Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Provides domain knowledge and guidance for the Flare Time Series Oracle (FTSO)—block-latency feeds, Scaling anchor feeds, feed IDs, onchain and offchain consumption, fee calculation, delegation, and smart contract integration. Use when working with FTSO, price feeds, oracle data, feed consumption, volatility incentives, or Flare Developer Hub FTSO guides and starter repos.
When the user wants to build, audit, or standardize UTM parameters for Google Ads campaigns — generating UTM URLs, setting up naming conventions, using dynamic value insertion, choosing between auto-tagging and manual UTMs, or ensuring GA4 channel grouping works correctly. Triggers on 'UTM', 'UTM parameters', 'UTM generator', 'UTM builder', 'tracking URL', 'campaign tracking', 'auto-tagging', 'GCLID', 'UTM naming convention', 'GA4 source medium', 'utm_source utm_medium utm_campaign', 'build UTM links', or 'Google Ads tracking links'. For conversion action setup in Google Ads see google-ads-conversion-tracking.
Converge the raw answers of real fieldwork —clusters with their noise and their outliers, jobs to be done in three dimensions with two verbatims each, the customer profile, opportunities scored against the business outcome— and write the Insight Brief as dated research of the loaded product node. Manually triggered, fifth station of the discovery pipeline: the first one that runs after real fieldwork.
The first agent-native Meta Ads CLI with local SQLite history and compound queries the live API cannot answer. Trigger phrases: `find creative fatigue on Meta`, `check Facebook ad performance`, `which Meta ads should I retire`, `Meta audience overlap`, `stuck in learning`, `use meta-ads`, `run meta-ads`.
Translate scientific questions into clear data inputs, outputs, and baseline workflows. Use when Codex needs to help researchers clarify what data goes in, what target or result should come out, how samples/labels/features are defined, and how to solve the resulting data problem through visualization, preprocessing, baseline design, training, evaluation, and reporting.
Product-usage reference for the official ScraperAPI command-line tool (`sapi`, distributed as `scraperapi-cli`). Use this skill whenever the user wants to scrape, run async jobs, fetch structured data, manage crawls, check account credits, or drive DataPipeline projects from a terminal or shell script — anywhere a one-liner is more convenient than writing SDK code. Trigger on: "scrape this URL from the terminal", "use sapi to fetch X", "ScraperAPI CLI", "ScraperAPI from bash", "sapi scrape", "sapi cost", "sapi jobs", "sapi structured amazon", "pipe ScraperAPI into jq", "shell one-liner to scrape Y", "scrape from a Makefile / cron / CI", "check my ScraperAPI credits from the command line", "submit 10000 URLs as a batch from a file". Covers install, auth resolution order, every top-level command (`scrape`, `cost`, `jobs`, `structured`, `crawler`, `pipeline`, `account`, `config`, `init`), JSON / piping behaviour, pre-flight cost checks, and common shell recipes. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Product-usage reference for ScraperAPI's Crawler — crawl an entire site or section by following links automatically. Consult when the user needs to extract data from many pages of a site without knowing the URLs upfront. Use when user asks: "crawl an entire website with ScraperAPI", "scrape all pages on a domain", "follow links and scrape each page", "how do I use the ScraperAPI crawler API", "scrape a site map", "extract data from every product page on a site", "ScraperAPI crawler job API". Covers job creation, URL regex patterns, depth vs budget, per-page scraping parameters, status polling, webhooks, scheduling, and credit costs. Also invoke when the user is building a site-wide scraper and asks which ScraperAPI product to use. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Genome-ASSEMBLY discovery, QC, and replicon mapping for any organism (bacteria, archaea, fungi, and beyond) using NCBI Datasets. Resolves an organism name or taxid to assemblies, picks the reference/representative or best-quality assembly, pulls assembly QC metrics (total length, contig/scaffold N50, contig count, GC%, assembly level, RefSeq category), enumerates chromosomes and plasmids via per-replicon sequence reports, and compares candidate assemblies on quality. Use for "what genomes are available for [organism]", "assembly stats / N50 / GC content for [GCF_/GCA_ accession]", "how many plasmids does [strain] have", "compare assemblies for [species]", "find the reference genome for [taxon]", "is this assembly Complete Genome or just contigs". NOT for gene-level orthology/synteny (use tooluniverse-comparative-genomics), plant gene structure (use tooluniverse-plant-genomics), de novo assembly from raw reads (no tool exists), or taxonomy-only name/lineage lookups.
Use when starting any conversation that touches Oracle Database or PL/SQL - it establishes how to pick the right pythia skill BEFORE any response or action, including clarifying questions. Fires on plain requests in any language, no slash command needed - "build/add/fix/explain/review/deploy X", "lam/them/sua/tao/giai thich/kiem tra/trien khai X" (Vietnamese, with or without diacritics). Also use whenever unsure which pythia skill applies, or on catching yourself answering an Oracle question with none open.
A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
Alibaba Cloud PolarDB-X Distributed Database AI Assistant. Use for PolarDB-X cluster management, topology inspection, performance diagnostics, SQL optimization, data distribution analysis, elastic scaling diagnostics, connection/session analysis, security audit, backup/restore, parameter tuning, and other O&M operations. Triggers: "PolarDB-X", "distributed database", "pxc-", "DN/CN nodes", "data sharding", "PolarDB-X diagnostics", "PolarDB-X performance", "PolarDB-X slow SQL", "YaoChi Agent", "PolarDB-X topology", "PolarDB-X backup", "PolarDB-X security audit", "PolarDB-X scaling"
Diagnoses ClickHouse table design - partitioning, ORDER BY and primary key, column count, Nullable usage, identifier length and materialized view structure. Use for schema anti-patterns, too many partitions, oversized partitions, bad sort keys and materialized view design problems.
Forecast cash flow under Amazon's DD+7 (Delivery-Date-Based Reserve) payout policy. Models when funds clear, sizes the working-capital buffer the seller needs, and stress-tests scenarios for slow shipping or refund spikes. Use when a user asks about DD+7, Delivery-Date-Based Reserve, payout delays, working capital, cash flow under the new reserve policy, or how much cash they need to keep on hand. Trigger phrases: \"DD+7\", \"payout reserve\", \"delivery date reserve\", \"working capital\", \"cash flow\", \"Amazon holds my money\". Works with zero tools. the user provides sales velocity, fulfillment mix, and seasonality.
Use when authoring a data model document — the persistence/domain model of stored data: typed entities, relationships + cardinality, keys/constraints, indexes, normalization, the storage choice, and lifecycle. Guides the producer through the METHOD, not the outline: deriving entities from the feature-spec's nouns + access patterns, detecting the paradigm and modeling in it (relational, document/NoSQL access-pattern-first, graph, wide-column, key-value), making integrity rules explicit (keys, constraints, cardinality + referential rule), justifying each index by an access pattern, and amending an approved model as a versioned, migration-planned delta — so an engineer can build + query the schema. Composes with a data-model template tool + a deep-research capability. Assumes the upstream feature-spec (+ architecture-doc) — never a blank page. Not the API wire contract (the api-spec references these entities downstream, one-directional), not the implementation/DDL, and not reviewing a finished data model.
Brings Azure Monitor, Application Insights, and Log Analytics telemetry into Fabric as Eventhouse external delta tables and correlates it with business data. Use to onboard observability data, judge whether latency or availability affected revenue, or build a Real-Time dashboard and Operations Agent over it.
Create or update a Coral source spec YAML for a custom HTTP API or local dataset. Use when authoring a standalone source for `coral source add --file`, or when adapting that spec into a Coral repo source under `sources/core` or `sources/community`.
Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.
Performs discounted cash flow (DCF) valuation analysis to estimate intrinsic value per share. Triggers when user asks for fair value, intrinsic value, DCF, valuation, "what is X worth", price target, undervalued/overvalued analysis, or wants to compare current price to fundamental value.
Use when exposure, outcome, covariate or eligibility definitions and cutoffs need a citable basis before the protocol. Reads the data dictionary first, then maps each variable to a guideline or published definition and the database columns in a citation-backed table.
Manages Fabric deployment pipelines for ALM promotion across dev, test, and prod stages, including stage creation, workspace assignment, selective forward or backward deploys, operation polling, stage role assignments, and the pipeline and workspace permissions each action requires. For Git sync use git-integration-operations-cli.
Draw **animated graphics / interactive charts / data dashboards** inside Feishu Docs — Miaobi BOX is the only carrier in Feishu Docs that can actually run CSS/JS (iframe sandbox). It can draw: the full ECharts suite (line/bar/pie/radar/scatter/heatmap/Sankey/funnel/gauge/candlestick/boxplot/parallel/sunburst/treemap/force-directed graph/timeline/Gantt), real maps with lat-long flying lines, echarts-gl 3D (map3D/3D bar/3D scatter/3D surface), real Three.js 3D scenes, word clouds, liquid fill, pure CSS animation, Canvas particles, SVG vector animation, scrolling KPI dashboard. When the user asks to "draw in Feishu Docs / make animations / animated graphics / interactive charts / data dashboard / Dashboard / line chart / bar chart / map / flying line chart / 3D chart / relationship graph / process animation / ECharts / visualization", or wants to build an **interactive document mini-app that can call AI / read and write Base tables / persist state / get user identity**, or mentions "Miaobi BOX / HTML widget / make the graphics in Feishu Docs move / embed a web page into Feishu Docs / window.magic", **you must use this skill**. Note: to be "animated" you can only use Miaobi BOX; the SVG nodes of the board (feishu-cli-board) are rasterized into static images by the server and will not move.
Computational provenance audit verifying every number, table, and figure in a manuscript derives from code, not manual entry. Triggers on: "check provenance", "verify reproducibility", "audit my pipeline", "are my numbers from code", "provenance audit". Companion to manuscript-review (prose audit).
Create, edit, and run plain-text MATLAB live scripts (.m files) with rich text formatting, LaTeX equations, section breaks, and inline figures. Use when generating tutorials, analysis notebooks, reports, documentation, or educational content, when modifying existing live scripts, or when converting existing binary .mlx files to .m for version control. Requires R2025a+.
Use when the user wants to operate or validate web pages with the locally logged-in Chrome (including testing their own site, E2E, screenshots), read logged-in admin backends, scrape data that has no API, fill out forms, or troubleshoot OpenCLI doctor, session, or adapter failures, or explicitly mentions opencli, browser automation, or tab stealing. For public text, use HTTP first; route topic research to agent-reach; route SEO and keyword decisions to rankup; route backlink selection, submission, and Semrush/Similarweb report interpretation to backlink; this Skill only handles browser driving, sessions, and data retrieval mechanisms.
Implements retrieval-augmented generation pipelines. Use when building document retrieval systems, choosing chunking strategies, selecting embedding models, configuring vector stores, implementing hybrid search, or evaluating RAG quality. Use for embedding strategy, vector stores, retrieval pipelines, chunking, hybrid search, re-ranking, multi-query retrieval, parent document retrieval, contextual compression, MMR diversity selection, reciprocal rank fusion, and evaluation. For KB architecture selection and governance, use the knowledge-base-manager skill. For knowledge graphs, use the knowledge-graph-builder skill.
Product-usage reference for ScraperAPI's DataPipeline — managed, scheduled scraping projects that run automatically and deliver results to a webhook or dashboard download. Consult when the user needs recurring scraping, has a large list of URLs/ASINs/queries to process, or wants to avoid building and maintaining their own scraping infrastructure. Use when user asks: "schedule recurring scraping with ScraperAPI", "ScraperAPI DataPipeline", "how do I run a scraping project on a schedule", "scrape 10000 ASINs automatically", "ScraperAPI managed scraping project", "set up a ScraperAPI pipeline", "deliver scraping results to a webhook automatically". Covers project types, input methods, scheduling, output delivery, the DataPipeline API, job management, and credit costs. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Use this skill whenever the user wants to check, track, or be alerted about product prices on Amazon, Walmart, or via Google Shopping. Trigger on: "monitor the price of this Amazon product", "did the price drop on [Walmart URL]?", "track these ASINs", "compare today's prices to last week", "alert me if [product] goes below $X", "what's the current price of [product]?", "check my price watchlist", "scrape the price of [URL]". Accepts ASINs, Amazon/Walmart product URLs, or free-text product queries for Google Shopping. Reads an optional baseline JSON file to detect changes, fetches live prices via ScraperAPI's structured endpoints, and reports increases, decreases, restocks, and out-of-stock transitions in a structured change report. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Build and implement web scrapers using ScraperAPI. Use this skill whenever the user asks to build, write, create, or implement a scraper, or wants runnable code that extracts data from a website. Trigger on: "build me a scraper for [website]", "write a scraper that fetches product pages from [ecommerce site]", "I need to scrape [data] from [website]", "create a script that extracts [fields] from [URL]", "help me scrape [website] — I need [fields]", "write code to scrape [website]", "make a script that scrapes [website]", "implement a scraper for [URL]". Guides architectural decisions (structured endpoint vs. raw HTML, JS rendering, proxy tier, sync vs. async batch), then generates a complete runnable Python or Node.js script with retry logic, error handling, pagination, and credit estimation. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Diagnose broken Splunk searches, reports, alerts, and dashboards from supplied evidence. Use when an existing search or saved object is empty, missing, wrong, inconsistent, invisible, not dispatching, not triggering, or not rendering and the user needs the first unsupported functional stage, the smallest next evidence, aligned read-only validation, and an exact authoring, governance, field, workflow, or performance handoff. Do not use for SPL, object authoring, implementation, optimization, configuration, mutation, or remediation.
Build the Excel toolkit that runs a selection between a published ToR and the award: procurement mode (master workbook plus one scorer workbook per panel member) and appraisal mode (generic multi-criteria decision analysis), off one weighted scoring engine. Use for panel scoring set-up, scoring grids, evaluation workbooks, or any weighted multi-criteria appraisal. Distinct from tor-procurement (writes and grades the ToR), procurement-offer-review (analyses one incoming offer), donor-proposal-scoring (proposals IPPF sends out rather than receives), and accreditation-desk-review (MAs against IPPF standards).
Backend work on a Taruvi app: provisioning datatables, Cerbos policies, roles, users, buckets, secrets, analytics queries, or raw SQL via the Taruvi MCP server; and authoring Python function bodies that run in the Taruvi function runtime (`def main(params, user_data, sdk_client)`) for multi-resource cascades, event/cron handlers, public webhooks, and external API calls. Triggers: "create a datatable", "Frictionless schema", "Cerbos policy", "serverless function", "scheduled job", "analytics query", "sdk_client", `manage_function`, `execute_raw_sql`. Skip for Refine frontend work — use `taruvi-refine-providers` instead.
Vue 3 components for the Unovis data visualization framework, @unovis/vue 1.7.0. Use when writing, debugging, or updating code that imports @unovis/vue or renders Vis* components such as VisXYContainer, VisSingleContainer, VisArea, VisLine, VisStackedBar, VisCrosshair, or VisTooltip. Covers the component catalog, props, events, tooltips, colors, patterns, SSR, and the 1.7.0 API changes.
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.