Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use when building a DB-backed job queue / task scheduler on SQLAlchemy 2.x whose readiness is dependency-driven, whose jobs are long-running and stateful, and whose only durable substrate is the relational DB — single box, embeddable, no broker, multi-dialect (SQLite / PostgreSQL / MySQL). Covers the generic jobs/job_deps model, the ready-set query, the per-dialect atomic lease (FOR UPDATE SKIP LOCKED on PostgreSQL + MySQL 8 vs BEGIN IMMEDIATE on SQLite, with dialect detection), crash-resume via heartbeat + lease expiry + stale-lease reclaim, the hung-but-alive per-dispatch timeout, weighted fair-share over group_key, the scan→rank→lease→dispatch→persist→reclaim tick loop, polling vs LISTEN/NOTIFY wake, and the at-least-once idempotency contract. Keywords: job queue, task scheduler, ready-set, SKIP LOCKED, lease, crash-resume, multi-dialect, SQLAlchemy, background jobs. Sync-first. Not the SQLAlchemy data layer or locking primitive (sqlalchemy), not migrations (alembic).
Search for trending content on Toutiao: enter keywords to find the latest Toutiao posts (articles/images and videos), sort by reads or time, set a time range, view results in a terminal table, export to CSV, and generate interactive HTML reports. Use when users want to search popular Toutiao content, track Toutiao trends, or analyze viral articles. If the API returns insufficient credits (code=3210), automatically open Alipay to top up and continue the search.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Build, update, serve, or fork the user's personal zframes market dashboard. Use when the user says "/zframes", "build me a dashboard", "set up my terminal", "make me a market terminal", "add X to my dashboard", "start/open/serve my dashboard", "fork this dashboard <url>", "run this shared zframes link", or wants a personalized live market dashboard (crypto + stocks). If they give a zframes explorer link (a .../dashboard/<id> URL), fork it onto their machine. If a dashboard already exists and the user just wants to start it, serve it — don't rebuild from scratch. Writes a validated dashboard.json and serves it live with the CLI — the agent never writes React.
Author Fabric Activator rules and Reflex items through Fabric REST API and `az rest`. Invoke for write intents: create or delete items; add or update rule definitions; configure thresholds, filters, Teams/email notifications, Fabric item actions, and Eventhouse/Eventstream/Real-Time Hub/DTB/Ontology/Power BI sources. Pure GET/explain prompts belong to `activator-consumption-cli`. Clarification for missing sources, thresholds, recipients, and action targets happens inside this skill. After another data skill finds a timely operational signal such as a spike, failure, anomaly, SLA risk, or capacity constraint, proactively ask whether the user wants an alert for future occurrences. Triggers: "create an alert", "create an activator", "create a reflex", "create an activator item", "create an alert item", "notify me when", "let me know when", "take action when", "send me an email when", "send a teams message when", "run a pipeline when", "update an alert", "delete an alert", "activator rule".
Google API integration for blog performance: PageSpeed Insights, CrUX Core Web Vitals with 25-week history, Search Console performance, URL Inspection, Indexing API, GA4 organic traffic, NLP entity analysis for E-E-A-T, YouTube video search for embedding, and Google Ads Keyword Planner. Progressive feature availability based on credential tier (API key, OAuth/service account, GA4, Ads). Shares config with claude-seo at ~/.config/claude-seo/google-api.json. Use when user says "google data", "page speed", "core web vitals", "search console", "indexation", "GA4", "keyword research", "nlp entities", "blog performance", "youtube search", "google api setup".
Use when the user wants to integrate a Kafka client into an existing application or scaffold a Java Kafka client project (Maven or Gradle based) for Confluent Cloud, local Docker, or WarpStream. Covers the Apache Kafka Java clients (KafkaProducer/KafkaConsumer/KafkaShareConsumer) with Avro, JSON Schema, or Protobuf serializers. Also use when the user wants to optimize Java Kafka client configuration for WarpStream. Do NOT trigger for Kafka Streams apps (use kafka-streams-programming), Flink, connectors, or Kafka Python client (use developing-kafka-python-client).
Use when changing anything other systems or stored data already depend on — editing a .proto file, writing a database migration, adding or dropping a column, renaming a field, changing a response shape, changing an enum, or altering a queue message format. Triggers include "will this break clients", "is this backward compatible", "buf breaking", "ALTER TABLE", "migration", "deprecate this endpoint", and any change to a published API version.
When the user wants to forecast sales revenue, build a commit/upside/best-case forecast, calculate weighted pipeline, predict whether they'll hit their number, or run a forecast call. Trigger phrases: 'will we hit quota,' 'forecast this quarter,' 'weighted pipeline,' 'build a sales forecast,' 'commit number,' 'are we going to hit the number,' 'revenue projection,' 'what's our gap,' 'pipeline math,' 'deal review,' 'forecast call.' For pipeline data that feeds the forecast, see pipeline-review. For comp plans tied to quota, see sales-comp.
Use this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital
Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.
Use when optimizing conversion rates, designing A/B tests, or improving landing pages. Covers A/B testing methodology, landing page optimization, form design, statistical significance, funnel analysis, and CRO prioritization frameworks.
Designing and reviewing the semantic layer of a Materialize SQL code base as a canonical ontology — a shared raw database, a shared core database, and one database per use case. Use this skill when a user asks how to structure or organize Materialize schemas and databases, wants to define canonical semantic objects (entities, events, measurements, relationship objects), needs identity resolution or temporal semantics for public objects, is deciding what belongs in a shared layer versus a consumer-specific one, is building or validating a relationship registry (core.public.relationships, reference edges, cardinality, optionality), or mentions ontology, semantic layer, semantic model, semantic objects, context graph, raw/core/use-case boundaries, grain, or enforceable layer dependencies. Also trigger when reviewing an existing Materialize code base for leaked private schemas, duplicated concepts, or undocumented public objects.
Use the official Parallel CLI for explicitly selected Parallel web search, extraction, research, enrichment, FindAll, or monitoring. Choose a bounded workflow, retain job IDs, and verify source evidence. Do not activate for every web lookup or move generic reminders into a paid Parallel monitor.
Core Power BI data modeling, source connectivity, and platform fundamentals. PROACTIVELY activate for: (1) Power BI data modeling and star-schema design, (2) relationships (active/inactive, bidirectional, USERELATIONSHIP), (3) data-source selection (DirectQuery vs Import vs Direct Lake vs composite), (4) incremental refresh setup, (5) gateway configuration (on-prem and VNet gateways), (6) streaming datasets and push-data scenarios, (7) Dataflow Gen2 basics, (8) Power BI common gotchas and pitfalls (bidirectional filtering, AutoExist, blank-row), (9) workspace identity and OAuth2 / service-principal auth, (10) semantic model architecture review. Provides: star-schema templates, mode-selection matrix, incremental refresh recipe, gateway setup steps, and a common-gotchas reference.
Redis + Redisson caching and distributed locking skill. Covers cache key naming conventions, TTL setting principles, RedisTemplate serialization choices (JSON vs JDK vs String), the three Redisson distributed lock modes (watchDog / tryLock with leaseTime / without leaseTime decisions), cache penetration (bloom filter / null value caching) / breakdown (mutex) / avalanche (random TTL) protection, and Caffeine vs Redis selection decisions. Corrects LLM mistakes: not setting a TTL, mixing StringRedisTemplate and RedisTemplate, Redisson locks without try-finally unlock, and the watchdog not taking effect when a leaseTime is specified.
Handle Salesforce data operations including data migration, sandbox seeding, bulk data loading, data export, and data cleanup. Use when asked about data migration, sandbox seeding, bulk data loading, CSV import/export, or moving data between orgs. Activate on mentions of "data migration", "sandbox seed", "bulk load", "CSV import", "data export", "upsert", or "data plan".
Establishes how to reach ClickHouse (MCP tool or clickhouse-client), the cluster name for clusterAllReplicas('{cluster}', ...) query packs, the default 24-hour time window, and the shared report header. Use first, before any other altinity-expert-clickhouse skill.
This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.
Build backend integrations that sync data between B2C Commerce and external systems like ERPs, OMS, WMS, or CRMs using SCAPI Admin APIs. Use this skill whenever the user needs to pull or push orders, products, inventory, or customer data programmatically from a backend service, set up server-to-server authentication with Account Manager client credentials and admin OAuth scopes, implement bulk inventory imports with NDJSON, or call any Commerce API from a script or pipeline (not a storefront). Also use when building nightly data exports, warehouse sync jobs, or customer data integrations — even if they just say "pull orders into our ERP" or "sync inventory from the warehouse".
Generate dark-mode-compatible inline SVG data visualization charts for blog posts. Supports horizontal bar, grouped bar, donut, line, lollipop, area, and radar charts with automatic platform detection (HTML vs JSX/MDX). Enforces chart type diversity, accessible markup (role=img, aria-label), source attribution, and transparent backgrounds. Use whenever the user mentions data visualization, charts, graphs, comparison tables that need to be visualized, or wants to embed inline SVG visualizations in a blog post, even if not invoking blog-write. Use when user says "blog chart", "generate chart", "data visualization", "svg chart", "blog graph", "visualize data", or when the blog-write workflow identifies chart-worthy data points (3+ comparable metrics, trends, before/after data).
Build source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monitoring view with clear metrics, filters, source definitions, and QA.
Upgrade marketing, content, SEO/AEO/GEO, and revenue skills so changes are judged by platform analytics instead of vibes. Use when applying closed-loop learning to X, YouTube, SEO, AEO/GEO, outbound, paid creative, or revenue workflows.
Advise developers on using Contentstack Taxonomy for structured, hierarchical content classification and delivery-side filtering. Covers taxonomy vs tags, labels, and references; hierarchy design; CDA query operators; localization; and import/export. Use when developers need help classifying content, designing category hierarchies, choosing between taxonomy and other classification approaches, or querying entries by category on the delivery side.
Build Convex schemas, queries, mutations, actions, and client usage with strict validators and indexes. Use for data modeling, function authoring, argument/return validation, and performance guidance. Use proactively when work touches convex/schema.ts, functions, or api.* references. Examples: - user: "Design tables for multi-tenant app" → defineSchema/defineTable with indexes - user: "Write a mutation" → args/returns validators + auth checks - user: "Optimize query" → add index and withIndex range expression - user: "Use useQuery" → show generated hook usage
Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
Starting from data phenomena, generate actionable product decision recommendations and output them as a visualized HTML report. Trigger when the user says "analyze this dataset", "why did this metric drop", "do a retention analysis for me", "what's causing user churn", "see where the funnel drops off", or "give me a data analysis report". Also applies when: the user uploads a CSV/Excel data file and asks for analytical insights; the user provides SQL query results and asks for interpretation; the user asks for metric decomposition, attribution analysis, cohort comparison, or A/B test analysis; the user mentions data analysis keywords such as "metric tree", "funnel", "retention", "conversion rate", "DAU decline", "cohort", or "attribution". Not applicable to: pure BI dashboard building (use xlsx), pure SQL writing (write directly), or pure data cleaning (write a script directly).
Run a product-analytics analysis that produces a decision, not a dashboard — start from the question, pick actionable over vanity metrics, separate signal from noise, segment (averages lie), pair quant with qual, and end in an insight→impact→action story. Catches the defaults Claude misses: vanity metrics, correlation-as-causation, reacting to small samples/short windows, survivorship/selection bias, unsegmented averages. Trigger when asked to analyze product data/metrics, plan what to measure, build a funnel/cohort/segment/retention analysis, read analytics, or turn data into a recommendation.
Use when designing RisingWave schemas, writing materialized views, configuring sources or sinks, optimizing streaming SQL performance, troubleshooting slow backfills, setting up CDC, designing time-windowed aggregations, or reviewing a RisingWave streaming pipeline
Pull real SEO metrics for any domain using Apify scrapers for Semrush and Ahrefs data. Gets domain authority, organic traffic estimates, keyword rankings, backlink profiles, top performing pages, and auto-discovers competitors from keyword overlap. No Semrush/Ahrefs subscription needed — uses Apify actors that scrape public pages.
Guide to analyze multiple documents (PDF, DOCX) against user-defined columns and produce a structured Excel output with citations. Use when the user wants to: (1) Extract specific information from multiple documents into a table, (2) Compare clauses or provisions across contracts, (3) Create a document review matrix with source citations. Triggers on: 'tabular review', 'document matrix', 'extract from documents', 'compare across documents', 'review multiple contracts'.
AI-driven comprehensive health analysis system that integrates multi-dimensional health data, identifies abnormal patterns, predicts health risks, and provides personalized recommendations. Supports intelligent Q&A and AI health report generation.
Comprehensive survey and instrument design assistant supporting questionnaire construction, Likert scale design, question types (open/closed/matrix), response bias mitigation, sampling strategies (probability/non-probability), pilot testing, instrument validation (Cronbach's alpha, factor analysis), online survey tools (Qualtrics, REDCap, Google Forms), interview protocol development, focus group facilitation, mixed-mode surveys, and cultural adaptation of instruments. Use when designing a survey or questionnaire, building Likert scales, planning a sampling strategy, pilot testing, validating an instrument (Cronbach's alpha, factor analysis), developing an interview protocol, improving response rates, or working in Qualtrics or REDCap. For analyzing interview/focus-group data use alterlab-qualitative-methods; for qual+quant integration alterlab-mixed-methods; for test selection/power analysis alterlab-statistical-analysis; for IRB/consent alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Diagnoses part lifecycle problems from system.part_log - part creation, merges, mutations, downloads, removals and moves. Use when there are too many parts or micro-batch inserts, merge backlog or slow merges, mutation storms from ALTER UPDATE or DELETE, unusual DownloadPart replication churn, unexpected RemovePart spikes, or Keeper znode growth tied to part activity.
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.