Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Best-practices reference for the ScraperAPI Java SDK (com.scraperapi:sdk Maven artifact). Consult whenever the user is writing, debugging, or reviewing Java code that calls ScraperAPI. Use when user asks: "scrape a website with Java and ScraperAPI", "ScraperAPI Java example", "how do I add ScraperAPI to my Maven project", "Java ScraperAPI render", "ScraperAPI Java fluent API", "ScraperAPI Java premium proxy", "ScraperAPI Java error handling", "ScraperAPI Java retry". Covers Maven/Gradle setup, the fluent builder API, all request parameters, the escalation ladder, async jobs, structured data calls, error handling, and credit costs. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Best-practices reference for the ScraperAPI PHP SDK (scraperapi/sdk Composer package). Consult whenever the user is writing, debugging, or reviewing PHP code that calls ScraperAPI. Use when user asks: "scrape a website with PHP and ScraperAPI", "ScraperAPI PHP example", "how do I use the ScraperAPI PHP SDK", "PHP ScraperAPI render", "ScraperAPI PHP premium proxy", "ScraperAPI PHP Composer install", "ScraperAPI PHP error handling". Covers Composer setup, all request parameters, the escalation ladder, POST requests, error handling, and credit costs. Note: Transmits user-supplied queries, URLs, and content to ScraperAPI.
Use the Scrapfly RPA API to run typed workflow graphs against managed cloud browsers, retrieve produced artifacts (HTML snapshots, downloads, HTTP response bodies, JS return values), and route those artifacts into Scrapfly's managed bucket or your own customer GCS or S3 bucket. Use when the user asks to "run an RPA workflow", "fetch artifacts from a run", "configure BYOS storage", "set up customer GCS or S3 for RPA", "test a storage destination", or any task involving the /v2/rpa/* endpoints.
Scaffold custom objects, fields, validation rules, permission sets, and other schema metadata as SFDX source XML. Use when asked to create objects, custom fields, permission sets, validation rules, or generate metadata XML. Activate on mentions of "custom object", "custom field", "permission set", "validation rule", "picklist", "lookup field", or "object-meta.xml".
Definition-of-done checklist for shipping a new write, signal, table, edge, or query to the ax graph. Use when adding a SurrealDB table/edge/field, an ingest derive-stage, a new analytic query, or any new ax capability - before opening the PR. Ensures every write gets an on-demand read AND a proactive (agent-facing) read AND docs/distribution, not just the write. Triggers on "ship", "new signal", "new table/edge", "new lens/query", "wire this up", "is this done", or finishing an ax feature branch.
Interact with SwanLab — both writing tracking code (init/log/finish/multimedia) and querying experiment data via CLI (`swanlab api`). Use this skill when the user wants to write training tracking code, log metrics or media, manage experiments, inspect metrics/logs/keys, list projects/runs, filter experiments, manage self-hosted users, automate queries via CLI, or mentions "swanlab", "experiment tracking", "log metrics", "swanlab api", "swanlab cli".
Comprehensive guide for using `looker-cli`. Covers profile setup and defaults, using the --profile flag, persistent logins (`session login`), command discovery (`meta tree` & `meta search`), and request body inspection (`--describe-body` & `--template`).
Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
Analyzes Weibo (微博) trending topics and generates creative product ideas based on hot search keywords. Fetches real-time trending data from Weibo API, researches background information and user insights, evaluates product development potential using a 100-point scoring system, and creates an interactive HTML dashboard. Use when analyzing Chinese social media trends, identifying product opportunities from trending topics, or generating market-driven creative product concepts.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
This skill generates interactive Chart.js visualizations for use in iframes using any chart type supported by the library (line, bar, pie, doughnut, radar, polar area, bubble, scatter). Use this skill when users need to create data visualizations for educational content, reports, or dashboards. The skill creates complete MicroSim packages with HTML, CSS, and documentation.
Step-by-step workflow for creating complete Power BI reports from scratch using pbir CLI. Covers model discovery, report creation, page layout, theme setup, visual placement, field binding, filtering, formatting, validation, and publishing. Automatically invoke when the user asks to "create a new report", "build a report from scratch", "make a dashboard", "set up a report with KPIs", "create an executive dashboard", "add pages and visuals to a new report".
Enforces database integrity for PostgreSQL and Prisma systems. Use when designing schemas, writing migrations, or configuring Row-Level Security. Use for type-safe SQL, naming alignment, constraint validation, zero-trust RLS policies, UUIDv7 primary keys, and zero-downtime deployments.
Use when designing a study that benchmarks AI systems against a human-expert panel, before data collection. Plans the arms, decoupled rubrics with anchors, planted calibration probes, reviewer panel, inter-rater reliability targets and LLM-as-judge versus human adjudication.
Query EVM chain data via Etherscan API v2. Use for on-chain lookups where Etherscan v2 applies: balances, transactions, token transfers (ERC-20/721/1155), contract source/ABI, gas prices, event logs, and verification of transaction completion. Also trigger when another tool submits a transaction and you need to confirm it finalized on-chain.
Owns Fabric Eventstream items end to end: sources, operators, destinations, routing, retention, throughput and connection strings, plus read-only topology checks. Pick it for anything that names an Eventstream, creating and listing included. Where the rows finally land is eventhouse-cli.
Diagnose Splunk forwarder and data-ingest paths from sanitized, read-only evidence. Use for Universal or Heavy Forwarder transport, deployment-client effects on ingestion, HEC and other inputs, parsing and timestamps, event boundaries, routing, sourcetype or index mismatches, missing, delayed, duplicated, or dropped events, and queues or backpressure in Splunk Cloud Platform or Splunk Enterprise; do not use for live mutation, broad architecture, fleet policy, HEC token administration, search-time extraction, or provider-owned remediation.
Generates GTM implementation documentation, reporting impact analysis, GA4 report configurations, and stakeholder summaries. Use when users need to "document GTM implementation", "what reports can I build", "create event schema docs", "generate stakeholder summary", "analyze reporting impact", or want to understand business value of tracking data. Creates technical documentation, suggests GA4 explorations, defines remarketing audiences, and translates technical events into business insights.
DWS cluster high memory root cause diagnosis skill, based on KooCLI v3.2.0+ and DWS Autopilot MCP Server. Automatically collects memory metrics, analyzes root causes (customer-side / system-side), and outputs a standardized diagnosis report. Applicable to scenarios where DWS cluster memory usage is too high, memory alarms, and OOM. Trigger words: 内存高, 内存告警, 内存诊断, 内存使用率过高, 内存不足, OOM, 内存溢出, 动态内存使用率超阈值, high memory, memory alarm, memory diagnosis.
Operate the Kalshi Trading Bot CLI for prediction-market research, edge discovery, basket construction, backtesting, portfolio monitoring, and guarded trade execution. Use when the user mentions kalshi-trading-bot-cli, Kalshi CLI, prediction market trading workflows, market edge scans, basket sizing, or CLI-based Kalshi trading.
Use this skill when the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx base.
Analyze workforce data — attrition, engagement, diversity, and productivity. Trigger with "attrition rate", "turnover analysis", "diversity metrics", "engagement data", "retention risk", or when the user wants to understand workforce trends from HR data.
Find out what is going wrong in LLM or agent traffic by reading sampled Phoenix traces, spans, or sessions, writing free-form notes (open coding), then grouping the notes into a few narrow annotations, one per failure dimension with a small label set and counts, that pick eval targets and fix priorities (axial coding). Use for "what's going wrong with this agent", "I just instrumented my app, where do I start", "review these traces", "the chatbot keeps losing context", "what kinds of mistakes is the model making", "what categories of failures do we have", "what should I build evals for", "how do I prioritize fixes", "group these notes", "MECE breakdown" — or any framing that needs observations or categories grounded in real traces rather than invented top-down, even without naming the technique.
Anchors predictions in historical reality by identifying a class of similar past events and using their statistical frequency as a baseline (outside view) before analyzing case-specific details. Use when starting a forecast, establishing base rates, testing "this time is different" claims, or when user mentions reference classes, outside view, base rates, or starting a new prediction.
Enriches contacts and companies with verified emails, phones, and firmographic data — single-record, batch, and waterfall enrichment with credit optimization. Use when contact data is missing emails or phones, lead records are incomplete or outdated, enrichment credits are burning too fast, you need to enrich a batch of leads before a campaign, or a single enrichment provider isn't catching enough data. Do NOT use for building new prospect lists from scratch (use /sales-prospect-list), interpreting buying signals (use /sales-intent), CRM data hygiene and deduplication (use /sales-data-hygiene), ZoomInfo-specific enrichment config (use /sales-zoominfo), Clearbit/Breeze Intelligence platform help (use /sales-clearbit), RB2B platform help (use /sales-rb2b), 6sense platform help (use /sales-6sense), general Apollo platform help (use /sales-apollo), Clay platform help (use /sales-clay), or LeadMagic platform help (use /sales-leadmagic).
Use when the user wants Facebook research or workflow guidance for lead generation, influencer discovery, brand monitoring, competitor analysis, content analytics, trend research, or audience analysis, including page or profile audits, post, reel, photo, or comment extraction, group monitoring, transcript retrieval, and Facebook ad or advertiser analysis.
Use when: comparing SQL Server tables across instances, data migration validation, ETL verification, row mismatch detection, schema drift, reconciliation report, production vs staging comparison. Uses mssql-python driver with Apache Arrow for fast columnar data transfer and comparison.
TikTok Shop public intelligence via unified host MCP `topview-mcp` (progressive disclosure: use_tiktok_data → get_tool_schema → call_tool; deferred td_* tools). Use when: TikTok Shop market trends, product selection, viral material breakdown, scripts, content calendar, affiliate/creator ops, ads/live ops. NOT for: Amazon, Shopee, YouTube KOL pool, or media generation (use topview-amazon-ops, topview-shopee-ops, topview-youtube-kol-ops, or topview-generate).
Generate your Weekly Performance Advisor dashboard from your La Growth Machine data: a two-tab cockpit (To do + Weekly performance) that flags campaigns to fix, sorts replies by urgency, and tracks reply volume week over week. It pulls only YOUR live LGM data, scores each running campaign against baked 3-zone benchmarks, classifies your untagged replies, and renders a live artifact you can re-open every Monday. Use when the user wants a weekly outbound performance dashboard, a Monday cockpit, campaign health at a glance, "how are my campaigns doing this week", "who do I need to reply to", or a reply-triage + campaign-health view. Triggers on: 'weekly performance dashboard', 'my outbound cockpit', 'weekly performance advisor', 'campaign health this week', 'how are my campaigns doing this week', 'my Monday cockpit'. First run does a short setup (detect + connect LGM, pick the identity, optional deal layer); every run after that just rebuilds the dashboard. Maintained by La Growth Machine.
Zvec vector database development assistant. Use this skill when users need to develop vector search applications based on zvec, build RAG systems, implement semantic search, or handle vector data storage and querying. Suitable for Python and Node.js development environments, providing complete technical guidance from basic concepts to advanced usage. Proactively use this skill when users mention vector databases, similarity search, embedding storage, HNSW/IVF indexes, hybrid search, multi-vector queries, or zvec API usage.
Diagnose ClickHouse merge performance, part backlog, merge memory, and "too many parts" errors. Use when merges look stopped or slow, parts pile up, TOO_MANY_PARTS or parts_to_throw_insert is hit, or merges fail with MEMORY_LIMIT_EXCEEDED.
Diagnoses ClickHouse mutations - ALTER UPDATE, ALTER DELETE, MATERIALIZE and column changes. Use for stuck mutations, mutations that never finish, latest_fail_reason errors, growing mutation queues and slow ALTER operations.
Fast ClickHouse server health snapshot (object counts, memory, disks, replication summary, system log TTLs, error rates, background pools, detached parts) that routes to the specialist skills. Use as the entry point for "is the server healthy" or when the problem area is not yet known.
Azure Machine Learning Workspace and Azure AI Foundry deep dive. PROACTIVELY activate for: (1) creating and configuring Azure ML workspaces, (2) Azure AI Foundry hubs and projects, (3) ML workspace networking (managed VNet, private endpoints, DNS), (4) ML compute clusters and compute instances, (5) managed online endpoints, batch endpoints, Kubernetes endpoints, (6) managed identities for ML resources, (7) ACR integration for custom environments, (8) storage account configuration, (9) az ml CLI v2 commands, (10) PowerShell Az.MachineLearningServices, (11) reading ML compute and deployment logs, (12) GPU SKU selection (ND/NC series, H100/H200/A100). Provides: workspace setup playbook, network-isolation patterns, endpoint deployment templates, az ml CLI cheat sheet, and log diagnosis workflow.
Use when the user says 'export my site', 'backup my pages before editing', 'download my content locally', or 'migrate content to my other site'. Exports pages, posts, and custom posts to portable local packages with builder data, media, and markdown, then imports elsewhere with ID remapping.
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.