Agent Skills

by medusajs

Execute mcloud projects commands to list, get, or delete Cloud projects. Use when discovering projects, resolving project handles by name, or retrieving project details including linked environments.

mcloud-variables

by medusajs

Execute mcloud variables commands to list and get environment variables for a Cloud environment. Use when inspecting, reading, or exporting environment variables. Never pass --reveal unless the user explicitly requests secret values.

creating-agents-in-medusa

by medusajs

Use when building an internal admin-facing AI agent in a Medusa project. These agents are operated by merchants and store operators — not customers. Covers data models, module service, agent runtime (tools, system prompt, streamText), streaming API routes (NDJSON), and admin UI chat extensions. Load for any internal agent type: store operations assistant, product audit, cohort analysis, customer service tooling for support staff, etc. Do NOT use for customer-facing agents (storefront chatbots, buyer-side assistants).

mcloud-auth

by medusajs

Execute mcloud authentication and context commands: login, logout, whoami, use, version, and signup. Use when setting up the CLI, switching accounts, verifying auth state, setting the active org/project/environment context, or checking the CLI version.

new-user

by medusajs

Create an admin user in Medusa

storefront-best-practices

by medusajs

ALWAYS use this skill when working on ecommerce storefronts, online stores, shopping sites. Use for ANY storefront component including checkout pages, cart, payment flows, product pages, product listings, navigation, homepage, or ANY page/component in a storefront. CRITICAL for adding checkout, implementing cart, integrating Medusa backend, or building any ecommerce functionality. Framework-agnostic (Next.js, SvelteKit, TanStack Start, React, Vue). Provides patterns, decision frameworks, backend integration guidance.

temporal-developer

✓★ 191

by temporalio

Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET, Ruby, and Rust. Use when the user is building workflows, activities, workers, or background job queues with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns. Also use when the user mentions "run a Temporal workflow from the CLI", "start a dev server", "run temporal server start-dev", "temporal workflow start", "temporal workflow execute", "temporal workflow signal", "temporal workflow query", "temporal workflow update".

agent-observability-auto-experiment

by datadog-labs

Run an iterative code-improvement hill-climb against real Datadog LLM-Obs data, locally, with Claude Code as the agent. Establishes a baseline eval, makes one focused change, re-scores with the same harness, keeps the change if it improves the score in the goal's direction (labeling within-noise gains tentative), and repeats. Use when the user says "run an auto experiment", "hill-climb this code", "iteratively improve X and measure the delta", "optimize this prompt/file against my traces", "auto-optimize against LLM-Obs", or wants the local equivalent of the auto_experiments worker. Works from an ml_app, a dataset_id, an annotation_queue_id (a queue of human-labelled interactions), a list of trace_ids, or (by exception) a local dataset file. The corpus and its val/test splits live in Datadog LLM-Obs Datasets, created once per run with a timestamp in their names.

agent-observability-build-eval-from-annotations

by datadog-labs

Fit a Datadog LLM-Obs evaluator to human labels. Takes an annotation queue, works out where in the trace the labelled property actually lives, drafts an LLM-judge that predicts the human label, scores that judge against the already-labelled rows with a metric agreed with the user, then hill-climbs it — inspect the errors, make one focused change, re-score, keep it only if it beats the best — for a bounded number of iterations, and finally publishes the winner to Datadog as a DISABLED evaluator (not a Datadog draft — a real evaluator with `enabled: false`). Use when the user says "build an eval from my annotations", "build an evaluator from the annotation queue", "turn my annotations into an evaluator", "learn an evaluator from my labels", "fit a judge to the annotation queue", "auto-label", "auto labelling", "automate this annotation queue", "scale up my human labels", or wants the rest of a queue graded the way the humans graded the first rows. Needs an annotation queue with at least two classes present in the human labels (e.g. one true and one false for a boolean).

agent-observability-experiment-bootstrap

by datadog-labs

Bootstrap a reproducible LLM Observability experiment through the Python ddtrace SDK or the Node dd-trace SDK. Use for experiment, dataset, evaluator, benchmark, regression, or LLM-as-a-judge scaffolding. The legacy Python invocation remains supported.

agent-observability-replay-trace

by datadog-labs

Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing evaluators, root-causing failed traces, or RUM/HTTP session replay.

dd-account-setup

by datadog-labs

Ensure the user has an authenticated Datadog account with a valid DD_API_KEY on the right region before any Datadog setup or instrumentation. Detects existing DD_API_KEY / DD_APP_KEY / DD_SITE, validates them against the Datadog API, and fixes the common wrong-region 403. If no usable key exists, signs the user in (OAuth) or creates a new account, then obtains and validates a key. Use this whenever a user needs a Datadog account or API key, hits a 403 / wrong-region error, or is about to run any Datadog *-setup or instrumentation skill.

dd-aws-integration

by datadog-labs

Set up the Datadog AWS integration with Terraform - creates the cross-account IAM role Datadog assumes (external ID, no stored credentials), attaches the permission policies Datadog publishes, and registers the account through datadog_integration_aws_account so AWS metrics, the resource catalog, and CSPM findings start flowing. Use when the user has AWS resources they want to monitor, wants to connect an AWS account to Datadog, asks to set up or repair the AWS integration, or needs the Datadog IAM role and external ID provisioned. Does not set up log forwarding.

dd-azure-integration

by datadog-labs

Set up the Datadog Azure integration with Terraform - creates an Entra ID app registration and service principal, assigns Monitoring Reader across the chosen subscriptions and management groups, grants the Microsoft Graph permissions Datadog needs for resource discovery, and registers the tenant so Azure metrics and resource collection start flowing. Use when the user wants to monitor Azure VMs, App Service, SQL Database, or AKS, wants to connect an Azure subscription or management group or tenant to Datadog, or asks to set up or repair the Azure integration. Does not set up log forwarding.

dd-gcp-integration

by datadog-labs

Set up the Datadog Google Cloud integration with Terraform - creates a service account in the host project, lets Datadog's delegate principal impersonate it via roles/iam.serviceAccountTokenCreator (no service-account keys), enables the required APIs, grants the monitoring roles across the chosen projects and folders, and registers the account through datadog_integration_gcp_sts. Use when the user wants to monitor GCP resources such as Compute Engine, Cloud SQL, GKE, Cloud Run, or Pub/Sub, wants to connect a GCP project or folder or organization to Datadog, or asks to set up or repair the GCP integration. Does not set up log forwarding.

dd-instrument-llmo

by datadog-labs

Instrument the current project with Datadog LLM Observability for Python or Node.js/Next.js backends that call LLMs or run AI agents. Detects the runtime and LLM framework, provisions credentials, adds SDK init (ddtrace/dd-trace) with the correct kwargs, persists the dependency into the deploy manifest, and audits session-ID plumbing for gaps — fixing them when found. Use when the user says "instrument this project with LLM Observability", "add LLM Observability", "monitor my AI app in Datadog", "add LLM spans", "add agent session tracking", or "verify/repair my LLM Observability setup".

dd-instrument-rum

by datadog-labs

Instrument browser-based web applications with Datadog Browser RUM. Detect the application framework, router, package manager, bundler, entrypoint, credentials, and existing RUM setup; add or safely complete classic Browser RUM instrumentation for React, Next.js App or Pages Router, Angular, Vue, Nuxt, Svelte, vanilla JavaScript, SPAs, and iframe-hosted apps; avoid duplicate initialization; and verify the application still builds. Use when asked to add, set up, instrument, repair, or verify Datadog RUM, Browser Monitoring, Session Replay, or framework-specific Browser RUM plugins.

dd-oci-integration

by datadog-labs

Set up the Datadog Oracle Cloud Infrastructure (OCI) integration with Terraform - verifies ~/.oci/config, then applies Datadog's official oracle-cloud-integration module to create the Datadog service user, group, IAM policies, and API key in the tenancy and register it with Datadog, optionally including log collection. Use when the user has Oracle Cloud resources, wants to monitor an OCI tenancy, wants to connect OCI to Datadog, or asks to set up or repair the OCI integration.

dd-orchestrator

by datadog-labs

Entry point for Datadog onboarding. Takes a developer's plain-language goal, ensures a valid Datadog account with dd-account-setup, asks dd-product-recommender which products fit, detects the project's platform and cloud, then composes an ordered plan across the existing skills (agent install, product enable, verify, and optional cloud integration) and dispatches to each by source URL — honestly flagging products with no skill yet. Use when the user says "set up Datadog", "onboard my app / this repo to Datadog", "instrument my project", or states a monitoring goal without naming a specific product or skill.

dd-product-recommender

by datadog-labs

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.

dv-connect

by microsoft

One-step setup for a Dataverse environment — installs tools, authenticates, registers the MCP server, and writes `.env`. Use when starting a new project, switching environments, fixing authentication, or troubleshooting an MCP connection that won't come up.

dv-overview

by microsoft

Tool routing and cross-cutting rules for Dataverse work — which skill applies to which task, environment-confirmation, and pull-to-repo. Use when the user mentions Dataverse, Dynamics 365, Power Platform, or CRM; this skill picks the specialist (dv-connect / dv-data / dv-metadata / dv-query / dv-solution / dv-admin / dv-security) for the request.

dv-security

by microsoft

Security-role assignment, user access, application users, business units, and admin self-elevation in Dataverse environments. Use when the user wants to give someone access, grant a role, become an admin, or add a service principal.

dv-solution

by microsoft

Dataverse solution lifecycle — create, export, import, promote across environments, and validate deployments. Use when the user wants to package customizations, deploy to another environment, or move work between dev / test / prod.

suggest

✓★ 159

by brave

USE FOR query autocomplete/suggestions. Fast (<100ms). Returns suggested queries as user types. Supports rich suggestions with entity info. Typo-resilient.

agent-install

by datadog-labs

Install the Datadog Agent on Linux hosts via SSH with Single Step Instrumentation (SSI) enabled — SSI automatically instruments applications for APM without code changes. Only use if no agent is installed yet.

agent-observability-eval-bootstrap

by datadog-labs

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.

agent-observability-eval-pipeline

by datadog-labs

End-to-end Agent Observability pipeline for an instrumented ml_app — 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-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 ml_app, 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>`.

agent-observability-experiment-analyzer

by datadog-labs

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.

agent-observability-session-classify