Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Tabular Editor documentation search and configuration file guidance (.tmuo, Preferences.json, UiPreferences.json, Layouts.json). Automatically invoke when the user asks about "TE docs", "Tabular Editor features", "TE3 preferences", ".tmuo files", "workspace database settings", "DAX editor settings", "per-model TE3 configuration", or needs to search Tabular Editor documentation for how-to guidance.
Answer questions using the Tenzir documentation. Use whenever the user asks about TQL syntax, pipeline operators, functions, data parsing or transformation, normalization, OCSF mapping, enrichment, lookup tables, contexts, packages, nodes, platform setup, deployment, configuration, integrations with tools like Splunk, Kafka, S3, Elasticsearch, or any other Tenzir feature. Also use when the user asks how to collect, route, filter, aggregate, or export security data with Tenzir, or needs help writing or debugging TQL pipelines, even if they don't mention 'Tenzir' explicitly but are clearly working in a Tenzir context. Also use for questions about Tenzir the company or product, release notes and changelog, blog posts, and solution use cases.
The Pelosi Tracker. Watches the stock trades members of Congress must disclose under the STOCK Act: reads the House Clerk's public Periodic Transaction Reports (PTRs) as soon as they are posted, alerts once per new filing, and turns each PDF into clean rows (who, owner, stock, ticker, buy or sell, options strike and expiry, trade date, amount range, how many days it took to disclose). Flags trades in stocks you hold. Senate reports are covered by a documented manual route. Use when the user says "what is Pelosi buying", "track Nancy Pelosi", "Congress trades", "politician stock trades", "did any congressman buy X", "alert me when Pelosi files", or "run the Pelosi skill".
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. Provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The industry standard for Large Language Models (LLMs) and foundation models in science.
Explains PostHog billing usage and spend from the customer's visible Billing MCP tools. Use when the user asks why usage or spend is high, which product or project is driving usage, what a usage type means, how to reduce usage, what changed over time, why they got a usage change alert, or whether a spike/drop alert was real or noisy. Also use before product-specific analytics skills when the user names a billable PostHog product metric such as events, recordings, feature flag requests, exceptions, survey responses, synced rows, logs, AI events, AI credits, or Inbox credits. Starts from Billing usage/spend tools, then routes to customer-visible product MCP surfaces for deeper investigation.
Neural web search and content extraction using x402-protected APIs. Better than WebSearch for deep research and WebFetch for blocked sites. USE FOR: - Deep web research and investigation - Finding similar pages to a reference URL - Extracting clean text from web pages - Scraping sites that block standard fetchers - Getting direct answers to factual questions - Research requiring multiple sources TRIGGERS: - "research", "investigate", "deep dive", "find sources" - "similar to", "pages like", "more like this" - "scrape", "extract content from", "get the text from" - "blocked site", "can't access", "paywall" - "what is", "explain", "answer this" Prefer Exa for semantic/neural search, Firecrawl for direct scraping. IMPORTANT: Never guess endpoint paths. All paths follow the pattern https://enrichx402.com/api/{provider}/{action}. Use exact URLs from the Quick Reference table below or call x402.discover_api_endpoints first.
This skill should be used when users need to scrape content from websites, extract text from web pages, crawl and follow links, or download documentation from online sources. It features concurrent URL processing, automatic deduplication, content filtering, domain restrictions, and proper directory hierarchy based on URL structure. Use for documentation gathering, content extraction, web archival, or research data collection.
Train and deploy a TinyML model for the XIAO ESP32S3 (Sense) using the Edge Impulse REST API only — no edge-impulse-cli needed (its serialport dep fails to build on modern Node/Windows). Covers: dataset upload, impulse creation (audio MFCC / vision transfer-learning), training jobs, downloading the Arduino library, and the on-device fixes required to actually run it on the ESP32-S3. Use this skill whenever the user wants to train/retrain a model ("재훈련", "edge impulse", "TinyML 훈련", "모델 배포"), upload a dataset to Edge Impulse, or gets EI Arduino-library build/runtime errors (mel filterbank, objs.a, tensor arena, EI_MAX_OVERFLOW_BUFFER_COUNT).
Load PROACTIVELY when task involves AI, LLM, or machine learning features. Use when user says "add AI chat", "implement streaming responses", "build a RAG pipeline", "add embeddings", or "integrate OpenAI". Covers chat interfaces, streaming with Vercel AI SDK, retrieval-augmented generation, vector search, embeddings pipelines, tool/function calling, and provider abstraction for OpenAI, Anthropic, and local models.
Use the AKShare library to fetch and analyze Chinese A-share market data. AKShare is a free open-source Python financial data interface library that requires no registration or token. Supports real-time stock quotes, historical K-line data, financial statements, valuation metrics, capital flows, technical indicators, scheduled tasks, and more. Use this skill when the user needs to fetch A-share prices, analyze a stock's fundamentals, query financial data, calculate technical indicators, or analyze capital flows.
Use when managing Alibaba Cloud AIRec (Airec) via OpenAPI/SDK, including the user needs recommendation-engine resource operations in Alibaba Cloud, including list/create/update flows, status inspection, and troubleshooting AIRec configuration or runtime issues.
Drives TÜBİTAK Açık Bilim Politikası (Open Science Policy) compliance and deposition into Aperta — TÜBİTAK ULAKBİM's national open archive at aperta.ulakbim.gov.tr — encoding its principles (İlke 1: deposit the accepted manuscript in Aperta on acceptance; İlke 2: open access at most 6 months after publication for STEM, 12 for SSH; İlke 6: document data that must stay closed, e.g. for KVKK; İlke 9: report compliance in the final report) and scaffolding the five-question TÜBİTAK Veri Yönetim Planı / VYP (data management plan) uploaded with ARDEB applications. Use when depositing to Aperta, complying with the TÜBİTAK açık bilim policy, preparing a TÜBİTAK data management plan (VYP), reporting open-access compliance in a final report, or documenting a justified data embargo. For Zenodo/Dryad/OSF and international DMPs prefer alterlab-open-science; for the KVKK lawful-basis/anonymisation plan prefer alterlab-kvkk-dmp. Part of the AlterLab Academic Skills suite.
Produces KVKK-compliant (Law 6698, as amended by Law 7499 — published in the Official Gazette 12 Mar 2024, KVKK provisions effective 1 Jun 2024) data management plans for Turkish research, encoding the açık rıza (explicit consent) default basis, the Art. 28(1)(b) anonymization exemption as the primary compliance lever, the Art. 6 special-category regime for health/genetic/biometric data, Art. 7 deletion/destruction/anonymization at purpose-end, the Art. 13 thirty-day data-subject response window, Art. 9 cross-border adequacy-decision rules for cloud/overseas data, and Art. 16 VERBIS pre-processing registration, with a KVKK-vs-GDPR crosswalk. Use when the user needs a KVKK data management plan, to anonymize a research dataset under Turkish law, a VERBIS check, or to fix EU DMP boilerplate for Turkey; for pure GDPR/HIPAA use alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Retrieves Türkiye higher-education program and admission statistics from YÖK Atlas (yokatlas.yok.gov.tr) — quotas (kontenjan), placements (yerleşen), minimum admission scores (taban puan), success ranks (başarı sırası), and per-title academic-staff counts — via the keyless yokatlas-py wrapper (≥0.6.0; 0.7.0 current, MIT) over the JSON API at /api/tercih-kilavuz/ (search plus university, program-group and province lookups), with SearchFilters for puan türü (SAY/SÖZ/EA/DİL/TYT), university type (DEVLET/VAKIF), province, and success-rank ranges. Use when the request mentions YÖK Atlas, program statistics, kontenjan/quota data, taban puan/minimum admission score, başarı sırası/success rank, placement counts, or program staff counts for institutional research, benchmarking, or advising. For an academic's CV/profile/affiliation prefer alterlab-yok-akademik; for theses prefer alterlab-yok-tez. Legacy PHP pages are dead since April 2026. Part of the AlterLab Academic Skills suite.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.
Work with ArcGIS Utility Networks for modeling and analyzing connected infrastructure including network tracing, associations visualization, and topology validation. Use for electric, gas, water, and telecom network analysis.
Turn one minimoths crawl session into a dated prices table appended to a product note in the Obsidian vault. Use when asked to update a product note with today's prices, add a prices section, or find the best price for a product after running a search.
byted-emr-skills provides skills for managing Volcengine EMR (Volcengine E-MapReduce, "EMR" for short, is an enterprise-grade big data analytics system of the open source Hadoop ecosystem and fully compatible with open source), including managing EMR on ECS clusters, EMR on VKE clusters, EMR serverless queues, compute groups, job templates/instances, logs and monitoring, and offers EMR Agent intelligent diagnosis and knowledge Q&A capabilities. When users mention needs such as "EMR on ECS cluster", "EMR on VKE cluster", "Serverless queue", "Serverless job", "SparkSQL/PrestoSQL/Ray/PySpark/SparkJar job", "job logs", "job monitoring" or "job diagnosis", this skill should be preferred.
When the user wants to measure, track, or reduce carbon emissions in the supply chain. Also use when the user mentions "carbon accounting," "GHG emissions," "Scope 1/2/3 emissions," "carbon footprint calculation," "emissions reporting," "carbon reduction," "climate impact," "decarbonization," or "emissions baseline." For circular economy approaches, see circular-economy. For sustainable sourcing, see sustainable-sourcing.
Guides the user through building composite score workflows when they ask about composite scores, indexes, multi-variable scores, ranking areas, site scoring, market potential, resilience indexes, risk indexes, weighted scores, PCA, or supervised/unsupervised scoring.
Builds, schedules, and operates analytics DAGs in CARTO Workflows — the no-code/low-code orchestration layer over the data warehouse. Triggers when the user wants to author a workflow, run/edit one, schedule a DAG, or copy a workflow across profiles or orgs.
Generate a working geospatial app powered by CARTO and deck.gl — basemap, layers (vector / H3 / quadbin / raster), widgets, filters, legend, inputs, optional chat-with-map agent, and the right auth strategy (public token, OAuth, SSO, M2M, or CARTO-hosted)
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
Drive CodeBurn, a free open-source local-first CLI/TUI/web/menubar tool that reads the session files already on disk from 40 AI coding tools (Claude Code, Codex, Cursor, Gemini CLI, Grok, OpenCode, and more) and breaks down token usage and dollar cost by task, model, tool, and project. Use when the user wants to see where their AI coding spend went, find and fix token waste in a Claude Code / agent setup, cap a session's budget before it runs away, compare which model is actually worth its price, check whether AI spend shipped or was reverted, or wire live usage/savings data into an agent over MCP. Triggers on: "codeburn", "npx codeburn", "AI token usage", "AI coding cost", "where did my Claude spend go", "codeburn optimize", "codeburn guard", "codeburn compare models", "codeburn yield", "token waste in CLAUDE.md", "AI spend dashboard".
Guides creation of BKN (Business Knowledge Network) definition files following v2.0.1 spec. Covers network, object_type, relation_type, action_type, concept_group. Use when creating knowledge networks, BKN files, object types, relation types, action types, concept groups, or when user asks to model business knowledge in BKN format. When kweaver-core is also loaded, use it to run kweaver CLI (auth, bkn push) after files exist.
Data analysis pro skill providing three core functions: data analysis, data interpretation, and data visualization. **Use Cases**: (1) Data Analysis - Statistics, filtering, aggregation, calculation (e.g., "Calculate total sales", "Filter data greater than 100") (2) Data Interpretation - Trend analysis, pattern discovery, report generation (e.g., "Analyze sales trends", "Interpret data changes") (3) Data Visualization - Chart generation, data display (e.g., "Draw a bar chart", "Generate a pie chart") **Trigger Keywords**: analyze data, statistics, calculate, interpret trends, generate chart, visualize, plot **Prerequisites**: Set environment variable CHARTGEN_API_KEY (obtain from chartgen.ai)
Use when designing BK-CI database tables, DDL scripts, indexes, table partitioning, or data archiving strategies. Prefer this when the user mentions table schemas, field design, index optimization, SQL script naming, sharding, or archive migration.
Build and search knowledge bases from documents using the Denser Retriever API. No-code RAG pipeline - upload PDFs, DOCX, PPTX, and other files, then search them with semantic search through natural conversation. Use this skill for: knowledge base creation, document upload, semantic search, document retrieval, PDF search, file indexing, RAG (retrieval-augmented generation), vector search, AI search, question answering over documents, text search API, document Q&A, knowledge management, and enterprise search. Trigger on: 'knowledge base', 'semantic search', 'document search', 'RAG', 'search my files', 'upload documents', 'PDF search', 'denser retriever', 'index documents', 'search knowledge base', 'document retrieval', 'vector search', or any request to search, query, or build a searchable collection of files.
Guide for creating, editing, and using parameter sets (paramsets) in IBM watsonx.data integration. Covers parameter types, value sets, PROJDEF, local parameters, runtime parameters, flow parameters, #param# and ${param} reference syntax for both DataStage (batch) and StreamSets (streaming) flows, and parameterizing connection properties with external parameter sets. Use when the user wants to create, list, update, or delete parameter sets; manage value sets; attach parameter sets to flows or connections; parameterize connection properties; or reference parameters inside a flow or connection.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs
Design, launch, analyze, and stop GrowthBook A/B tests, and search or record durable Learnings across experiments. Use for experiments, split tests, variations, hypotheses, sample size, guardrail metrics, SRM, chance to win, lift, declaring a winner, "what have we learned", "have we tested this before", or "record this learning". For bandits, identify them and direct the user to GrowthBook UI. For feature flags and rollouts, use feature-flags. For product analytics or metric catalog work, use analytics. For API key configuration, use gb-setup.
Explore PostHog MCP intent clusters — agent goals grouped by semantic similarity, with each cluster's tool distribution and error rates, plus the tool-centric pivot (capture rate per intent, discovery rate against the advertised catalog, description fit, tool overlaps). Use when the user asks "what are agents trying to do with the MCP?", "group the intents", "which goals fail most?", "what does each cluster route to?", "when agents have this intent do they find my tool?", "which tools get mixed up?", wants to recompute the clustering, or pastes an MCP analytics intent-clustering URL.
Investigate individual PostHog MCP sessions — the sequence of tool calls a single agent made in one run, what it was trying to do, and where it went wrong. Use when the user asks "what did this MCP session do?", "show me the tool calls for session X", "what was the agent's goal?", "which sessions had errors?", "who is connecting to my MCP?", or pastes an MCP analytics sessions URL.
Build a starting-point taxonomy for an MCP tool — what users were trying to accomplish before they reached the tool — and publish it as a PostHog notebook. Reconstructs each session's goal from its opening tool calls, then clusters those goals into named categories with size, share, and facet mix. Use when the user asks "why do people use this tool?", "what are users actually trying to do?", "what problem brings people here?", "where do these sessions start?", "segment usage of <tool> by goal", or wants a Clio-style taxonomy of MCP usage. Complements exploring-mcp-intent-clusters, which groups what agents did per call rather than why the session began. The agent running this skill writes the goal labels itself, reading the corpus query output session by session — the bundled scripts cover the mechanical facets but measurably lose the goal's altitude, so do not delegate that field to them.
Investigate the quality of PostHog MCP tool calls — error rates, latency, reach, and which tools are failing or slow. Use when the user asks "which MCP tool has the highest error rate?", "what's the slowest tool?", "which tools fail most often?", "how reliable is tool X?", wants a tool-quality matrix, or pastes an MCP analytics tool-quality / dashboard URL and asks what it shows.
Guides agents through pulling a Replay Vision scanner's observations, reading the findings, and acting on them — summarizing patterns across sessions, drilling into individual recordings, and turning real, corroborated issues into PostHog tasks, insights, or an investigating-replay hand-off.\nTRIGGER when: user wants to pull/read/triage Replay Vision observations, asks \"what has my scanner found\", wants to act on or summarize scanner findings, turn observations into tasks/work, or points at a /replay-vision/<scanner-id> URL.\nDO NOT TRIGGER when: creating or sizing a scanner (use creating-replay-vision-scanners), running a one-off scan you don't then analyse, or authoring a signals scout.
Free, keyless financial data via CDP. Stock price snapshots and historical OHLCV (Yahoo Finance) plus income / balance-sheet / cash-flow statements (SEC EDGAR XBRL). No API key, no account. Returns structured JSON or pretty-printed tables. Requires browser-harness-js on PATH and a running Chromium-based browser with remote debugging.
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.