage tasks, search projects, update assignments, track progress, and integrate your development workflow with Asana's work management platform.
Agent that simplifies and refines code for clarity, consistency, and maintainability while preserving functionality
Modernize legacy codebases (COBOL, legacy Java/C++/.NET, monolith web apps) with a structured preflight / assess / map / extract-rules / brief / (reimagine | transform | uplift) / harden / status workflow. Cross-stack rewrites, greenfield reimagining, and same-stack version uplifts (e.g. .NET Framework → .NET 8); an interactive topology viewer; specialist agents; and optional dynamic-workflow orchestration with adversarial verification.
The Terraform MCP Server provides seamless integration with Terraform ecosystem, enabling advanced automation and interaction capabilities for Infrastructure as Code (IaC) development.
A comprehensive example plugin demonstrating all Claude Code extension options including commands, agents, skills, hooks, and MCP servers
Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
Telegram channel for Claude Code — messaging bridge with built-in access control. Manage pairing, allowlists, and policy via /telegram:access.
iMessage channel for Claude Code — reads chat.db directly, sends via AppleScript. Built-in access control; manage pairing, allowlists, and policy via /imessage:access.
Official GitHub MCP server for repository management. Create issues, manage pull requests, review code, search repositories, and interact with GitHub's full API directly from Claude Code.
AI code review agent for GitHub and GitLab. View and resolve Greptile's PR review comments directly from Claude Code.
Discord channel for Claude Code — messaging bridge with built-in access control. Manage pairing, allowlists, and policy via /discord:access.
GitLab DevOps platform integration. Manage repositories, merge requests, CI/CD pipelines, issues, and wikis. Full access to GitLab's comprehensive DevOps lifecycle tools.
Asana project management integration. Create and manage tasks, search projects, update assignments, track progress, and integrate your development workflow with Asana's work management platform.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Google Firebase MCP integration. Manage Firestore databases, authentication, cloud functions, hosting, and storage. Build and manage your Firebase backend directly from your development workflow.
Localhost iMessage-style web chat for Claude Code — test surface with file upload and edits. No tokens, no access control.
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Google People: Manage contacts and profiles.
Google Workflow: Convert a Gmail message into a Google Tasks entry.
Manage an executive's schedule, inbox, and communications.
Gmail: Send an email.
gws CLI: Shared patterns for authentication, global flags, and output formatting.
Google Model Armor: Create a new Model Armor template.
Google Workflow: Today's meetings + open tasks as a standup summary.
Google Model Armor: Filter user-generated content for safety.
Google Model Armor: Sanitize a model response through a Model Armor template.
Export a Google Sheets spreadsheet as a CSV file for local backup or processing.
Read and write Google Forms.
Coordinate projects — track tasks, schedule meetings, and share docs.
Administer IT — monitor security and configure Workspace.
Google Workflow: Prepare for your next meeting: agenda, attendees, and linked docs.
Google Apps Script: Upload local files to an Apps Script project.
Create, organize, and distribute content across Workspace.
Google Calendar: Manage calendars and events.
Organize research — manage references, notes, and collaboration.
Google Workflow: Weekly summary: this week's meetings + unread email count.
Read and write Google Docs.
Manage Google Keep notes.
Google Workflow: Cross-service productivity workflows.
Gmail: Forward a message to new recipients.
Retrieve and review responses from a Google Form.
Gmail: Send, read, and manage email.
Plan and manage events — scheduling, invitations, and logistics.
Google Tasks: Manage task lists and tasks.
List and download all files from a Google Drive folder.
Google Workspace Events: Renew/reactivate Workspace Events subscriptions.
Google Sheets: Read and write spreadsheets.
Google Sheets: Read values from a spreadsheet.
Manage Google Meet conferences.
Manage customer support — track tickets, respond, escalate issues.
Google Slides: Read and write presentations.
Google Chat: Manage Chat spaces and messages.
Manage Google Apps Script projects.
Manage sales workflows — track deals, schedule calls, client comms.
Handle HR workflows — onboarding, announcements, and employee comms.
Subscribe to Google Workspace events.
Google Calendar: Create a new event.
Create recurring focus time blocks on Google Calendar to protect deep work hours.
Google Sheets: Append a row to a spreadsheet.
Google Workflow: Announce a Drive file in a Chat space.
Google Docs: Append text to a document.
Google Classroom: Manage classes, rosters, and coursework.
Read data from two tabs in a Google Sheet to compare and identify differences.
Google Model Armor: Sanitize a user prompt through a Model Armor template.
Lead a team — run standups, coordinate tasks, and communicate.
🤗 smolagents: a barebones library for agents. Agents write python code to call tools or orchestrate other agents.
Use Codex from Claude Code to review code or delegate tasks.
Processes GCP infrastructure design and deployment workflows. Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Don't use for non-GCP cloud providers, or general Terraform coding outside the ADC context.
Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an entire audience for Customer Match, mobile device ID audiences, or any other audience use case supported by the Data Manager API. Don't use for uploading events or conversions (use the data-manager-api-event-ingestion skill).
Use this skill for Interactive Media Ads (IMA) SDK client-side ad insertion when you are requesting video ads client-side into websites, apps, TVs or other platforms with VAST or VMAP. Do not use for Dynamic Ad Insertion (DAI), SSAI, or SGAI (use the ima-sdk-dai-basics skill instead).
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
Provides instructions to implement, integrate, or configure Google Mobile Ads (GMA) banner ads in Android, iOS, or Unity mobile applications. Use when the task involves setting up banner ads in a mobile application. Don't use for other ad formats like interstitial or rewarded ads.
Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).
Guides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API. Don't use for implementing audience or event ingestion logic (use the data-manager-api-audience-ingestion or data-manager-api-event-ingestion skills instead).
Manages Google Analytics account and property settings, enables the Analytics Admin API via the Cloud CLI, lists accounts and properties, and manages data streams, custom dimensions, conversion events, and integrations. Use when you need to programmatically configure Google Analytics accounts, provision properties, manage data retention, configure Measurement Protocol secrets, or manage Firebase and Google Ads links.
Guides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a "retrieve campaigns" script. Troubleshoots common setup errors: USERPERMISSIONDENIED, logincustomerid issues, and DEVELOPERTOKENNOTAPPROVED. Use this skill when: - The user asks how to get started with the Google Ads API. - The user needs to set up Google Ads credentials or developer tokens. - The user wants to write a quickstart/example script for Google Ads. - The user encounters errors like USERPERMISSIONDENIED or DEVELOPERTOKENNOTAPPROVED.
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (e.g., Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
Provides instructions for implementing, integrating, or configuring
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (gcloud ai model-garden models list-deployment-config), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use agent-platform-endpoint-management. Don't use for public Vertex AI deployments (use the vertex-deploy skill) or for running model evaluations (use the agent-platform-eval-flywheel skill).
Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized analytics reports, query metrics (like activeUsers, screenPageViews) and dimensions (like city, date), check metrics and dimensions compatibility, or verify API enablement. Don't use for Google Analytics Admin API operations (e.g., creating properties, managing users) or for front-end tracking installation.
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
Provides instructions for implementing, integrating, or configuring
Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use agent-platform-tuning), deploying models to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
Runs batch and HPC workloads on GKE, utilizing job queues and parallel processing. Use when running GKE batch jobs, configuring GKE HPC, or setting up GKE job queues. Don't use for standard web application deployments (use gke-app-onboarding instead).
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when you need to generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).
Automates the end-to-end detection engineering workflow in Google SecOps using MCP tools. Use when fetching threat intelligence from blogs, generating Threat Detection Opportunities (TDOs), simulating attacker behavior with synthetic UDM events, evaluating rule coverage, generating new YARA-L 2.0 rules to close coverage gaps, and with user approval, deploy them to SecOps. Don't use when asked to perform threat hunting actions, and SOC investigative actions.
Configures GKE Backup Plans and restore workflows. Use for backup policies, disaster recovery, or GKE cluster restores. Don't use for database backups.
This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
Manages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).
Provides safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).
Diagnoses, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.
Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.
Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (bq), checking cluster cost budgets (gcloud billing), or diagnosing cost drivers like pod requests vs. actual utilization (kubectl top). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization instead).
Diagnoses and prevents vbarcontrolagent segfaults, out-of-memory (OOM) errors, and TPU device initialization failures on TPU v6e nodes in GKE caused by race conditions during TPU device resets or high-frequency metrics polling. Use when troubleshooting vbarcontrolagent crashes, memory cgroup OOMs in serial console logs, tpu-device-plugin metrics checksum corruption errors, or custom TPU metrics collection conflicts on GKE TPU v6e nodes. Don't use for general non-TPU container OOM troubleshooting or standard GKE node lifecycle operations.
Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving ClientMessage/ServerMessage protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot generateContent, embeddings, image/video generation, or fine-tuning — use the gemini-api skill for those.
Provides guidance and instructions on managing remote devices on Developer Device Platform (DDP). Use when reserving remote Android devices, establishing connection tunnels, checking session status, or extending/cancelling leases. Don't use for iOS or local device/hardware inquiries.
Configure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.
Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead).
Generates Logging Query Language (LQL) queries for Google Cloud Logging from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.
Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).
MCP Toolbox for Databases enables your agent to connect to your database.
Official GSAP skills for Claude, Cursor, and other AI agents — animations, timelines, ScrollTrigger, plugins, utilities, React, and performance
Control headless Chrome via Cloudflare Browser Rendering CDP WebSocket. Use for screenshots, page navigation, scraping, and video capture when browser automation is needed in a Cloudflare Workers environment. Requires CDP_SECRET env var and cdpUrl configured in browser.profiles.
An AWS Labs Model Context Protocol (MCP) server for EKS
An AWS Labs Model Context Protocol (MCP) server for Timestream for InfluxDB
MCP server for interacting with AWS Managed Prometheus
An AWS Labs Model Context Protocol (MCP) server for awslabs.s3-tables-mcp-server
An AWS Labs Model Context Protocol (MCP) server for managing AWS IAM resources including users, roles, policies, and permissions
A Model Context Protocol (MCP) server that provides tools for AWS Billing and Cost Management by wrapping boto3 SDK functions.
An AWS Labs Model Context Protocol (MCP) server for postgres
An AWS Labs Model Context Protocol (MCP) server for AWS Transform — manage transformation workspaces, jobs, connectors, HITL tasks, artifacts, and chat
MCP server for discovering and exploring AWS Registry of Open Data datasets
An AWS Labs Model Context Protocol (MCP) server for valkey
An AWS Labs Model Context Protocol (MCP) server for Amazon ElastiCache
An AWS Labs Model Context Protocol (MCP) server for Bedrock Knowledge Base Retrieval
An AWS Labs Model Context Protocol (MCP) server for AWS Lambda Tools
An AWS Labs Model Context Protocol (MCP) server for managing AWS resources via Cloud Control API
The official MCP Server for interacting with AWS DynamoDB
An AWS Labs Model Context Protocol (MCP) server for HealthImaging
An AWS Labs Model Context Protocol (MCP) server for documentdb
An AWS Labs Model Context Protocol (MCP) server for AWS Step Functions
An AWS Labs Model Context Protocol (MCP) server for cloudtrail
An AWS Labs Model Context Protocol (MCP) server for Microsoft SQL Server on AWS RDS
An AWS Labs Model Context Protocol (MCP) server for document parsing
AWS Well-Architected Security Assessment Tool MCP Server
An AWS Labs Model Context Protocol (MCP) server for Amazon ElastiCache Memcached
An AWS Labs Model Context Protocol (MCP) server for Oracle Database on AWS RDS
MCP server for AWS SageMaker AI
An AWS Labs Model Context Protocol (MCP) server for mysql
An AWS Labs Model Context Protocol (MCP) server for Redshift
An Model Context Protocol (MCP) server for AWS SupportAPI.
An AWS Labs Model Context Protocol (MCP) server for AWS Security Agent — automated security scanning, penetration testing, and remediation
A Model Context Protocol server for AmazonMQ to provision and manage your AMQ brokers
An AWS Labs Model Context Protocol (MCP) server for Amazon Q Business anonymous mode application.
Model Context Protocol (MCP) server for Amazon Bedrock AgentCore services
An Amazon Neptune MCP server that allows for fetching status, schema, and querying using openCypher, Gremlin, and SPARQL for Neptune Database and openCypher for Neptune Analytics.
An implementation of the Model Context Protocol integrated with Amazon Bedrock Knowledge Bases
An implementation of the Model Context Protocol integrated with Amazon Nova Canvas for image generation
An AWS Labs Model Context Protocol (MCP) server for amazon-kendra-index-mcp-server
An Amazon Keyspaces (for Apache Cassandra) MCP server for interacting with Amazon Keyspaces and Apache Cassandra.
An AWS Labs Model Context Protocol (MCP) server for Aurora DSQL
A Model Context Protocol server for Amazon Translate to provide text translation, custom terminology management, and batch translation processing
This is an AWS Labs Model Context Protocol (MCP) server implementation that enables Independent Software Vendors (ISVs) to interact with Amazon Q Business
A Model Context Protocol server for Amazon SNS and SQS to provision and manage your messaging services
Model Context Protocol (MCP) server for interacting with AWS
An AWS Labs Model Context Protocol (MCP) server for dataprocessing
An AWS Labs Model Context Protocol (MCP) server for aws-network
An AWS Labs Model Context Protocol (MCP) server for aws-iot-sitewise
An AWS Labs Model Context Protocol (MCP) server for AWS Location Service
An Infrastructure as Code MCP server that provides CloudFormation template validation, compliance checking, and deployment troubleshooting capabilities.
An AWS Labs Model Context Protocol (MCP) server for AWS HealthOmics
CoCounsel Legal delivers comprehensive Westlaw Deep Research reports with inline, linked citations to Westlaw and Practical Law sources.
Runs M&A diligence at scale with cited tabular review, builds disclosure schedules and closing checklists, drafts board consents and minutes in house format, and tracks entity compliance deadlines across jurisdictions.
Reviews hires and terminations for jurisdiction-specific risk flags, classifies workers against the controlling state test, tracks leave deadlines before they're missed, runs internal investigations, and drafts policies with state supplements where the law differs.
Triages proposed AI use cases against your registry, runs impact assessments across the regimes in scope, reviews vendor AI terms for training-on-data and liability gaps, and keeps your AI policy current with practice.
Runs first-pass trademark clearance and freedom-to-operate triage, screens invention disclosures for initial patentability, drafts and triages cease-and-desist letters and DMCA takedowns (send and respond), checks open source compliance, reviews IP clauses, and tracks registrations and renewal deadlines.
Reviews vendor agreements, NDAs, and SaaS subscriptions against your sales-side or purchasing-side playbook, tracks renewals and cancel-by deadlines before they're missed, routes escalations to the right approver, and translates reviews into summaries business stakeholders will actually read.
Python SDK for Claude Code
MCP server for Firecrawl — search, scrape, and interact with the web. Supports both cloud and self-hosted instances. Features include web search, scraping, page interaction, batch processing, and LLM-powered content analysis.
Run Warden to analyze code changes before committing. Use when asked to "run warden", "check my changes", "review before commit", "warden config", "warden.toml", "create a warden skill", "add trigger", or any Warden-related local development task.
XcodeBuildMCP is a Model Context Protocol server that provides tools for Xcode project management, simulator management, and app utilities.
Turn your coding agent into a SOTA browser agent. Drives a local Playwright workspace via one bash command at a time, saving screenshots and an action log into finalruns/run<id>/, and visually self-verifies the result.
A library of skills for the Gemini API, SDK and model interactions.
Official Claude plugin for Dart and Flutter that installs Flutter/Dart Skills and Dart MCP server for building natively compiled, visually stunning applications for mobile, web, desktop, and embedded devices from a single codebase
Skills for the Cloudflare developer platform: Workers, Durable Objects, Agents SDK, MCP servers, Wrangler CLI, and web performance
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 (pgcron, pgmq), vector/semantic search (pgvector), and restoring dumps (pgrestore) 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.
Evaluate Expo skills in this repo end-to-end - trigger accuracy, generated code quality, and runtime screenshots on iOS simulator and Android emulator via Expo Go (web optional). Use when the user wants to eval an Expo skill, test that a skill produces working code, benchmark a skill with device screenshots, or verify a skill's output renders correctly.
One-stop shop for building AI-powered products and businesses with Stripe.
A command line tool for setting up Stripe MCP server
Guide for upgrading Stripe API versions and SDKs
One-stop shop for building AI-powered products and businesses with Stripe.
One-stop shop for building AI-powered products and businesses with Stripe.
Stripe Agent Toolkit
One-stop shop for building AI-powered products and businesses with Stripe.
One-stop shop for building AI-powered products and businesses with Stripe.
One-stop shop for building AI-powered products and businesses with Stripe.
Provides React Navigation UI patterns for stacks, tabs, drawers etc. Use when building navigation UIs with React Navigation, configuring headers, bottom sheets or handling safe areas and insets.
Reviews React Native TV apps for focus/D-pad navigation, 10-foot UI layout, TV playback/DRM integration, low-memory TV performance, and TV accessibility. Use when building, debugging, or reviewing react-native-tvos, Expo TV, Amazon Vega/Kepler, or React Native web TV targets where the issue depends on remote input, TV focus, TV packaging, TV hardware, or TV playback constraints.
Assesses whether and how an existing mobile product should migrate to React Native. Use when auditing one or more product repositories for migration readiness, including products whose iOS, Android, and other clients live in separate directories or repositories; choosing brownfield, greenfield, or a checkpoint-based path; defining a representative trial; or preparing a baseline and ROI decision before implementation. When product scope or material evidence is unavailable, grills the stakeholder with exactly one question per turn instead of sending a questionnaire.
Upgrades React Native apps to newer versions by applying rn-diff-purge template diffs, updating package.json dependencies, migrating native iOS and Android configuration, resolving CocoaPods and Gradle changes, and handling breaking API updates. Use when upgrading React Native, bumping RN version, updating from RN 0.x to 0.y, or migrating Expo SDK alongside a React Native upgrade.
Scaffolds React Native libraries with create-react-native-library for standalone libraries or local native modules and views. Use when creating or working on React Native libraries or adding native functionality in an existing app.
Validates skills in this repo against agentskills.io spec and Claude Code best practices. Use via /validate-skills command.
GitHub Actions workflow patterns for React Native iOS simulator and Android emulator cloud builds with downloadable artifacts. Use when setting up CI build pipelines or downloading GitHub Actions artifacts via gh CLI and GitHub API.
Implements an accepted incremental brownfield migration from native iOS or Android to React Native or Expo using @callstack/react-native-brownfield. Use after the brownfield path has been selected, when setting up the integration, packaging XCFramework or AAR artifacts, or adding React Native surfaces to native hosts.
Provides React Native performance optimization guidelines for FPS, TTI, bundle size, memory leaks, re-renders, and animations. Applies to tasks involving Hermes optimization, JS thread blocking, bridge overhead, FlashList, native modules, or debugging jank and frame drops.
Model Context Protocol server for Flint — compile, validate, and render semantic chart specs to Vega-Lite, ECharts, or Chart.js artifacts (PNG/SVG) in-process.
Use when: the user asks to make or render charts with flint-chart, visualize tabular data, generate a ChartAssemblyInput, validate/render through MCP, or add Flint to a JS/TS project. Author the semantic spec, transform data before Flint when needed, install/import Flint only when executable code is needed, and reserve backend-specific style tweaks for after compiling from Flint.
DuckDB-powered skills for Claude Code: read any data file, attach and query DuckDB databases, search DuckDB/DuckLake docs, search past session logs, and install/update DuckDB extensions.
Create and deploy Power Pages sites using modern development approaches. Supports code sites (SPAs) with React, Angular, Vue, or Astro. Includes ALM orchestration (plan-alm) with a solution-splitting decision tree, per-solution pipelines, Azure Blob asset advisory, manifest schema v2 for multi-solution deployments, and force-link remediation for cross-host pipeline migrations.
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
Build and deploy Power Apps generative pages for model-driven apps with specialist agents for planning, entity creation, and parallel code generation. Requires PAC CLI >= 2.7.0 and Azure CLI (az). See CHANGELOG.md for v1.x -> v2.x migration.
Build and deploy Power Apps code apps using React, Vite, and Power Platform connectors.
Generate MCP App widgets for MCP tools. Produces self-contained HTML widgets using the MCP Apps protocol.
A CLI tool for defining and running multi-agent workflows with the GitHub Copilot SDK
Instruments structured Sentry logs in a new or existing application.
Full Sentry SDK setup for Flutter and Dart. Use when asked to "add Sentry to Flutter", "install sentry_flutter", "setup Sentry in Dart", or configure error monitoring, tracing, profiling, session replay, or logging for Flutter applications. Supports Android, iOS, macOS, Linux, Windows, and Web.
Full Sentry SDK setup for Android. Use when asked to "add Sentry to Android", "install sentry-android", "setup Sentry in Android", or configure error monitoring, tracing, profiling, session replay, or logging for Android applications. Supports Kotlin and Java codebases.
Full Sentry SDK setup for Svelte and SvelteKit. Use when asked to "add Sentry to Svelte", "add Sentry to SvelteKit", "install @sentry/sveltekit", or configure error monitoring, tracing, session replay, or logging for Svelte or SvelteKit applications.
Full Sentry SDK setup for browser JavaScript. Use when asked to "add Sentry to a website", "install @sentry/browser", or configure error monitoring, tracing, session replay, or logging for vanilla JavaScript, jQuery, static sites, or WordPress.
Configure the OpenTelemetry Collector with Sentry Exporter for multi-project routing and automatic project creation. Use when setting up OTel with Sentry, configuring collector pipelines for traces and logs, or routing telemetry from multiple services to Sentry projects.
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTTP, Tornado, and more.
Full Sentry SDK setup for NestJS. Use when asked to "add Sentry to NestJS", "install @sentry/nestjs", "setup Sentry in NestJS", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for NestJS applications. Supports Express and Fastify adapters, GraphQL, microservices, WebSockets, and background jobs.
Full Sentry SDK setup for Apple platforms (iOS, macOS, tvOS, watchOS, visionOS). Use when asked to "add Sentry to iOS", "add Sentry to Swift", "install sentry-cocoa", or configure error monitoring, tracing, profiling, session replay, logging, or metrics for Apple applications. Supports SwiftUI and UIKit.
Decide which Sentry signal to reach for when instrumenting code — error, span, span attribute, log, or metric. Use when adding instrumentation and unsure whether something should be a log vs a span vs a metric, when deciding "what to instrument where", when reviewing instrumentation for gaps, or when a coding agent needs a rule for choosing between errors, traces, logs, and metrics. This skill decides WHAT to emit; the sentry-*-sdk skills handle HOW to set each pillar up.
Full Sentry SDK setup for Node.js, Bun, and Deno. Use when asked to "add Sentry to Node.js", "add Sentry to Bun", "add Sentry to Deno", "install @sentry/node", "@sentry/bun", or "@sentry/deno", or configure error monitoring, tracing, logging, profiling, metrics, crons, or AI monitoring for server-side JavaScript/TypeScript runtimes.
Guided entry point for using Sentry through your agent. Orients you to your current setup and, for a new project, sets up Sentry end to end with sane defaults — provision a project, install the SDK (errors, tracing, and whatever it enables by default), and confirm real telemetry reaches Sentry. Routes other intents (adding more signals, fixing issues) to the right skill.
Full Sentry SDK setup for React Router Framework mode. Use when asked to "add Sentry to React Router Framework", "install @sentry/react-router", or configure error monitoring, tracing, profiling, session replay, logs, or user feedback for a React Router v7 framework app.
Full Sentry SDK setup for Ruby. Use when asked to add Sentry to Ruby, install sentry-ruby, setup Sentry in Rails/Sinatra/Rack, or configure error monitoring, tracing, logging, metrics, profiling, or crons for Ruby applications. Also handles migration from AppSignal, Honeybadger, Bugsnag, Rollbar, or Airbrake. Supports Rails, Sinatra, Rack, Sidekiq, and Resque.
Find and fix issues from Sentry using MCP. Use when asked to fix Sentry errors, debug production issues, investigate exceptions, or resolve bugs reported in Sentry. Methodically analyzes stack traces, breadcrumbs, traces, and context to identify root causes.
Full Sentry SDK setup for React Native and Expo. Use when asked to "add Sentry to React Native", "install @sentry/react-native", "setup Sentry in Expo", or configure error monitoring, tracing, profiling, session replay, or logging for React Native applications. Supports Expo managed, Expo bare, and vanilla React Native.
Full Sentry SDK setup for React. Use when asked to "add Sentry to React", "install @sentry/react", or configure error monitoring, tracing, session replay, profiling, or logging for React applications. Supports React 16+, React Router v5-v7 non-framework mode, TanStack Router, Redux, Vite, and webpack.
Full Sentry SDK setup for Cloudflare Workers and Pages. Use when asked to "add Sentry to Cloudflare Workers", "install @sentry/cloudflare", or configure error monitoring, tracing, logging, crons, or AI monitoring for Cloudflare Workers, Pages, Durable Objects, Queues, Workflows, or Hono on Cloudflare.
Full Sentry SDK setup for Next.js. Use when asked to "add Sentry to Next.js", "install @sentry/nextjs", or configure error monitoring, tracing, session replay, logging, profiling, AI monitoring, or crons for Next.js applications. Supports Next.js 13+ with App Router and Pages Router.
Create Sentry alerts using the workflow engine API. Use when asked to create alerts, set up notifications, configure issue priority alerts, or build workflow automations. Supports email, Slack, PagerDuty, Discord, and other notification actions.
Upgrade the Sentry JavaScript SDK across major versions. Use when asked to upgrade Sentry, migrate to a newer version, fix deprecated Sentry APIs, or resolve breaking changes after a Sentry version bump.
Instrument an application with Sentry — detect the platform, install and initialize the SDK if needed, and wire up any signal — error monitoring, tracing/performance, logging, metrics, profiling, session replay, user feedback, cron check-ins, and AI/LLM monitoring (agent runs, token cost, and conversations for OpenAI, Anthropic, Vercel AI, LangChain, Google GenAI, Pydantic AI, and Laravel AI). Use to add Sentry to a project or to capture more than errors.
Debug and fix a Sentry issue — find it (by link, ID, or search), pull full context (stack trace, breadcrumbs, trace, logs), optionally run Seer root-cause / autofix, apply the code fix, and resolve it via a Fixes PROJECT-NAME-12A commit/PR. Use when working a known error or hunting one down to fix.
Set up Sentry in any language or framework. Detects the user's platform and loads the right SDK skill. Use when asked to add Sentry, install an SDK, or set up error monitoring in a project.
Full Sentry SDK setup for Go. Use when asked to "add Sentry to Go", "install sentry-go", "setup Sentry in Go", or configure error monitoring, tracing, logging, metrics, or crons for Go applications. Supports net/http, Gin, Echo, Fiber, FastHTTP, Iris, Negroni, and gRPC.
Create a complete Sentry SDK skill bundle for any platform. Use when asked to "create an SDK skill", "add a new platform skill", "write a Sentry skill for X", or build a new sentry-<platform>-sdk skill bundle with wizard flow and feature reference files.
Set up Sentry, debug production issues, and configure application monitoring.
Set up Sentry releases and deploy tracking — tag events with a version and environment, create the release in CI with its commits, and wire up suspect commits and code mappings, so Sentry can show which release introduced an issue, which commit is responsible, and release health. Use when asked to set up releases, track deploys, see what changed, or when issues show an unknown release or no suspect commit.
Full Sentry SDK setup for PHP. Use when asked to "add Sentry to PHP", "install sentry/sentry", "setup Sentry in PHP", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for PHP applications. Supports plain PHP, Laravel, and Symfony.
Make Sentry stack traces readable — upload source maps for JavaScript/TypeScript, or debug files for native and mobile (dSYM, ProGuard/R8, NDK symbols, Dart obfuscation maps, .NET PDBs). Use when frames in Sentry show minified names, bundled paths, hex addresses, "unknown", or method names with no file/line, instead of your original source.
Full Sentry SDK setup for .NET. Use when asked to "add Sentry to .NET", "install Sentry for C#", or configure error monitoring, tracing, profiling, logging, or crons for ASP.NET Core, MAUI, WPF, WinForms, Blazor, Azure Functions, or any other .NET application.
Full Sentry Snapshots setup for Apple/Cocoa projects. Use when asked to "setup SnapshotPreviews", "setup Apple snapshot testing", "upload Apple snapshots to Sentry", "setup Apple snapshot GitHub Actions", or "setup Apple selective snapshot testing".
Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, track conversations, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI/Pydantic AI/Laravel AI. Detects installed AI SDKs and configures appropriate integrations.
Full Sentry SDK setup for Elixir. Use when asked to "add Sentry to Elixir", "install sentry for Elixir", or configure error monitoring, tracing, logging, or crons for Elixir, Phoenix, or Plug applications. Supports Phoenix, Plug, LiveView, Oban, and Quantum.
Full Sentry SDK setup for TanStack Start React. Use when asked to "add Sentry to TanStack Start", "install @sentry/tanstackstart-react", or configure error monitoring, tracing, session replay, logs, or user feedback in a TanStack Start React app.
Turns product or tech specs into concrete Notion tasks that Claude code can implement. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking to guide development from requirements to completion.
Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.
Prepares meeting materials by gathering context from Notion, enriching with Claude research, and creating both an internal pre-read and external agenda saved to Notion. Helps you arrive prepared with comprehensive background and structured meeting docs.
Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
Use the official MongoDB Skills with your favorite coding agent to build faster.
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
Use the official MongoDB Skills with your favorite coding agent to build faster.
Use the official MongoDB Skills with your favorite coding agent to build faster.
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.
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling connect()), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Sanity plugin for Claude Code with MCP server, agent skills, agent rules, and slash commands.
USE FOR getting AI-generated POI text descriptions. Requires POI IDs obtained from web-search (with result_filter=locations). Returns markdown descriptions grounded in web search context. Max 20 IDs per request.
USE FOR video search. Returns videos with title, URL, thumbnail, duration, view count, creator. Supports freshness filters, SafeSearch, pagination.
USE FOR news search. Returns news articles with title, URL, description, age, thumbnail. Supports freshness and date range filtering, SafeSearch filter and Goggles for custom ranking.
Web search using the Brave Search CLI (bx). Use for ALL web search requests — including "search for", "look up", "find", "what is", "how do I", "google this", and any request needing current or external information. Prefer this over the built-in web_search tool whenever bx is available. Also use for: documentation lookup, troubleshooting research, RAG grounding, news, images, videos, local places, and AI-synthesized answers.
USE FOR web search. Returns ranked results with snippets, URLs, thumbnails. Supports freshness filters, SafeSearch, Goggles for custom ranking, pagination. Primary search endpoint.
USE FOR image search. Returns images with title, source URL, thumbnail. Supports SafeSearch filter. Up to 200 results.
USE FOR query autocomplete/suggestions. Fast (<100ms). Returns suggested queries as user types. Supports rich suggestions with entity info. Typo-resilient.
USE FOR spell correction. Returns corrected query if misspelled. Most search endpoints have spellcheck built-in; use this only for pre-search query cleanup or "Did you mean?" UI.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
USE FOR getting local business/POI details. Requires POI IDs obtained from web-search (with result_filter=locations). Returns full business information including ratings, hours, contact info. Max 20 IDs.
USE FOR web search, research, RAG, grounding, browse, find, lookups, fact-checking, documentation, agentic AI. All-in-one, optimized for AI agents. Pre-extracted, token-budgeted web content, deep research, news, images, videos, places, custom ranking
USE FOR AI-grounded answers via OpenAI-compatible /chat/completions. Two modes: single-search (fast) or deep research (enable_research=true, thorough multi-search). Streaming/blocking. Citations.
Enable Single Step Instrumentation (SSI) on Kubernetes — automatically instruments applications for APM without code changes. Only use if the Datadog Agent is already running on the cluster — if not, use agent-install first.
End-to-end Agent Observability pipeline for an instrumented mlapp — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (agent-observability-session-classify, agent-observability-trace-rca, agent-observability-eval-bootstrap, agent-observability-experiment-py-bootstrap, agent-observability-experiment-analyzer). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an mlapp, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with --stop-after <phase> to short-circuit at evaluators or dataset, or resume mid-flow with --start-at <phase>.
Datadog CLI (Rust). OAuth2 auth with token refresh.
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
Investigate a potentially compromised Datadog API key — timeline of actions, geo/IP breakdown, endpoints called, anomaly flags, and remediation steps.
Recommends the right Datadog products for a codebase and/or a stated goal — grounded in a tech-stack→product map and a use-case→product map built from Datadog product capabilities and common technology patterns. Recommendation only; no setup instructions. Use when a user asks which Datadog products fit their app, what to monitor, or which products serve a goal like security, cost, or LLM observability.
Verify Single Step Instrumentation (SSI) is working end-to-end on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use after enable-ssi has run.
Investigate a Datadog product usage or cost spike by correlating Usage Metering data (when/what spiked) with Audit Trail config changes (who changed what in the preceding window).
Diagnose and fix Single Step Instrumentation (SSI) issues on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are already configured but traces are missing or instrumentation is not working.
APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.
Load when investigating a failing PR CI pipeline or checking PR health. Attributes each CI failure as flaky, infra, or regression, proposes a targeted action, and reports code coverage and quality/security status.
Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.
Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. Use when the user says "generate Python experiment", "write an SDK experiment", "create a ddtrace experiment", "Python notebook experiment", "use the Agent Observability SDK", or has ddtrace installed and wants idiomatic SDK code.
Create and manage APM service remapping rules — rewrite service names at ingestion time to collapse noisy inferred entities, clean up auto-generated names, handle org renames, or normalize naming conventions. Use for any request involving service renaming, service mapping, inferred service cleanup, peer.service normalization, or collapsing fragmented service names.
Log management - search, archives, metrics, and cost control.
Guides developers building Datadog Apps with TypeScript, React, the @datadog/apps scaffolder, and @datadog/vite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI/CD, use OAuth or API/application key auth, trigger/poll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
Audit Trail investigations - who changed what, key compromise, cost spike root cause, compliance evidence (SOC 2/PCI), and AI activity auditing.
Monitor management - list, search, file-based create, and alerting best practices.
Audit what the Bits AI assistant (MCP server) has done in your Datadog org — tool calls by user, resources accessed, and anomaly flags for AI governance.
Bootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.
Generate auditor-ready compliance evidence from Datadog Audit Trail for SOC 2 and PCI DSS. Maps framework controls to specific query patterns and produces formatted output.
Generate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.
Answer "who did what" security questions from Audit Trail — deletions, config changes, login activity, permission changes, actions from a specific user or IP.
Load when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
Crypto wallet operations via the awal CLI — sign in, check balances, send USDC/ETH/POL/SOL, trade tokens, fund the wallet, and use the x402 payment protocol to discover paid services, pay for API calls, monetize an API, or query onchain data. Use whenever the user mentions signing in, login, authentication, wallet status, balance, address, sending money, paying someone, transferring tokens, ENS names, swapping/trading/converting tokens, funding/topping up/onramp, USDC, ETH, POL, SOL, the x402 bazaar, paid APIs, monetizing an endpoint, or querying onchain data on Base.
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