Agent Skills

by medusajs

Load automatically when planning, researching, or implementing ANY Medusa backend features (custom modules, API routes, workflows, data models, module links, business logic). REQUIRED for all Medusa backend work in ALL modes (planning, implementation, exploration). Contains architectural patterns, best practices, and critical rules that MCP servers don't provide.

new-user

by medusajs

Create an admin user in Medusa

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.

ffuf-web-fuzzing

by jthack

Expert guidance for ffuf web fuzzing during penetration testing, including authenticated fuzzing with raw requests, auto-calibration, and result analysis

building-admin-dashboard-customizations

by medusajs

Load automatically when planning, researching, or implementing Medusa Admin dashboard UI (widgets, custom pages, forms, tables, data loading, navigation). REQUIRED for all admin UI work in ALL modes (planning, implementation, exploration). Contains design patterns, component usage, and data loading patterns that MCP servers don't provide.

building-storefronts

by medusajs

Load automatically when planning, researching, or implementing Medusa storefront features (calling custom API routes, SDK integration, React Query patterns, data fetching). REQUIRED for all storefront development in ALL modes (planning, implementation, exploration). Contains SDK usage patterns, frontend integration, and critical rules for calling Medusa APIs.

mcloud-environments

by medusajs

Execute mcloud environments commands to list, get, create, delete, redeploy, or trigger builds for Cloud environments. Use when managing environment lifecycle, redeploying after variable changes, or starting new builds from source.

mcloud-projects

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.

db-generate

by medusajs

Generate database migrations for a Medusa module

mcloud-logs

by medusajs

Execute mcloud logs to fetch and stream runtime logs for Cloud environments. Use when reading backend or storefront logs, filtering by time range, searching for errors, or scoping logs to a specific deployment.

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.

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.

mcloud-deployments

by medusajs

Execute mcloud deployments commands to list deployments, retrieve deployment details, and fetch build logs. Use when listing deployments, checking deployment status, or reading build output for debugging build failures.

mcloud-organizations

by medusajs

Execute mcloud organizations commands to list or get Cloud organizations. Use when discovering organizations, resolving organization IDs by name, or retrieving organization details including members and subscription.

🔥🔥✓ VerifiedFree

using-medusa-cloud

by medusajs

Manages Medusa Cloud resources through the Cloud CLI (mcloud). Use when deploying, debugging deployments, managing environments, environment variables, or any Medusa Cloud operation. CRITICAL for mcloud commands, deployment failures, build logs, Cloud setup, and CI/CD workflows.

learning-medusa

by medusajs

Load automatically when user asks to learn Medusa development (e.g., "teach me how to build with medusa", "guide me through medusa", "I want to learn medusa"). Interactive guided tutorial where Claude acts as a coding bootcamp instructor, teaching step-by-step with checkpoints and verification.

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).

db-migrate

by medusajs

Run database migrations in Medusa

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