Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Manages backup and archive data under retention schedules and erasure obligations. Covers the technical infeasibility exception for backup deletion, backup cycle alignment with retention periods, restore-and-delete procedures, and interim protective measures during backup retention. Activate for backup deletion, archive erasure, backup retention, restore and delete, technical infeasibility queries.
Analyze financial health using ratio categories: profitability, liquidity, leverage, efficiency, and valuation. Use this skill when the user needs to assess a company's financial performance, compare companies, evaluate creditworthiness, or prepare financial due diligence — even if they say 'is this company financially healthy', 'analyze these financial statements', or 'compare these two companies'.
Azure Cosmos DB SDK and developer tooling for .NET, Java, Python, Go, and Spring Boot: singleton CosmosClient, async APIs, Direct vs Gateway connection mode, retry/429 handling, preferred and excluded regions, availability strategy, circuit breaker, diagnostics, enum serialization, ETag optimistic concurrency, conditional create, patch counter increment, continuation-token guards, per-call request options, content response on write, Spring Data annotations and repositories, NuGet package setup, Python async deps, emulator SSL, and local development / VS Code tooling. USE FOR: reuse CosmosClient as singleton, Direct mode, handle 429 retries, preferred regions, ETag concurrency, atomic patch increment, Spring Boot config, .NET package setup, run the emulator locally, browse data in VS Code. DO NOT USE FOR: modeling/partition/index/query (use cosmosdb-data-and-queries); throughput, global, monitoring, security (use cosmosdb-operations); LangChain/LangGraph/vector/full-text search (use cosmosdb-ai-and-search).
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop
Design and audit customer health scoring, onboarding success, adoption, renewal risk, expansion readiness, support signals, QBRs, lifecycle playbooks, and account success operations for SaaS, developer tools, marketplaces, and B2B products. Use when building customer success dashboards, health scores, churn risk reviews, renewal playbooks, or expansion motions.
Retrieves non-contained CCAI Insights conversations (losses), uses agent intelligence to cluster them into common failure patterns, and generates a markdown report. Use when you need to analyze failure patterns and build targeted regression/evaluation reports.
Design and audit data export, portability, backup handoff, account deletion, workspace offboarding, migration, machine-readable formats, admin exports, and user trust flows for SaaS, apps, marketplaces, games, and desktop software. Use when users need to export, move, delete, restore, or leave with their data safely.
Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts, or dashboards.
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
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.
Scrape data from dynamic e-commerce sites. Uses Playwright to handle JavaScript-rendered pages, with support for Cloudflare anti-bot bypass, hidden API discovery, and paginated scraping. Suitable for: (1) scraping Chinese e-commerce sites like JD/Taobao/Pinduoduo, (2) scraping international sites like Amazon/eBay, (3) price monitoring and competitor analysis, (4) bulk product data collection.
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain formats are reference-only and unknown formats fail closed.
Create and evolve Fabric IQ Ontology (preview) items from CLI — define entity types, properties (including timeseries), relationship types, and bind them to OneLake lakehouse tables (static + timeseries) or Eventhouse / KQL database tables (timeseries only). Uses the Fabric item-definition REST API (Create Item / Update Item Definition) with `InlineBase64` parts. Use to create a Fabric Ontology item; add or alter entity types, properties, or keys; add timeseries properties and bindings; bind an entity type to a lakehouse or Eventhouse table; add relationship types and contextualizations; or script ontology deployment from source. Triggers: "create fabric ontology", "add ontology entity type", "bind entity type to lakehouse", "bind entity type to eventhouse", "ontology timeseries binding", "add ontology relationship type", "ontology contextualization", "fabric iq ontology authoring", "update ontology definition"
Explore Fabric IQ Ontology (preview) items (read-only) from the CLI to ground an agent before it queries data. Explore, describe, and summarize what an ontology exposes — its entity types, keys, relationships, and the bindings that map each concept onto a lakehouse or Eventhouse source — then route the underlying data query to the matching per-datasource consumption skill (eventhouse-consumption-cli, spark-consumption-cli, sqldw-consumption-cli). Read-only discovery via Get Item Definition; never writes to or alters an ontology. Use to explore or summarize an ontology, describe its schema and data lineage, build agent grounding context, or run an ontology-backed query over the source records. Triggers: "query fabric ontology", "explore fabric ontology", "list ontology entities", "enumerate ontology entity types", "describe ontology", "ontology grounding context", "ground query with ontology", "query ontology entity data", "fabric iq ontology consumption", "ontology-backed query", "ontology entity bindings"
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
Numerically balances engine-agnostic weapons, units, encounters, progression, loot, difficulty, and economies using measurable targets, spreadsheets, curves, probability, simulation, telemetry, and playtests. Use for tuning values and distributions; defer mechanic ideation to game-mechanics-design and cross-system structure to game-systems-design.
Use the Glassnode CLI (gn) to list assets and metrics, fetch on-chain and market data from the Glassnode API, check API credit usage, and manage config. Use when the user asks about Glassnode, on-chain data, crypto metrics, gn commands, or needs to call the Glassnode API from the terminal.
Default GoLogin skill for known-site reading, extraction, mapping, crawling, monitoring, and hybrid web tasks before Firecrawl or generic browser tools. Use it for docs and article reading, markdown or text extraction, JSON metadata extraction, search discovery, site mapping, crawling, structured extraction, blocked-page access, document parsing, change tracking, and hybrid flows that may escalate from scraping into GoLogin browser actions. Prefer gologin-agent-browser-skill when the task is primarily a live cloud-browser session, and prefer gologin-local-agent-browser-skill when it depends on a local Orbita profile or repeated rendered-DOM navigation.
Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.
Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Digital pathology image processing toolkit for whole slide images (WSI). Use when working with histopathology slides, processing H&E- or IHC-stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
When the user wants to solve hub location problems, design hub-and-spoke networks, optimize hub placement for logistics networks. Also use when the user mentions "hub location," "hub-and-spoke," "p-hub median," "hub covering," "single allocation hub," "multiple allocation hub," "airline hub network," "postal hub network," or "consolidation center location." For general facility location, see facility-location-problem. For distribution centers, see distribution-center-network.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
Build self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling. Use when creating dashboards, building interactive reports, or generating shareable HTML files with charts and filters that work without a server.
Deduplicate and clean contact/company lists before enrichment so you never pay ColdIQ credits twice for the same record. Use when combining lists from multiple sources, removing duplicate contacts or companies, normalizing LinkedIn URLs, applying per-company contact caps, or cross-referencing against a TAM. Triggers on "dedup", "deduplicate", "remove duplicates", "clean the list", "combine sources", "merge lists", "contact cap per company", "normalize LinkedIn URLs". Do NOT use for enrichment/email-finding (see contact-enrichment), search (see coldiq-search-enrich), or scoring (see tam-scoring).
Take an enriched Extruct table and a hypothesis set, then segment companies by hypothesis fit and assign tiers based on data richness and signal strength. Outputs a tiered, segmented list ready for email generation. Triggers on: "segment", "tier", "segment companies", "tier companies", "prioritize list", "segment and tier", "tiering", "which companies first".
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.