Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
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Domain-driven design (DDD) modeling and architecture guidance. Used to create domain models, design bounded contexts, and implement aggregate roots, entities, value objects, domain services and domain events. Supports strategic design (context mapping, subdomain decomposition) and tactical design (aggregates, repositories, factory patterns). Use when the user needs domain modeling, microservice decomposition or complex business logic design.
Designing Data-Intensive Applications (DDIA) distilled reference guide by Martin Kleppmann. MUST be loaded when: designing database schemas, choosing storage engines, implementing replication or partitioning, handling distributed transactions, building batch/stream processing pipelines, choosing consistency models, implementing consensus, designing data flow architectures, evaluating trade-offs between availability and consistency, encoding/serialization decisions, data modeling (relational vs document vs graph), building fault-tolerant systems, or any system design and architecture discussion involving data-intensive applications. Trigger on: database design, replication, partitioning, sharding, transactions, isolation levels, consistency, consensus, CAP theorem, batch processing, stream processing, MapReduce, Kafka, event sourcing, CDC, OLTP, OLAP, B-tree, LSM-tree, data warehouse, schema evolution, encoding formats, distributed systems, fault tolerance, leader election, quorum.
Guides deal operations administration—quote-to-cash coordination, deal desk intake and routing, CRM opportunity hygiene, order form and SOW assembly, approval workflows, signature tracking, and handoffs to legal, finance, and provisioning after customer signature. Use when processing a sales deal, preparing order paperwork, running deal desk checklist, fixing CRM stage/fields, coordinating signatures, or closing the loop post-signature—not for contract legal redlines (commercial-counsel), board or equity approvals (corporate-counsel), ASC 606 accounting (senior-revenue-accountant), support tickets after go-live (customer-ops-specialist), or M&A/financing closing (transaction-manager).
Systematic debugging methodology, language-specific debuggers, and common scenario playbooks. Use for: debug, debugging, bug, crash, hang, memory leak, race condition, deadlock, bisect, reproduce, root cause, breakpoint, profiling, performance issue, segfault, stack trace, core dump.
Debug React Native issues systematically. Use when encountering native module errors like "Native module cannot be null", Metro bundler issues including port conflicts and cache corruption, platform-specific build failures for iOS CocoaPods or Android Gradle, bridge communication problems, Hermes engine bytecode compilation failures, red screen fatal errors, or New Architecture migration issues with TurboModules and Fabric renderer.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
Reviews proposed or existing work for unnecessary complexity and triages complexity-increasing recommendations without editing the reviewed targets. Use this skill when asked to simplify or decomplex source, tests, configuration, dependencies, plans, designs, or architecture; challenge over-engineering; audit unnecessary complexity; or triage recommendations that would add complexity. Do not use for general defect review, implementation or fixes, style-only cleanup, or conceptual explanations that need no durable review report.
Structured research → plan → annotate → implement workflow for non-trivial features. Separates planning from execution using persistent markdown artifacts (.claude/research.md, .claude/plan.md). Use when explicitly invoked via /deep-plan for feature development, refactors, migrations, or any multi-file change where architecture decisions matter.
Track deep work sessions locally (start/stop/status) and generate a GitHub-contribution-graph style minutes-per-day heatmap for sharing (e.g., via Telegram). Use when the user says things like “start deep work”, “stop deep work”, “am I in a session?”, “show my deep work graph”, or asks to review deep work history.
Delivers a clear goal until done — an app, a feature, or a fix. Plans a living goal.md under goals/ as an ordered list of phases the user confirms, then loops one phase-deliverer subagent per phase: load the skills the phase needs, write phase.md plus ≤7 task files (sources, steps, verification), implement each task with a commit, and adjust the remaining phases from its findings. Halts when a task is blocked or the goal's checks pass. Use only when the user names this skill (`/delivering-goal`, `$delivering-goal`, or "delivering-goal"), or references a file under a marked goals root (`@goals/01-mvp/goal.md`, a phase or task file)
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
A hard pre-adoption gate for every third-party dependency, in any language. Before a new package — or a major version bump — is added in js, go, python, kotlin, rust, ruby, java, anything: resolve its exact identity, scan it for known CVEs, review its supply-chain behaviour on socket.dev and snyk, then install it inside a disposable, network-isolated Docker container with no host mounts to inspect its full transitive dependency tree and what it runs at install time. Nothing enters the project until it passes.
Use when the user asks to update, bump, or migrate Java, Kotlin, Gradle, Maven, Node, or TypeScript dependencies; audit direct or transitive dependencies for CVEs or security advisories; or remediate a dependency vulnerability. Preserves the package manager, applies small verified upgrades, and checks vulnerability, release-age, and supply-chain risk.
Deploy a Decentraland scene to Genesis City (LAND-based). Covers pre-deployment checklist, scene.json validation, spawn points, and common deployment errors. Use when the user wants to deploy, publish, go live, or upload to parcels they own. Do NOT use for Worlds deployment (see deploy-worlds).
Bulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.
Design an API end-to-end — from requirements to a validated OpenAPI spec. Generates stories for review, then produces a linted spec. Emmanuel Paraskakis's method for designing APIs with LLMs. Use when user says "/design-api" or asks to design an API from requirements or stories. For a single step only, use `design-api-stories`, `design-api-spec`, or `design-api-review` instead.
Resume work from a previous session checkpoint. Uses the `resume-context` CLI to load feature, checkpoint, git state, and session history in a single call, then synthesizes a focused resumption summary before continuing. Use at the start of a new session to restore context from a previous checkpoint.
Access the DexPaprika API, CLI, and streaming service to query DEX data including networks, pools, tokens, and trading activity. Use this skill when making HTTP requests to api.dexpaprika.com or streaming.dexpaprika.com, or when using dexpaprika-cli for blockchain DEX information.
Advanced python-docx patterns for handling nested tables, complex cell structures, and content extraction beyond basic .text property. Complements the official docx skill with specialized techniques for forms, checklists, and complex layouts.
A skill that analyzes existing code (not necessarily DDD-based) and proposes a domain model (aggregates, local entities, value objects, domain services) with pseudocode. It proposes a design including properties, methods, invariants, and aggregate boundaries. Target languages: language-agnostic. Triggers: requests for existing-code analysis and domain-model extraction such as "extract a domain model from this existing code", "I want to redesign this code with DDD", "propose a domain model", "I want to find aggregates", "I want to migrate to DDD", "analyze the existing code and create a domain model", "turn legacy code into DDD".
A specialized skill for testing FluentValidation validators. Use when you need to create tests for Validator classes, verify business rules, or test error messages. Covers full use of FluentValidation.TestHelper, ShouldHaveValidationErrorFor, async validation, cross-field logic, and more. Make sure to use this skill whenever the user mentions FluentValidation testing, Validator testing, ShouldHaveValidationErrorFor, TestHelper, or testing business validation rules, even if they don't explicitly ask for validation testing guidance. Keywords: validator, 驗證器, fluentvalidation, validation testing, UserValidator, CreateOrderValidator, TestHelper, ShouldHaveValidationErrorFor, ShouldNotHaveValidationErrorFor, TestValidate, TestValidateAsync, 測試驗證器, 驗證業務規則
Douyin account search tool. Enter keywords to find creator accounts on Douyin, with account name, follower count, total likes, following count, and post count all laid out at a glance; click a link to jump to the profile page for details. Use when the user needs to search Douyin accounts, find Douyin influencers, or learn about Douyin creators. Trigger words: 抖音账号搜索、抖音达人、抖音博主、搜抖音账号、抖音KOL、抖音网红.
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Implement Rails nested attributes with dynamic add/remove functionality using Turbo Streams and Simple Form. Use when building forms where users need to manage multiple child records (has_many associations), add/remove nested items without page refresh, or create bulk records inline.
End-to-end frontend testing with Playwright. Page Object Model, selectors, fixtures, accessibility audits. Limited E2E coverage currently - area for improvement. Keywords: Playwright, E2E, Page Object Model, POM, data-testid, getByRole, getByLabel, getByText, fixtures, axe-playwright, frontend testing
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.
Train a model on a novel, then continue writing in that novel's world — with memory of characters, places, and recent events. Load this skill whenever the user asks to train/fine-tune/learn the style of a novel or .txt, mentions Echo-Quill / Alchemist / 炼金 / 双塔, asks to open the training dashboard, asks how the system works, asks to stop or restart the training services, asks to write/continue text (with or without memory), asks to train an additional chapter or continuation they wrote, or asks to roll back to the original-novel state. The skill manages a local FastAPI backend + Vite/React dashboard, dependency installation, .env scaffolding, training, memory-mode continuation, pure-style continuation, and rollback — the user supplies a novel path or a passage, the skill handles everything underneath.
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.