Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Alibaba Cloud Tair (Redis OSS-Compatible) Database AI Assistant. For Tair/Redis instance management, performance diagnostics, memory analysis, hotspot key detection, latency troubleshooting, parameter tuning, connection session analysis. Use when user questions involve Tair, Redis, instance IDs starting with r-, memory analysis, hotspot keys, eviction policy, big key detection, etc.
Expert knowledge for Azure Data Science Virtual Machines development including troubleshooting, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when managing DSVM images/tools, IaC deployment (Bicep/ARM), Key Vault secrets, MLflow, or GPU/Jupyter issues, and other Azure Data Science Virtual Machines related development tasks. Not for Azure Virtual Machines (use azure-virtual-machines), Azure Machine Learning (use azure-machine-learning), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).
Azure SQL Database optimization and platform-specific features. PROACTIVELY activate for: (1) Azure SQL Database optimization, (2) DTU vs vCore selection and right-sizing, (3) automatic tuning (force last good plan, create/drop index), (4) Hyperscale tier and read replicas, (5) Serverless tier and auto-pause, (6) Azure SQL performance monitoring (Query Performance Insight, Intelligent Insights), (7) elastic pools, (8) Always Encrypted with secure enclaves, (9) Managed Instance vs Azure SQL DB tradeoffs, (10) failover groups and geo-replication. Provides: tier-selection matrix, automatic-tuning enablement steps, Hyperscale architecture overview, monitoring queries, and elastic pool sizing guidance
Deploy MISP, configure threat feeds (MISP community, freetext, TAXII, CSV), and use the PyMISP API to programmatically fetch, add, and search events and IOCs, building automated collection pipelines that aggregate indicators from community and commercial sources. Use when gathering, storing, or correlating IOCs and threat intelligence, or when scripting MISP ingestion via PyMISP.
Evaluate one sklearn-compatible learner with the locked validation scheme and persist structured skore reports. The splitter and any non-default score are already on the DataOp from `build-ml-pipeline`. This skill writes the call without `splitter=`, persists the report, and records the locator. When the locked split is the training table and test table that shipped with the data, fit on the training table, then pass `splitter="prefit"` with only the test table. When `model-ml-pipeline` dispatched this turn, return the locator. A standalone turn owns the evaluate-stage close. TRIGGER when code calls `cross_val_score`, `cross_validate`, `classification_report`, `.skb.cross_validate`, or a handwritten metric print, or the user asks to score, evaluate, or run CV on one learner. Narrative reads of a persisted report belong to `audit-ml-pipeline`. HOW TO USE: before any evaluation call. Resolve G-SKORE-MODE, read the stops, and emit Pre-flight before code. Confirm symbols with `python -m skore_skills api get`.
Use when writing raw SQL with GRDB, complex joins across 4+ tables, window functions, ValueObservation for reactive queries, or dropping down from SQLiteData for performance. Direct SQLite access for iOS/macOS with type-safe queries and migrations.
Create or inspect Jinkō data tables via the jinko-sdk. Use this skill whenever the user wants to upload observed data for trial overlays or calibration objectives from CSV, SQLite, or pandas DataFrame; check data-table schema columns; inspect existing data tables; or verify metadata.public.validForFitnessFunction. Do not use this skill for output sets; use jinko-output-set for that.
Use Sorftime data to query Amazon product details and historical trends by ASIN across 14 marketplaces. Trigger for Sorftime product details, ASIN lookups, sales trends, price curves or history, BSR ranking history or trends, profit analysis, FBA fee analysis, gross margin, product trend analysis, daily or monthly sales, revenue trends, Deal promotion history, or related requests. Also trigger for Amazon product details or historical trend data by ASIN, even if “Sorftime” is not mentioned.
Multidimensional Amazon product search and filtering based on Sorftime data, covering 14 marketplaces and historical monthly snapshots. Trigger when the user mentions Sorftime product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshots, product search, monthly sales volume or revenue, finding products with ABA keywords, price-range filtering, new product discovery, multi-criteria filtering, product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, or historical snapshot. Trigger even if the user does not explicitly mention “Sorftime,” as long as they want to search, filter, compare, or explore Amazon products by category, brand, or seller.
Signals scout for Core Web Vitals (`$web_vitals`). Watches each page's p75 LCP / INP / CLS / FCP against Google's thresholds and its own history — poor-band pages, band crossings, sharp regressions — and dates each regression against deploys and flag rollouts.
Use when running commands inside a Zeabur service container. For one-off database operations such as queries, data cleanup, or migrations (e.g. mongosh, psql, mysql, redis-cli). Use when the user says “exec into container,” “run command in service,” “query database,” “delete from database,” “run mongo command,” “run SQL,” “check files in container,” “debug inside service,” or “shell into service.” Also for container-level debugging such as checking environment variables, files, processes, or connectivity. NOT for deploying databases (use zeabur-template-deploy instead)
Alibaba Cloud Tair development toolkit — 7 capabilities covering architecture selection, data structure design, instance creation & configuration, connection management, performance monitoring, error troubleshooting, and backup & recovery. Executes real cloud operations via aliyun CLI (creating instances, modifying whitelists, managing backups, restoring data). Restore operations are high-risk and will overwrite current data. Ensure the RAM account has required permissions (see references/ram-policies.md). Triggers: "tair", "create tair instance", "tair instance", "redis", "data structure", "backup", "PITR", "tair architecture", "tair connection", "tair error".
Expert knowledge for Azure AI Personalizer development, including troubleshooting, decision making, security, configuration, integrations, and coding patterns. Use when choosing single- vs. multi-slot setups, tuning policies or exploration, using apprentice mode, explainability, or local inference, and for other Azure AI Personalizer development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure Machine Learning (use azure-machine-learning), Azure AI Metrics Advisor (use azure-metrics-advisor), or Azure AI Anomaly Detector (use azure-anomaly-detector).
Use this skill for any Yandex DataLens automation task through the Python package `datalens-sdk`. Trigger on: DataLens, datalens, даталенс, chart, чарт, график, dashboard, дашборд, dataset, датасет, connection, подключение, workbook, воркбук, collection, коллекция, wizard chart, QL chart, editor chart, BI automation, автоматизация DataLens, "create a dashboard", "построй дашборд", "создай чарт", "export dataset", or "clone dashboard"; entity ids such as dataset_id, chart_id, dashboard_id, or workbook_id; and requests to create, update, inspect, import, export, copy, or diagnose DataLens objects with code. NOT for: business questions about metric values; viewing or screenshotting the DataLens web UI; embedding or iframes; raw SQL/YQL analysis that does not manage DataLens entities; raw HTTP API calls; standalone HTML pages or reports, except publishing or managing them through this SDK
Queries patent citation data from PatSnap, including citation counts and details of citing patents. Triggered when users mention patent citations, citation analysis, patent influence, citation frequency, citations of a patent family, forward citations, or ask which patents cite a given patent; also for patent citations, citation count, patent influence, citation analysis, or PatSnap. Use even if “PatSnap” is not explicitly mentioned, as long as the user wants to find how often a patent is cited or which patents cite it.
Local-first architecture decision framework for web applications. Covers when to go local-first vs. server-based vs. hybrid, sync engine selection (ElectricSQL, Zero, PowerSync, Replicache, LiveStore, Triplit), client-side storage options (IndexedDB, OPFS, SQLite WASM, PGlite), and conflict resolution strategies (LWW, CRDTs, server-wins, field-level merge). Use when deciding whether to adopt local-first architecture, choosing a sync engine, selecting client storage, or designing conflict resolution strategies.
Optimizes Magento 2 indexing for search performance and database efficiency. Use when optimizing search performance, configuring Elasticsearch, designing database indexes, or improving reindexing strategies. Masters indexer optimization, Elasticsearch configuration, and database indexing.
How to connect AI agents to MariaDB using the Model Context Protocol (MCP). Use when setting up MCP with MariaDB, connecting Claude Code, Cursor, or other AI tools to a MariaDB database, enabling natural language database queries, or building AI applications that need live database access. The MariaDB MCP Server lets agents query schemas, run read-only SQL, and perform semantic search against MariaDB.
Plan molecular dynamics post-processing for materials simulations, including RDF, MSD and diffusion, VACF/VDOS, coordination numbers, bond-angle distributions, stress-strain curves, equilibration detection, PBC unwrapping, and trajectory format choices. Use before writing MD analysis scripts or trusting trajectory-derived results.
Expert patterns for Segment Customer Data Platform, including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when: segment, analytics.js, customer data platform, cdp, tracking plan
Signals scout for PostHog MCP tool calls. Watches `$mcp_tool_call` telemetry for tools that need improvement — broad-reach failure rates, retry hammering, slow or context-bloating responses — grouped by owning product category, each with a fix suggestion.
Windows and Git Bash compatibility for ADF tooling. PROACTIVELY activate for: (1) MSYS_NO_PATHCONV usage with ADF CLI tools, (2) Git Bash path conversion when running ADF npm builds, (3) PowerShell scripts called from Git Bash for ADF, (4) Azure CLI from Git Bash on Windows for ADF deployments, (5) ARM template export issues on Windows, (6) ADF utilities npm package on Windows, (7) line-ending issues in JSON pipeline definitions. Provides: MSYS environment-variable recipes, npm-on-Windows fixes, PowerShell-from-bash patterns, and Azure CLI authentication on Git Bash.
Azure Data Factory (ADF) CI/CD, deployment, and pipeline development. Proactively activate for: (1) ADF CI/CD setup (npm validation, ARM template export), (2) ADF ARM template deployment, (3) ADF npm build validation in CI, (4) PrePostDeploymentScript for trigger and resource cleanup, (5) ADF GitHub Actions workflows, (6) ADF Azure DevOps pipelines, (7) ADF Git integration (collaboration vs. publish branch), (8) parameterizing linked services and datasets across environments, (9) ADF triggers (schedule, tumbling window, event), and (10) deployment slots and blue-green deployments for ADF. Provides complete CI/CD YAML for GitHub Actions and Azure DevOps, a PrePostDeploymentScript reference, parameterization patterns, and trigger management recipes.
Analyze the most expensive users in AI observability and explain why they cost so much. Use when the user asks about top spenders, expensive users, per-user LLM cost, user-level cost drivers, or patterns behind high AI observability spend.
How to author, edit, and adapt PostHog Signals scouts—the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on “write/edit/customize a signals scout,” “new scout for X,” “tune my scout schedule,” “make a scout that watches <event>.”
Expert knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines, or Azure-integrated storage/BI, and other Azure HDInsight related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics).
Creates, validates, formats, and modifies Infrahub schema YAML files — nodes, generics, attributes, relationships, and extensions. Also checks the Infrahub Marketplace for an existing published schema to reuse before modelling a domain from scratch. TRIGGER when: designing data models, adding schema nodes, validating schema definitions, planning schema migrations, modifying or extending an existing schema, changing an attribute or relationship on a node kind, debugging why a schema fails to load or a migration is rejected, checking the marketplace for an off-the-shelf schema, modeling file objects / attachments / uploads (PDFs, images, certificates, documents), formatting schema files, canonicalising schema key order, or running `infrahubctl schema format` (including as a CI gate). DO NOT TRIGGER when: populating or editing object data files (use infrahub-managing-objects), writing checks/generators/transforms, querying live data, planning an Infrahub version upgrade (use infrahub-planning-upgrades)
Creates, modifies and debugs Infrahub transforms that convert data into JSON, text, CSV, or device configs using Python or Jinja2 templates, with YAML-driven tests. TRIGGER when: building config generation, data export, format conversion, Jinja2 templates, artifact pipelines, writing or running tests for a transform, modifying or extending an existing transform or template, debugging why a transform renders the wrong output or an artifact fails to generate. DO NOT TRIGGER when: designing schemas, writing validation checks, creating generators, querying live data
Create, run, poll, and inspect results for Jinkō CMA-ES calibrations via the jinko-sdk: attach data tables and/or an advanced output set as fitness-function sources, set CMA-ES options and parameter priors, launch and monitor the run, and read performance/results payloads. Use whenever the user needs the SDK mechanics of building or driving a Calibration object. Do not use this skill for calibration business rules (defaults, diagnostics, deliverable rules). Do not use this skill for advanced output set / scoring design authoring — use jinko-output-set. Do not use this skill for data-table creation or validForFitnessFunction checks — use jinko-data-table. Do not use this skill for model or protocol authoring — use jinko-model / jinko-protocol. Do not use this skill for calibration-plan orchestration or iteration workflow.
Build or edit a Jinkō computational model (QSP/PK-PD) via the jinko-sdk: parameters, categorical parameters, compartments, species, ODEs, reactions, dosing events, algebraic rules, baseline checks, solving options, units, and component tags. Use this skill whenever the user wants to create a model from scratch, create an empty model, edit an existing model, add or modify components, apply input/source/output tags, configure unit checking, define model-level dosing events, combine two models and resolve their conflicts, validate diagnostics, or debug model sanity or simple_solve errors. Prefer editing existing models over recreating them. Do not use this skill for running trials; use jinko-trial for trial execution.
Create, inspect, validate, and incrementally edit Jinkō output sets via the jinko-sdk: simple output sets (measure designs) that list scalar measures derived from model outputs, and advanced output sets (scoring designs) that define constraints, scalars, and weighted objectives for scoring virtual populations. Validate scoring expressions and read diagnostics before attaching an output set elsewhere. Do not use this skill for attaching a simple or advanced output set to a trial and running it; use jinko-trial for that. Do not use this skill for data-table creation or fitness-function metadata; use jinko-data-table for that. Do not use this skill for calibration setup or CMA-ES options.
Design or edit multi-arm Jinkō protocol designs via the jinko-sdk. Use this skill whenever the user wants to compare doses, schedules, administration routes, treatment activation flags, or combinations of treatments by overriding model component values per arm. Protocol designs assign values to model-defined inputs; dosing functions, schedule parameterization, treatment activation logic, and administration-mode logic belong in the model. Use jinko-model when those functions or inputs do not exist yet. Use jinko-trial for running trials.
Create, sanity-check, run, poll, download results, and compute plot data for Jinkō in-silico trials via the jinko-sdk. Use this skill whenever the user wants to set up a trial from a computational model and simple output set, optionally attach a vpop, protocol, data table, or advanced scoring output set, launch a trial, wait for completion, inspect completed trials, download TimeSeries and Scalar results as pandas DataFrames, or get calculated plot data (scalar distributions, time-series quantile bands, survival curves, contribution/tornado analysis, per-patient scatter values), filtered or grouped by descriptors, to draw their own plots. Do not use this skill for model editing, vpop creation, protocol design authoring, data-table upload, output-set creation/editing, or configuring a stored TrialVisualization.
Create, update, inspect, sanity-check, and retrieve Jinkō TrialVisualization project items for completed or running trials. Use this skill whenever the user wants a trial visualization, trial viz, time-series plot setup, scalar result plots, scatter plots, contribution analysis, survival analysis, data overlays, or to fetch the current visualization JSON. The SDK exposes a typed TrialVisualization API: creation helpers plus a per-section subservice for every plot type.
Create, generate, inspect, or work with Jinkō virtual populations (vpops) and vpop designs via the jinko-sdk. Use this skill whenever the user wants to upload a vpop from CSV or pandas DataFrame, create a vpop generator from marginal distributions, generate a vpop from a vpop design, inspect vpop content/statistics, or edit an existing vpop design. Vpops generated or uploaded as Vpop project items are not editable; edit the vpop design instead and regenerate.
A one-stop tool for getting content from the internet—video downloads, audio extraction and transcription, subtitle downloads, comment collection, creator data, web scraping, aggregated search, cross-platform publishing, and analytics, covering 30+ platforms (Douyin/TikTok/YouTube/Bilibili/Xiaohongshu/Zhihu/official accounts/Channels/Weibo/Reddit/HackerNews/Jike/Knowledge Planet/Xueqiu/Tieba/Toutiao/Twitter, etc.). Automatically selects the best tool through a fallback chain. Use when the user says “download,” “get a video,” “grab this,” “crawl,” “read this,” “read this link,” “check this URL,” “transcribe,” “get subtitles,” “comments,” “check comments,” “grab comments,” “creator data,” “creator updates,” “UP主,” “what did so-and-so post recently,” “cover image,” “post to Douyin,” “post to Xiaohongshu,” “schedule a post,” “draft,” “account data,” “follower count,” “take a screenshot,” “webpage text,” or “what is this page about”; provides any URL, short link, BV number, Channels link, or official account article link and asks to retrieve its content; mentions a specific platform such as Douyin/TikTok/YouTube/Bilibili/Xiaohongshu/Zhihu/official accounts/Channels/Weibo and requests downloading or searching content, reading comments, or collecting data; or asks for any operation involving getting content from the internet. Prefer tools in this fallback order: search uses anysearch→opencli→agent-reach; downloads are routed by platform; scraping uses Scrapling→browser-use→web-access; for browser operations (logged-in pages/JS rendering/multi-step interactions/cross-site transfers), prioritize huashu-chrome MCP (since 2026-10-21, real logged-in state + evidence of results, replacing chrome-devtools MCP). Purely technical fact-checking or searches for tutorials and experience (without a platform name and without retrieving content) defer to laohan-sousuo (decided by Jeffrey on 2026-10-02).
Use when running a systematic review and meta-analysis, DTA or intervention. Covers PROSPERO protocol, search, screening, extraction, risk of bias (QUADAS-3, RoB 2, ROBINS-I), bivariate/HSROC or random-effects pooling, forest plots, heterogeneity and PRISMA reporting. Topic scouting is /ma-scout.
Migrates Synapse Data Factory pipelines to Fabric Data Factory, turning linked services into Fabric connections, inlining datasets into activities, converting global parameters to Variable Libraries, and mapping SynapseNotebook to TridentNotebook. Use for pipeline JSON. For Synapse Spark notebook code use synapse-migration.
Diagnoses and resolves PostHog ingestion warnings — problems recorded while ingesting events (dropped events, rejected person merges, oversized payloads, invalid data). Use when a user asks why events are missing, dropped, or undercounted, why identify/alias calls don't work or accounts stay duplicated, why person or group properties aren't updating or profiles look inflated, why recordings have gaps, heatmaps are empty, LLM token counts are missing, why cookieless events vanish, or whenever the `ingestion_warning` health check fires. Explains severity triage (error = dropped, warning = modified, info = intentional) and routes all warning types — size limits and enrichment, merges and distinct IDs, high-volume distinct IDs, `$process_person_profile`, timestamps, cookieless, heatmaps, transformations, session replay — to per-issue reference files with code-level causes and per-SDK fixes.
Develop Microsoft Fabric Spark/data engineering workflows and write code in Fabric Notebook cells with routing to resources. Provides workspace/lakehouse management, notebook code authoring (PySpark, Scala, SparkR, SQL), and Materialized Lake View (MLV) authoring (Spark SQL MLVs support incremental refresh; PySpark is full-refresh only). Routes to data engineering patterns, development workflow, or infrastructure orchestration. For existing MLV refresh or schedule management, use `mlv-operations-cli`. Triggers: "develop notebook", "Delta Lake patterns", "Spark development", "lakehouse configuration", "write notebook code", "notebookutils", "notebook cell", "PySpark notebook", "%%sql cell", "%%configure", "fabric notebook", "run notebook", "notebook deployment", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh", "review MLV for incremental refresh", "MLV refresh policy"
Analyze stocks, ETFs, and crypto using Yahoo Finance and Alpha Vantage. Get real-time quotes, fundamentals (P/E, EPS, margins, balance sheet), technical indicators (RSI, MACD, Bollinger), dividends, earnings, options chains, analyst ratings, institutional holders, sector comparisons, and screening. Use when the user asks about stock prices, company financials, market trends, portfolio analysis, or any investment research.
Time Machine. Replays every possible outcome of a trade before you risk a dollar: 10,000 simulated sessions plus every past time the setup fired. Takes the plan from trader-exit (entry, stop, target), rebuilds the rest of the day 10,000 times from the stock's own 5-minute bars over the last 60 days, and re-runs the same rule on every past session. Prints the odds of target first, stop first, or neither by the close, where the price sits at the close, the average R, the history, and one honest verdict, then hands off to /trader-budget. Use when the user says "run the time machine", "replay this trade", "what are the odds", "simulate this trade", "monte carlo", "backtest this setup", "how often does this hit the target", "is this a coin flip", or after /trader-exit writes a plan, before /trader-budget.
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.