Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Generate professional daily A-share morning/evening reports, including index quotes, market sentiment, candlestick charts, industry/concept sector rankings, individual stock gainers and losers, thematic news tracking, and comprehensive analysis, output as Markdown + PNG charts + PDF. Data source is the Eastmoney public API. Use when asked to create daily A-share reports or market analysis, or when the user mentions A股/大盘/股市/行情/晚报/早报.
Use when analyzing, selecting, validating, or communicating models for insurance, actuarial, financial-risk, or other consequential uncertain outcomes. Covers regression, generalized linear models, frequency-severity, panel and longitudinal data, survival, time series, credibility, reserving, tail risk, calibration, and model governance. Do not use for generic software forecasting, ordinary SaaS financial models, or credentialed actuarial, investment, legal, or regulatory advice without the relevant specialist review.
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
Read-only MongoDB CLI. Use when exploring databases, collections, schemas, or indexes, querying documents (find, get by id, count, sample, distinct, aggregate), checking database or collection statistics, or managing MongoDB connections and credentials — including identity-provider login (MONGODB-OIDC, workforce/workload identity federation, SSO).
Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.
Apple on-device machine learning framework reference. Core AI (.aimodel) / Core ML / Create ML / Vision / Natural Language / Speech / Media Intelligence / Music Understanding. AIModel, InferenceFunction, MLModel, MLModelConfiguration, MLMultiArray, MLTensor, MLComputeUnits, MLImageClassifier, MLTextClassifier, MLDataTable, VNCoreMLRequest, VNImageRequestHandler, VNRecognizeTextRequest, RecognizeTextRequest, NLTagger, NLEmbedding, NLLanguageRecognizer, SpeechAnalyzer, SpeechTranscriber, SFSpeechRecognizer, SFSpeechAudioBufferRecognitionRequest, SFTranscription, FaceGroupAnalyzer, MusicUnderstandingSession.
Automate controlled local ArcGIS Pro and ArcPy workflows through arcgis-mcp-bridge: inspect .aprx projects and file geodatabases, run geoprocessing, projection, raster, network, spatial-statistics, editing, symbology, and layout export with path and mutation guards. Use when the user explicitly names ArcGIS Pro, ArcPy, .aprx, .gdb, Esri geoprocessing, muend/arcgis-mcp-bridge, its health_check, PathGuard, or confirmation gates, or sketch-to-GIS extraction. Do not trigger for ArcGIS Online or Enterprise administration, QGIS or PyQGIS, generic open-source GIS, or live GUI control of an already-open ArcGIS Pro session.
Analyze an influencer's audience demographics to determine whether their followers match your target customer, with a clear pass/fail verdict. This skill should be used when evaluating audience fit, checking influencer demographics, analyzing audience data, reviewing an audience breakdown, assessing demographic alignment, vetting an influencer's audience, determining if a creator's followers match your target demo, reviewing a platform export or stats screenshot, pasting influencer stats, grading audience quality, deciding whether an influencer's audience is a good fit, checking if this creator is worth it, running an audience report, comparing creator audiences, or evaluating audience overlap with target demo. For overall creator vetting beyond demographics, see creator-vetting-scorecard. For finding new creators, see creator-discovery.
Makes an app's database connections reliable against the local Azure SQL Database container (Private Preview) and, unchanged, against Azure SQL Database in the cloud: connection pooling plus retry/transient-fault handling. Use when the user mentions "connection pooling", "retry logic", "transient fault", "retry on transient error", "EnableRetryOnFailure", "connection resiliency", "reliable connections", "pool size", "Max Pool Size", or says "the connection keeps dropping", "connections time out under load", "add backoff", or "make the DB layer resilient". This is the Azure SQL engine (EngineEdition 5), not the mssql/server SQL Server image. Reach for this whenever hardening a data-access layer that talks to SQL Server or Azure SQL.
Stands up an instant no-code REST + GraphQL API over the local Azure SQL Database container using Microsoft Data API Builder (DAB). Use when a user wants to "expose my table as an API", "add a REST API over the database", "generate a GraphQL API", "put an API in front of SQL", "CRUD API without writing code", or "dab init / dab-config.json". Also the way to serve a built-in MCP endpoint FROM the database via DAB (an API surface DAB provides, not a separate SQL MCP server). Prefer this over hand-writing a controller/ORM API when the user just needs REST or GraphQL over existing tables. Triggers include "Data API Builder", "dab start", "instant API over Azure SQL", "expose entities as REST/GraphQL". Reach for this even when the user only says "give me an API for this database".
Builds a serverless API and event-driven handlers over the local Azure SQL Database container using Azure Functions with the Azure SQL bindings. Use when a user wants "a serverless API over SQL", "Azure Functions with a database", "HTTP CRUD with SQL input/output bindings", "run code when a row changes", "react to inserts/updates/deletes", "event-driven on Azure SQL", or "SQL trigger function". The Azure SQL trigger binding (backed by Change Tracking) is the local event-driven mechanism; Change Event Streaming (CES) is cloud-only and cannot run against the local container. Triggers include "func start with SQL", "SqlTrigger", "SqlInput/SqlOutput binding", "local.settings.json SqlConnectionString". Reach for this when building serverless endpoints or change-driven logic on the local Azure SQL engine.
Use when implementing data analysis pipelines, statistical tests, or bioinformatics workflows in code (Python/R), particularly for genomics, transcriptomics, proteomics, or other -omics data. NOT for non-omics Python implementation (use senior-developer), pure statistical method selection without bioinformatics context (use statistician), or biological-validity questions about an existing analysis (use biologist-commentator).
End-to-end discipline for turning any large data source (audio libraries, email takeouts, document corpora, chat exports, API dumps) into brain pages at scale. The lifecycle spine: SCHEMA → ACCESS → TRIAL → EVALUATE → IMPROVE → CODIFY → TEST → SKILLIFY → BULK → MONITOR. State is tracked in a durable JSON manifest (see MANIFEST-PATTERN.md) so any crash, session boundary, or subagent fan-out resumes from ground truth instead of memory.
Use when writing C# / .NET application code (also VB.NET, F#) that connects to a data source through a CData driver — after the connection and SQL have been validated with the cdata-cli discovery skill. Covers mapping the language to the CData ADO.NET Data Provider edition, adding the provider from NuGet (CData.<Source>), activating its license, and connecting via the ADO.NET classes (<Source>Connection / <Source>Command). This is the .NET build-phase companion to cdata-cli; invoke it whenever the target application runs on .NET.
Crawl an entire website or section and extract content from every page. Use when you need content from many pages under a common URL prefix: "crawl the whole site", "get all docs pages", "scrape every blog post", "download the full docs for RAG", "extract all pages under /api". Async BFS — starts a job and polls for results. Step 4 of the crw workflow ladder.
Move Django performance-sensitive Python loops into database-side computation with annotations, aggregates, filtered aggregates, F expressions, functions, Subquery, OuterRef, Exists, Window expressions, db_default, and GeneratedField. Use when Django code counts, sums, filters, flags, ranks, or derives values in Python after fetching rows.
Model, migrate, refresh, and verify PostgreSQL materialized views in Django for expensive report or dashboard queries that can tolerate stale data. Use when ORM/index/query rewrites are not enough, the result is read often, freshness can be bounded, and a Django app needs unmanaged models, RunSQL migrations, refresh commands, or concurrent refresh.
Improve Django and Django REST Framework pagination for large querysets using bounded page sizes, stable ordering, indexed ordering, offset limits, keyset pagination, DRF CursorPagination, and count-query tradeoffs. Use when list pages, admin views, APIs, exports, or infinite scroll become slow, inconsistent, or memory-heavy as result sets grow.
Read and compare Django/PostgreSQL query execution plans using QuerySet.explain(), EXPLAIN, EXPLAIN ANALYZE, scan types, joins, estimates, actual timing, buffers, and row counts. Use when one SQL query dominates Django latency or when an index, ORM rewrite, pagination change, or materialized view needs proof from a query plan.
Find the Django ORM code and request path responsible for slow SQL by using APM traces, slow query logs, Django Debug Toolbar, query logging, and local reproduction. Use when a Django database performance issue is suspected but the exact queryset, view, serializer, template, or job causing it is not yet proven.
Analyze financial data from uploaded Excel/PDF files and generate interactive reports with sparkline trend charts. Supports output to PDF, DOCX, Markdown, and HTML. Use when: (1) User uploads Excel/CSV/PDF with financial data, (2) User asks for financial analysis or company report, (3) User wants visual reports from financial statements, (4) User mentions stock ticker + financial analysis, (5) User shares a Feishu sheet/doc link with financial data.
Comprehensive financial analysis skill for stock research, portfolio review, and opportunity discovery. Use this skill when the user wants to: analyze stocks, review a portfolio, scan for new stock ideas, get buy/sell ratings, compute support/resistance levels, check insider or Congress trades, assess sentiment from Reddit/StockTwits/news, pull analyst ratings, or generate entry/exit price targets. Triggers: stock analysis, portfolio review, stock scanner, financial analysis, buy/sell rating, price target, technical analysis, sentiment analysis, insider trading, Congress trades, stock ideas, swing trade, entry levels, exit levels, support resistance, watchlist, deep dive, stock report.
Build production search and NLP pipelines with Haystack. Pipeline DAG composition, document stores, retrievers, PromptBuilder (Jinja2), generators, evaluation, Hayhooks deployment. Use when building search pipelines or comparing NLP application frameworks. Do not use this skill for unrelated requests; route to the nearest named specialist.
Connects to Oxylabs remote headless browsers over the Chrome DevTools Protocol (CDP) with Playwright or Puppeteer. Built-in anti-detection, residential proxies, geo-targeting, persistent sessions and profiles, session recording and live VNC inspection for debugging. Use instead of WebFetch or a local browser whenever a site renders with JavaScript, blocks bots (DataDome, Cloudflare, Akamai), needs a real browser session, screenshots or PDFs. Covers connection, retries, error recovery and safe scraping of protected targets without any human help.
Huawei Cloud MRS cluster fault diagnosis skill. Diagnoses service faults, instance faults, and host faults through progressive root cause localization: quick log scan first, host troubleshooting when host issues are found, detailed investigation when no conclusion is reached. Diagnosis is driven by the built-in LakeWatch API client (lakewatch mode) or the MRS Manager API client (manager mode) and the per-layer knowledge base under fault_layer/ and scenarios/ (lakewatch) or fault_layer_manager/ and scenarios_manager/ (manager). The API mode is auto-detected by check_api_mode.py. No commands outside the knowledge base are fabricated. Applicable to MRS fault diagnosis and root cause localization scenarios where a service name or node name is provided. Trigger words: "故障诊断", "故障定位", "fault diagnosis", "fault diagnose", "MRS故障", "服务故障", "实例故障", "主机故障", "集群排查", "集群诊断", "启动失败", "停止异常", "KrbServer故障", "DBService故障", "fault troubleshooting"
Composable transformations of Python+NumPy programs. Differentiate, vectorize, JIT-compile to GPU/TPU. Built for high-performance machine learning research and complex scientific simulations. Use for automatic differentiation, GPU/TPU acceleration, higher-order derivatives, physics-informed machine learning, differentiable simulations, and automatic vectorization.
Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress. Do NOT use for resetting offsets or managing consumer groups.
Audit Kafka security configuration across the codebase and live cluster using the Lenses MCP server. Checks authentication (SASL), encryption (SSL/TLS), authorisation (ACLs), secrets management and environment tier mismatches. Use when user says "audit Kafka security", "check security config", "is my cluster secure" or asks about authentication, encryption or credentials. Do NOT use for configuring certificates, creating SASL users or setting up ACLs.
Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.
Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.
Build reusable conversion models — funnel/step conversion rates, drop-off, and time-to-convert — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute a conversion rate, funnel, step completion, drop-off, activation-funnel, signup-to-paid, or any "what % of users who did A went on to do B (within N days)" metric. Covers the funnel model (ordered steps, the conversion-window time-box, strict vs any-order), the person-vs-group aggregation unit, overall vs step-to-step conversion (two different numbers), breakdown attribution, and when a saved funnel insight beats a warehouse view. On PostHog, model funnels in HogQL with windowFunnel; in dbt, stage the event stream and compute an fct_conversion mart with tests. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics; pairs with query-funnel for interactive analysis.
Connects to Oxylabs remote headless browsers via Chrome DevTools Protocol (CDP) using Playwright or Puppeteer. Provides anti-detection, CAPTCHA handling, residential proxies, and geo-targeting built in. Use when browser automation needs remote execution, stealth capabilities, rendered pages, screenshots, PDFs, or complex JavaScript interaction.
The ONLY supported path for read-only Microsoft Fabric Power BI semantic model (formerly "Power BI dataset") query interactions. Execute DAX queries via the MCP server ExecuteQuery tool to: (1) discover semantic model metadata (tables, columns, measures, relationships, hierarchies, etc.) and their properties, (2) retrieve data from a semantic model. Triggers: "DAX query", "semantic model metadata", "list semantic model tables", "run EVALUATE", "get measure expression".
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.