Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Builds Moran's I spatial autocorrelation workflows in CARTO. Triggers when the user mentions spatial autocorrelation, Moran's I, spatial dependency, spatial correlation, spatial outliers, HH HL LH LL quadrants, high-high clusters, low-low clusters, spatial weight matrix, "is there clustering", "are values spatially correlated", local indicators of spatial association, LISA, spatial randomness test, or wants to determine whether a variable exhibits spatial clustering, dispersion, or randomness across a gridded dataset. Also relevant when the user needs to classify locations into cluster types (HH, HL, LH, LL) rather than just identifying hotspots and coldspots.
Guides the user through spatial enrichment workflows — triggered by requests to enrich, add demographics, estimate population around locations, compute spatial features, sociodemographic analysis, "what's around" queries, buffer/isochrone + join patterns, or trade area enrichment.
Prepare, validate, import, reconcile, and correct Support Ops daily ticket metrics from Google Sheets or exported JSON into CawPlan's cawplan_csm key-metrics domain. Use when: Support asks to onboard its ticket dashboard data, backfill or upload Support ticket counts, validate a Support metrics export, compare CawPlan metrics with the Support sheet, or generate a safe Apps Script exporter. NOT for: generic CawPlan business metrics, device/app telemetry, editing the Support dashboard UI, producing the weekly Google Doc, or inventing new CSM metric names and tags
Store key-value data globally with Cloudflare KV's edge network. Use when: caching API responses, storing configuration, managing user preferences, handling TTL expiration, or troubleshooting KV_ERROR, 429 rate limits, eventual consistency, or cacheTtl errors.
Convert a business question into a complete dashboard specification. Use when asked to design a dashboard, create a dashboard spec or brief, plan a BI report, or define what charts and metrics a dashboard should include. Produces a structured spec with metrics, dimensions, chart types, filters, and layout guidance.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Design and audit data deletion and erasure workflows covering user/account deletion, DSAR/RTBF, retention schedules, backups, logs, analytics, billing records, legal holds, fraud/security exceptions, subprocessors, exports, audit proof, customer communication, and recovery limits. Use when personal or customer data must be removed safely and provably.
Load PROACTIVELY when task involves database design, schemas, or data access. Use when user says "set up the database", "create a schema", "add a migration", "write a query", or "set up Prisma". Covers schema design and normalization, ORM setup (Prisma, Drizzle), migration workflows, connection pooling, query optimization, indexing strategies, seeding, and transaction patterns for PostgreSQL, MySQL, SQLite, and MongoDB.
Kaggle datasets, models, sources, and kagglehub workflows. PROACTIVELY activate for: (1) downloading datasets with kagglehub, (2) uploading datasets, (3) downloading or uploading Kaggle models, (4) competition_download, (5) notebook_output_download, (6) choosing Kaggle CLI vs kagglehub, (7) attaching dataset_sources, competition_sources, kernel_sources, or model_sources, (8) model artifact transfer, (9) source dependency cleanup, (10) kagglehub limitations for notebooks. Provides: dataset/model transfer patterns, source attachment guidance, tool selection, and limitation checks.
DDD4J MyBatis-Plus persistence layer conventions skill. Covers the ActiveRecord pattern (Entity must extend BaseEntity→Model<T>), @TableLogic logical deletion, automatic audit field population (createBy/createTime/updateBy/updateTime), the @Param("model") XML parameter convention, PaginationEntity pagination wrapper (15 rows/page/Oracle-style offset), the type handler system (BooleanEnum/dual JSON/ListSetArray splitting), and BaseMapper built-in methods such as getPagedList/setStatus/getCountBy*. Use this skill when users define Entity/Repository, write Mapper XML, use paginated queries, or configure type handlers in a DDD4J project. Use together with the ddd4j-core skill.
Answer prediction questions using market trading data, not opinions. Use when the user asks probability questions about geopolitics, economics, markets, industries, or any topic where real money is being traded on the outcome. Examples: 'What's the probability of WW3?', 'Will there be a recession?', 'Is AI in a bubble?', 'When will the Russia-Ukraine war end?', 'Is it a good time to buy gold?', 'Will SPY drop 5% this month?', 'Is NVDA options premium overpriced?'. The skill reads prices from prediction markets, commodities, equities, options chains, derivatives, yield curves, and currencies, then cross-validates multiple signals to produce a structured probability report.
Read Discord for financial research using the discord-cli tool (read-only). Use this skill whenever the user wants to read Discord channels, search for messages in trading servers, view guild/channel info, monitor crypto or market discussion groups, or gather financial sentiment from Discord. Triggers include: "check my Discord", "search Discord for", "read Discord messages", "what's happening in the trading Discord", "show Discord channels", "list my servers", "Discord sentiment on BTC", "what are people saying in Discord about AAPL", "monitor crypto Discord", "export Discord messages", any mention of Discord in context of reading financial news, market research, or trading community discussions. This skill is READ-ONLY — it does NOT support sending messages, reacting, or any write operations.
Use when the user needs to write, review, or migrate SQL queries in ADVPL/TLPP using BeginSQL/EndSQL blocks and macro expressions (%table%, %xfilial%, %notDel%, %exp%, %Order%) instead of raw TCQuery string concatenation -- covering INNER/LEFT JOIN patterns, aggregations (SUM/COUNT/GROUP BY/HAVING), subqueries, column type declarations, DML via TCSqlExec, and SQL-injection prevention. Also triggers on Portuguese phrasing like "consulta SQL", "query embutida", "migrar TCQuery para BeginSQL", "escrever SELECT com filial e notDel", or "montar JOIN em ADVPL".
Design and generate accessible, cohesive data visualizations, SVG charts, metric cards, and dashboard tiles with theme-adaptive styling and verified viewports. Use when creating SVG charts, rendering metrics plots, designing dashboard visuals, or visualizing performance trends — even if the user does not explicitly say "fable-dataviz" (e.g. "make a chart of this data", "plot these benchmarks", "create an SVG graph", "visualize these metrics"). Do NOT use for non-visual text-only data summaries or generic code edits.
Use MinerU to convert complex PDF documents into LLM-friendly Markdown/JSON. Applies to: (1) PDF to Markdown/JSON, (2) extracting text, tables, formulas, and images from PDFs, (3) parsing academic papers, technical documents, and business reports, (4) preparing document data for RAG applications, (5) batch processing PDFs. Trigger keywords: "PDF parsing", "PDF to Markdown", "extract PDF tables/formulas", "MinerU", "parse PDF", etc. Not for: reading/filling in/signing/splitting and merging PDFs (use the host pdf tools), parsing non-PDF formats such as Word/PPT, and cases where you only need to read a few pages (just read them directly, no conversion needed).
Build visually stunning geospatial demo webapps with MapLibre globe, DuckDB-WASM, and a warm dark HUD aesthetic. Use when building satellite imagery retrieval demos, embedding visualization tools, geospatial search UIs, or any map-centric data exploration frontend. Triggers on requests to scaffold a new geo demo, build a map-based UI, or create a retrieval/search visualization over satellite data.
Apply the Fama-French three-factor model to decompose asset returns into market, size, and value factors. Use this skill when the user needs to explain cross-sectional return differences, evaluate fund performance beyond CAPM alpha, assess small-cap or value tilts in a portfolio, or when they ask 'why do small caps earn more', 'is value premium real', or 'what factors drive returns'.
Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.
Compile one or more OpenViking knowledge bases or document collections into topic-organized, evidence-grounded high-level knowledge, including cross-source findings, trends, changes, drivers, comparisons, implications, and uncertainties. Use with ov compile when the user asks to distill or synthesize a knowledge base, compare multiple collections, or derive higher-order insights such as changes across financial reports; do not use for document-by-document summaries.
Provides comprehensive guidance for Lime ECharts including chart creation, configuration, data visualization, and interactive charts. Use when the user asks about Lime ECharts, needs to create charts, visualize data, or work with ECharts features.
Build evidence-traceable market research reports and assumption-driven market sizing or forecast scenarios. Use for market definition, industry and customer evidence, competitive landscapes, TAM/SAM/SOM reconciliation, forecast sensitivity, and auditable report scaffolds.
This skill should be used when the user asks to ingest, clean, validate, transform, version, monitor, or serve ML data and features. PROACTIVELY activate for: (1) data ingestion, preprocessing, feature engineering, leakage prevention, train/serving skew, (2) Spark, Dask, Polars, pandas, Ray Data, streaming pipelines, (3) Great Expectations, TFDV, Deequ, data quality and validation, (4) DVC, lakehouse tables, dataset versioning, lineage, reproducibility, (5) Feast, Tecton, Hopsworks feature stores, point-in-time joins, online/offline features. Provides: scalable, reproducible, leakage-safe ML data pipeline design.
Provides comprehensive guidance for generating MyBatis-Plus code including Entity, Mapper, Service, ServiceImpl, Controller, DTO, VO, BO and other related objects from database tables. Use ONLY when the user explicitly mentions MyBatis-Plus, mybatis-plus-generator, or wants to generate code using MyBatis-Plus framework. This skill automatically generates standard CRUD methods and custom methods based on user requirements for MyBatis-Plus projects. Supports MVC and DDD architectures, Java and Kotlin languages. Do NOT trigger for generic code generation, JPA/Hibernate, or other ORM frameworks.
No-code data model extensibility for NetBox. Create custom object types with typed fields, relationships to core models and other custom objects, full REST API, and standard NetBox features (tags, change logging, search, bookmarks, journaling).
How to write NetBox custom scripts for automation, data validation, and bulk operations. Use when building scripts that run inside NetBox — covers the Script class, form variables, ORM access, logging, transactions, scheduling, and common patterns.
When the user wants to forecast using deep learning, LSTMs, transformers, or neural networks. Also use when the user mentions "neural network forecasting," "LSTM," "GRU," "transformer forecasting," "attention mechanisms," "seq2seq," "temporal convolution," "deep learning time series," or complex non-linear patterns. For traditional forecasting, see demand-forecasting. For general ML, see ml-supply-chain.
Kaggle notebook/kernel lifecycle operations. PROACTIVELY activate for: (1) creating Kaggle notebooks or kernels, (2) `kaggle kernels init`, (3) pushing or running notebooks with `kaggle kernels push`, (4) pulling notebooks with metadata, (5) checking kernel status, files, logs, or outputs, (6) downloading output artifacts with file patterns, (7) deleting kernels, (8) Python API kernel operations, (9) auth for notebook CLI workflows. Provides: safe CLI/Python lifecycle commands, run monitoring, output retrieval, destructive-action guardrails.
Review privacy, data retention, deletion, export, consent, telemetry, analytics, backups, account closure, children/minors, store disclosures, and data lifecycle for SaaS, apps, games, desktop utilities, developer tools, marketplaces, and support systems. Use when product decisions touch user data, tracking, storage, privacy claims, compliance risk, or trust.
Generate real instrumentation code from the tracking plan and instrumentation guide. Produces typed SDK wrapper functions, identity management, and integration guidance. Outputs files in a tracking/ directory. Use when the user wants to generate or regenerate tracking code, implement the delta plan, turn the instrumentation guide into working code, 'implement tracking,' 'generate code,' 'create tracking module,' or 'build analytics SDK wrapper.'
Update the tracking plan when a feature ships, changes, or is removed. Assesses whether new events are needed, extends existing events with properties where possible, and produces a versioned mini-delta with changelog entry. Updates .telemetry/tracking-plan.yaml, delta.md, and changelog.md. Use when the user ships a new feature, modifies existing functionality, wants to keep tracking coherent as the product evolves, "feature shipped," "new feature tracking," "update tracking for [feature]," "what tracking does this feature need," or "instrument new feature."
Optimize your Webshare proxy pool for a specific target website. Tests all proxies against the target, identifies blocked IPs by country/ASN/subnet using data from the webshare CLI, and replaces them via the Webshare MCP. Use when proxies are failing on a site, when setting up a new scraping target, or when you want to tune your proxy pool. Invoke with a target URL as argument.
Parse resumes and CVs (PDF, Word, images) into structured JSON profiles using SoMark for accurate document parsing. Extracts name, contact info, work experience, education, skills, and certifications. Ideal for HR workflows, candidate review, and talent intelligence. Requires SoMark API Key (SOMARK_API_KEY).
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.