Skills
99,951 packaged capabilities and playbooks for agents. Install any of them with one command.
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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.
Use when designing or critiquing aggregate boundaries — phrases like "aggregate", "aggregate root", "should these be one entity or two", "consistency boundary", "transactional consistency", "this aggregate is huge", "should I reference by ID or by object". Defines aggregates as transactional consistency boundaries and the rules for entity references within and across them
Use when identifying DDD anti-patterns and smells in code or design — phrases like "anemic domain model", "primitive obsession", "leaky repository", "smart UI", "transaction script", "god aggregate", "big ball of mud". Catalogs the common DDD anti-patterns with symptoms, why each is wrong, and the pattern skill that explains the fix
Use when mapping relationships between bounded contexts — phrases like "context map", "how do these two services talk", "should we use an anticorruption layer", "shared kernel vs separate ways", "upstream/downstream", "what integration pattern fits here". Catalogs the seven relationship patterns (Shared Kernel, Customer-Supplier, Conformist, Anticorruption Layer, Open Host Service, Published Language, Separate Ways) and the signals that pick one.
Use when the model is "working but feels off" and the user wants to deepen it — phrases like "supple design", "refactor toward deeper insight", "implicit concept", "the model isn't quite right yet", "what would a better model look like", "intention-revealing interfaces", "side-effect-free functions", "assertions in the domain", "conceptual contours". Covers Evans's Part III techniques for evolving a model past its first draft.
Use when behavior does not naturally belong to a single entity or value object—phrases like “domain service”, “this logic spans multiple aggregates”, “where does this go”, “the entity is starting to know too much”, “we need a calculator/coordinator that isn't a thing”. Defines when to introduce a stateless domain service instead of forcing behavior onto an entity. Distinguishes domain services from application and infrastructure services.
Use when creation of an aggregate or complex value object requires non-trivial work — phrases like "factory", "the constructor is doing too much", "construction has business meaning", "how do I create this aggregate", "constructor with twelve parameters". Defines when to introduce a factory and when a constructor or static creation method suffices
Use when the user wants concrete code that realizes a DDD pattern — phrases like "scaffold an entity for X", "write the Aggregate for Y", "give me the repository interface", "implement this value object". Drives the conversation through naming + invariants and then delegates code generation to a language adapter skill (e.g., ddd-kotlin-idioms for Kotlin). Do NOT use for refactoring (route to ddd-refactor), review (route to ddd-review), or design from scratch (route to ddd-design)
Use when designing how aggregates are persisted and retrieved—phrases like “repository”, “how should I load this aggregate”, “JPA repository vs DDD repository”, “leaky repository”, “finder methods”, “the repository is doing too much”. Defines a repository as the abstraction that gives the illusion of an in-memory collection of aggregates. One repository per aggregate root.
Use when the user asks for a PR-style code review for DDD smells—phrases like “is this DDD-clean”, “review this for domain modeling issues”, “what's wrong with this aggregate”, “audit this module for DDD”. Scans a file, diff, or module against the DDD anti-pattern catalog and reports specific violations with citations to the relevant pattern skill. Do NOT use when the user wants new code (route to ddd-implement) or wants to design from scratch (route to ddd-design).
Use when deciding where to spend modeling effort — phrases like "core domain", "supporting subdomain", "generic subdomain", "what's worth building vs buying", "where should the senior engineers work", "is this our competitive edge or just plumbing". Distinguishes Core (competitive edge), Supporting (custom but not differentiating), and Generic (commodity, buy/library) subdomains
Use when the user is modeling a descriptive concept with no identity — phrases like "value object", "Money", "Address", "DateRange", "primitive obsession", "wrap this primitive into a type", "two instances with the same attributes are the same". Distinguishes from entities; pairs with ddd-entity
Use when a domain term needs defining, a glossary term is being used inconsistently or fuzzily, a hard-to-reverse decision needs an ADR, or existing ADRs need auditing, pruning, or archiving — produces glossary updates and a classified docs/adr tree (keep, archive, or drop a dead rejection). Also runs as a side-effect sub-skill when an interview or design skill calls for glossary and ADR upkeep.
When the user wants to balance demand and supply, resolve supply-demand mismatches, or optimize fulfillment allocation. Also use when the user mentions "demand-supply balancing," "ATP," "available-to-promise," "allocation," "supply rationing," "fulfillment optimization," "demand prioritization," or "supply allocation." For forecasting, see demand-forecasting. For S&OP, see sales-operations-planning.
Compare Figma designs against implementation screenshots, identifying layout, typography, color, and sizing discrepancies. Generates a structured visual review table and an interactive HTML comparison page with swipe and side-by-side modes. Use when the user asks to compare design with preview, compare Figma with screenshot, check design implementation, or provides a Figma URL alongside a screenshot.
Detect DLL side-loading and search-order hijacking (MITRE T1574) where adversaries plant malicious DLLs for legitimate signed applications to load, by analyzing Sysmon Event ID 7 DLL-load events, checking signatures/hashes against known-good versions, and flagging path anomalies with EDR tools like CrowdStrike, MDE, or SentinelOne. Use when investigating EDR alerts on unsigned DLLs, hunting for APT persistence via trojanized applications, or triaging incidents involving DLL hijacking.
Unity of knowledge and action: proactively identify real development work that is starting, in progress, or just finished; run an evidence-based pre-check before the first write to code, Git, or an external system; cite only confirmed constraints during execution; and after completion, once Git/test/MR evidence is obtained, automatically run a retrospective and safe evolution. Development intents in natural language — implementation, modification, debugging, refactoring, fixing, building, development verification, committing code, and Skill feature changes — should implicitly invoke it even if $development-brain is not written; pure product analysis, formal QA, release and deployment, concept explanations, ordinary Q&A, and read-only assessments of whether to update a Skill do not trigger it.
Use when the user needs to deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
When the user wants to optimize Direct Store Delivery (DSD) routes, plan delivery schedules for retail stores, or improve route efficiency for CPG distribution. Also use when the user mentions "DSD routing," "store delivery optimization," "retail route planning," "delivery windows," "merchandising routes," or "field sales routing." For general vehicle routing, see vehicle-routing-problem. For last-mile delivery, see last-mile-delivery.
Expert in course platform selection and implementation - comparing Teachable, Kajabi, Thinkific, and custom solutions. Covers LMS selection criteria, platform migrations, tech stack decisions, and building vs buying. Use when "course platform, lms, teachable, kajabi, thinkific, course hosting, learning management system, which platform" mentioned.
Guide for performing Node.js version upgrades in the Electron project. Use when working on the roller/node/main branch to fix patch conflicts during `e sync --3`. Covers the patch application workflow, conflict resolution, analyzing upstream Node.js changes, building, running the Node.js test suite, and proper commit formatting for patch fixes.
Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.
Evaluate project specifications at inception (Step One), deterministically assess Scale & Load, Domain Decoupling, and Resource Intensity vectors, and automatically enforce a distributed Microservices Architecture when warranted, preventing monolith scaffolding.
Design and author structured technical proposals, responsive artifacts, architecture diagrams, Mermaid charts, and interactive components. Use when creating standalone markdown reports, architectural specifications, Mermaid diagrams, or interactive artifact widgets — even if the user does not explicitly say "fable-artifact" (e.g. "create an artifact", "draw an architecture diagram", "write a technical proposal", "generate a mermaid flowchart"). Do NOT use for quick one-line conversational answers.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.