Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
All
When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.
Guide for Convex schema design, validators, and TypeScript types. Use when defining database schemas, creating validators for function arguments/returns, working with document types, or ensuring type safety. Activates for schema.ts creation, validator usage, Id/Doc type handling, or TypeScript integration tasks.
When the user wants to write, rewrite, or improve marketing copy for any page — including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for", "improve this copy", "rewrite this page", "marketing copy", "headline help", or "CTA copy." For email copy, see email-sequence. For popup copy, see popup-cro.
Multi-agent system research agent for CoWork OS. Use when: researching multi-agent papers, frameworks, production case studies; maintaining a research queue; producing CoWork OS applicability analysis and implementation recommendations. Triggers: 'multi-agent research', 'research multi-agent systems', 'multi-agent papers', 'agent orchestration research', 'CoWork OS research'.
This skill should be used when the user asks to “create an agent”, “make an agent”, “write an agent”, “build a subagent”, “add an agent to a plugin”, “design an autonomous agent”, “generate an agent file”, “write a system prompt for an agent”, “what frontmatter does an agent need”, or “create a specialized agent”. Not for skills or commands—use create-skill.
Scaffold a new Decentraland SDK7 scene project. Creates scene.json, package.json, tsconfig.json, and src/index.ts. Covers scene.json schema (parcels, spawnPoints, permissions, featureToggles), multi-parcel layouts, and project structure. Use when the user wants to start a new scene, create a project, or set up from scratch. Do NOT use for deployment (see deploy-scene or deploy-worlds).
Create a structured feature specification with self-contained task files organized into parallel execution waves. Use this skill when the user says "create a spec", "plan this feature", "write up an implementation plan", "break this into tasks", or after any planning conversation where the user wants to capture decisions as actionable spec files. Also use when the user says "/create-spec" or wants to decompose a feature into work items that agents can implement independently. This skill produces local spec files under specs/{feature}/ — no GitHub integration.
Automatically detect the design style of a webpage and apply matching pure-CSS hover effects. Zero JS, zero dependencies, no SSR issues. Covers navigation, cards, buttons, icons, tags, borders, shadows, and speech bubbles. Sourced from Hover.css (29.4k ★) and modernized.
HTTP request construction and API testing with curl and HTTPie. Use when user asks to "test API", "make HTTP request", "curl POST", "send request", "test endpoint", "debug API", "upload file", "check response time", "set auth header", "basic auth with curl", "send JSON", "test webhook", "check status code", "follow redirects", "rate limit testing", "measure API latency", "stress test endpoint", "mock API response", or any HTTP calls from the command line.
Design and audit customer training academies for onboarding, admin enablement, role-based curricula, certification, labs, templates, webinars, office hours, self-serve courses, localization, accessibility, lifecycle triggers, partner/customer education, adoption metrics, support deflection, and content governance. Use when education must improve activation, retention, expansion, and support quality.
Generate daily design inspiration & novel things report. Use when user asks for 'design news', 'design daily', 'design inspiration', '设计日报', '设计灵感', 'デザインニュース', 'what's new in design', 'novel products', '新奇事物'. Aggregates from Dribbble, Awwwards, Product Hunt, Behance, design influencers on Twitter, and more.
Guides enterprise data center portfolio planning and execution—multi-site capacity roadmaps, investment prioritization (build, expand, refresh, exit, colo vs owned), portfolio RAID and dependency management across DC programs, stage-gate governance, capex/opex alignment, regional and resiliency strategy, and steering-committee reporting. Use when prioritizing several DC initiatives, harmonizing site plans over 3–5 years, tracking a portfolio of hall builds and refreshes, or aligning facilities/IT/finance on DC investments—not for a single hall MEP design (data-center-design-execution-lead), host-level utilization (data-center-compute-supply-efficiency), generic software programs (technical-program-manager), or cloud IaC (infrastructure-engineer). For executing approved MW/rack delivery on schedule, use senior-data-center-capacity-delivery-manager.
Define REST APIs with @data-client/rest - resource(), RestEndpoint, CRUD (GET/POST/PUT/PATCH/DELETE), HTTP fetch, normalize, cache, urlPrefix, path-to-regexp parameters, searchParams, pagination, extend(), auth/headers, optimistic updates, polling, file download, blob, parseResponse. Use when defining or modifying network endpoints, REST resources, or the HTTP layer.
Model data with @data-client schemas (Entity, EntityMixin, Collection, Union, Query, Values, All, Invalidate, Lazy, Scalar) for atomic, consistent, referentially-equal async data via normalization, identity-based caching, and a single source of truth. Use when defining or editing pk, static schema, resource()/RestEndpoint schema, mutable lists/maps (push/unshift/assign/remove/move), polymorphic/discriminated types, memoized selectors / derived data, partial/supplementary entities, relational/nested/joined data, optimistic updates, or cache invalidation across @data-client/rest, /endpoint, /graphql, or /normalizr. Apply proactively when discussing data models, remote data shape, caching, normalization, identity, joins, polymorphism, mutable collections, or store consistency.
Design and audit data residency and regional operating models covering customer region commitments, data classification, storage, processing, backups, logs, analytics, search, AI providers, subprocessors, support/admin access, replication, failover, deletion, migrations, contracts, and proof. Use when customers or regulators require clarity about where data lives and moves.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Query Data912 market data endpoints for Argentina and USA instruments. Use when the user asks for MEP/CCL quotes, live Argentine market panels (stocks, options, cedears, notes, corporate debt, bonds), USA panels (ADRs, stocks), OHLC historical series by ticker, USA option chains, or volatility/risk metrics. Also use when the user mentions "Data912", "mep", "ccl", "cedears", "option chain", "historical bars", "OHLC", "implied volatility", "historical volatility", or "volatility percentiles" and expects API-backed market snapshots.
Designs comprehensive database schemas, including relational and NoSQL models, normalization, indexing strategies, relationship modeling, data types, constraints, and performance optimization. Covers entity-relationship diagrams, schema migrations, partitioning, and best practices for PostgreSQL, MySQL, MongoDB, and other databases. Use when designing databases, creating schemas, modeling data, optimizing queries, or when users mention "database design", "schema design", "data modeling", "ERD", "normalization", "indexing", or "database architecture".
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD), earth/environment (USGS, NOAA, EPA), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, ZINC, BindingDB), materials (Materials Project, COD), biology/genomics (Reactome, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, BioGRID, Gene Ontology, dbSNP, gnomAD, ENCODE, Human Protein Atlas, Human Cell Atlas), disease/clinical (COSMIC, Open Targets, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, World Bank, US Treasury), demographics (US Census, Eurostat, WHO). Use when looking up compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, or any public database API query.
MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries.
Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task
Delaware Personal Data Privacy Act (DPPA) compliance implementation. Covers consumer rights (access, correct, delete, portability, opt-out), controller obligations, processor requirements, sensitive data consent, universal opt-out recognition, DPIA requirements, and AG enforcement. Effective January 1, 2025, with no revenue threshold for applicability.
Deploy a Decentraland scene to a World (personal 3D space using a DCL NAME or ENS domain). Covers worldConfiguration setup, Places listing opt-out, and common deployment errors. Use when the user wants to deploy to a World, publish to a personal space, or use a DCL NAME/ENS domain. Do NOT use for Genesis City LAND deployment (see deploy-scene).
Before starting a nontrivial task, compute how much understanding the work demands and split it into "already understood" vs "learn first". Use when the user invokes /depth-check, asks "what do I need to understand before touching this?", "이거 시작하기 전에 뭘 알아야 해?", "어디까지 파악하고 들어가야 해?", or is about to start work in territory they may not know well. Maps the task's blast radius, grades the depth each area requires, and emits the short list of questions to answer before coding.
Audit existing UI code for consistency with your design system. Use this skill whenever the user wants to review, audit, check, or validate their UI implementation against design system standards. This includes checking for raw color values instead of tokens, missing states, spacing inconsistencies, accessibility gaps, dark mode support issues, or general design system compliance. Also trigger this when the user says things like 'does this look right', 'review my CSS', 'check this component', 'audit my UI code', or 'is this following the design system'.
Implementation orchestrator for guardrails projects — dispatch every plan task to a subagent under develop-tdd, merge the task branches, then run one review-conformance review per guidelines file and dispatch the fixes. Use when implementing any feature, fix, or refactor.
Write a developer onboarding document for a service, codebase, or team. Use when asked to write a developer guide, service README, onboarding doc for a new engineer, codebase orientation, or getting-started guide for a technical team. Produces a structured doc covering service overview, architecture, local setup, key patterns, testing, deployment, and who to ask for what.
Use when the user asks to generate a Substrait query plan, create a Substrait plan from natural language, convert a data query to Substrait JSON, write a Substrait DSL pipeline, translate a data request into a query plan, process a test entry from a JSONL dataset. Covers writing DSL code, calling MCP tools (compile_substrait_dsl, get_substrait_dsl_examples, load_test_entry), and self-correcting on compile errors. Trigger on: "generate substrait", "generate substrait dsl", "generate functional plan", "generate fp", "process entry", "run entry", "generate dsl".
digital life.skills — 6 tools for self-archaeology and experience distillation that use digital traces as evidence, to see your true self and reuse your own experience. Trigger words: estate settlement, social death archaeology, AI stand-in, past life, epitaph, distilled life, decision double, digital life, archaeology toolkit, digital life
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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.
