Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Make Cargo actually run something, or show what it would run — execute one connector action, run a multi-step workflow, trigger a batch across a whole segment or model, message an AI agent, build or edit a node graph, draw a workflow, tool or play as a diagram, and query the runtime tables (runs, batches, spans, records) with SQL. Triggers: "run this on all my contacts", "execute the action", "kick off a batch", "build a workflow", "schedule a play", "make it run every morning", "ask the agent", "show me the workflow", "what does this tool do", "visualize this play", "draw the graph", "explain this workflow", "how many runs failed today", "what is the output schema for this action", "add a step that". Skip when: explaining why a run misbehaved — use cargo-diagnostics; downloading result files — use cargo-analytics; committing the workflow as code — use cargo-project.
Get data out of Cargo and measure what ran — download a run output, export a segment or model to CSV or JSON, and pull run and batch success and error counts. Triggers: "download the results", "export this to CSV", "give me the file", "how many succeeded", "what is my error rate", "send me the enriched list", "get the output of that run", "how many records did it write". Skip when: asking why something failed or where credits went — use cargo-diagnostics; asking about credits, plans, or invoices — use cargo-billing.
Work with the data inside a Cargo workspace — models (Companies, Contacts, Deals…), datasets, columns, relationships, records, and SQL over workspace storage. Triggers: "what models do I have", "show me the schema", "add a column for", "how many contacts do I have", "SELECT … FROM", "query my companies table", "join contacts to companies", "what is the DDL", "set up a webhook-fed model", "where does this field live", "import this into a model", "unify these models", "merge duplicate accounts", "link contacts to companies", "set up a relationship between". Skip when: querying run or batch telemetry rather than business data — use cargo-orchestration; naming a reusable filtered audience — use cargo-segmentation.
Build a fully automated AI-powered data collection agent for any public source—job boards, prices, news, GitHub, sports, anything. Runs on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions. Use when the user wants to monitor, collect, or track any public data automatically.
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
Codified demand planning expertise for multi-location retailers: demand forecasting method selection, ABC/XYZ segmentation, safety stock and reorder-point optimization, promotional lift and post-promo dip estimation, and seasonal transition and markdown timing. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.
Admin-only paginated viewer for stable canister state. Use whenever the user asks for a viewer, dashboard, debug panel, or admin browse over backend data — users, items, orders, logs, or any stable Map/Set/Array/VarArray/List/Stack/Queue. Pre-installed in every Caffeine app via the `caffeineai-data-viewer` mops package; this skill explains what it does and how to keep using it correctly.
Ingest, index, search, edit, and monitor video and audio with the VideoDB Python SDK—upload from files, URLs, or RTSP feeds, build spoken and scene indexes with timestamped search and playable clips, transcode and reframe, do timeline edits (subtitles, overlays, dubbing), and run real-time alerts on live streams or desktop capture. Use when working with video search, transcription, clipping, transcoding, streaming, or live video alerts.
JetBrains Exposed ORM patterns, including DSL queries, DAO patterns, transactions, HikariCP connection pooling, Flyway migrations, and repository patterns. Use when working with the Exposed ORM, including DSL or DAO queries, transactions, pooling, or migrations.
Use this skill for token-level data: search tokens; trending/hot tokens (热门, 代币榜单); liquidity pools; holder distribution (whale/巨鲸, sniper, bundler-tagged holder %); token risk metadata (riskControlLevel, tokenTags, dev stats, suspicious/bundle holding % via advanced-info); recent buy/sell activity; trade feed/逐笔成交/每笔交易/stream trades; top profit addresses; token trade history; detailed price info with market cap, volume, liquidity, and holder count (price-info); or holder cluster analysis (持仓集中度, cluster overview, cluster rug pull risk/跑路风险, new wallet percentage/新钱包持仓比例, holder clusters, 'are top holders in same cluster'). Also handles Market API payment/计费/x402/402, Basic/Premium tier/quota/额度 questions, and MARKET_API_*_OVER_QUOTA / confirming:true responses on token endpoints (advanced-info, top-trader, cluster-*, trades, hot-tokens). NOTE: if the user wants to write a WebSocket script/脚本/bot, use okx-dex-ws instead.
Alibaba Cloud Bailian model fine-tuning entry point: use `bl dataset` / `bl finetune` / `bl deploy` when users want to fine-tune or train their own models (fine-tune, supporting SFT / SFT-LoRA / DPO / DPO-LoRA / CPT, across text, speech, and images), validate or upload training datasets, check training progress and logs, select a checkpoint, export fine-tuned artifacts, or deploy a dedicated model as a service. The workflow is: validate data → upload to get file-id → finetune create to start a task → watch progress → export → optionally compress/quantize → deploy. An API key is required; preview write operations with `--dry-run` first. Do not trigger for fine-tuning explicitly named for Volcano Ark/ark; use bailian-model-recommend to choose a model; use bailian-gen to generate images or videos with an existing model; use bailian-cli for other Bailian resource management. Official installation: `bl skill init` (installed together with the shared protocol bailian-protocol).
Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.
Analyze AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.
Use when the user wants to run SQL—especially analytical SQL—on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB—embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: the user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration
Use when discovering niche signals, auditing ICP or won/lost evidence, rescoring accounts, or building account and lead scoring Plays. Triggers on fit scoring, engagement scoring, backtesting, holdout evaluation, predictive lift, external proxies, and scoring leakage. Skip pure outreach copy or contributor skill installation tasks.
Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic-view DDL against Snowflake via CLI, or to guide Snowflake CLI installation and connection setup.
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
Build, modify, and ship Grafana dashboards as JSON via the HTTP API — panel types (timeseries / stat / gauge / table / heatmap / logs / traces / node-graph), `gridPos` 24-column layout, units, thresholds, template + datasource + chained variables, transformations (`organize` / `calculateField` / `filterByValue`), panel + dashboard links with `${__field.labels.x}` / `${__from}`, and Loki/Prometheus annotations. Use when scripting dashboard creation, writing the dashboard JSON for a new service, adding a `$job` dropdown variable, computing an "Error %" column with a transformation, overlaying deploys as annotations, or pushing a dashboard via `POST /api/dashboards/db` — even when the user says "create a dashboard for this metric", "add a service dropdown", "show errors as percentage", "overlay our deploys", or "export the dashboard JSON" without naming the API or schema. After every API push, verify with the returned `version` plus a GET on the dashboard UID.
Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.
Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file
Plan a SaaS vendor's presence as a source connector on third-party ETL/ELT platforms - demand validation before any build, platform selection from where customers' data stacks run, a build-path menu (platform-managed listing, vendor-maintained SDK connector, custom open-source tap) ranked by who absorbs the maintenance tax, an extraction-readiness audit of the product API, honest CDC scoping (API sources ship pseudo-CDC, never log-based), certification targets, and a funded maintenance plan. Use whenever the user mentions an ETL or ELT catalog listing, a source connector, data-pipeline integration, or adding CDC - even if they never say "connector strategy". Do NOT use for the vendor's own inbound marketplace - use samber/developer-platform-skills@connector-marketplace-strategy instead.
Answer crypto market-data questions with BlackForge. Use this skill whenever the user asks about order-book or trade data for a coin on a spot venue — its latest stats on an exchange, order-book depth or resting/pulled liquidity for a pair, taker buy-vs-sell volume, order-ladder rungs, price-level lifetime, trade timing, outsized trades, or market-cap/attention enrichment; when they want to pull, chart or compare a metric over a time range; or when they ask which pairs a venue lists. Covers 9 spot exchanges (binance, bitget, bybit, coinbase, gate, kraken, kucoin, mexc, okx) and 14,000+ spot pairs — 169 measurement columns per pair per closed minute (1m on Ultra/PAYG, 5m/1h/1d rollups on every plan). Trigger even when BlackForge is not named but the question is about crypto market data on a venue. Drives the BlackForge MCP tools (blackforge_catalog / blackforge_symbols / blackforge_latest / blackforge_series / blackforge_usage) or the `blackforge` CLI; never reimplements the API. Every returned column is a measurement, not a trade call.
Plan and review MySQL/InnoDB schema, indexing, query tuning, transactions, and operations. Use when creating or modifying MySQL tables, indexes, or queries; diagnosing slow/locking behavior; planning migrations; or troubleshooting replication and connection issues. Load when using a MySQL database.
Design customer-facing SQL access to a product's data—a JDBC/ODBC endpoint, warehouse share, or hosted query surface. Covers the architecture gate by scan frequency (zero-copy share, replicated copy, per-tenant compute isolation), engine-level tenant isolation with secure views and row-level security, an additive-only schema-stability contract enforced in CI, BI-tool connectivity and driver certification, query governance and cost caps, short-lived credential issuance, and pricing. Use whenever the user mentions JDBC, ODBC, SQL access for customers, connecting Tableau or Power BI to product data, or pricing a SQL add-on—even if they never say “SQL access.” Do NOT use for bulk file delivery; use samber/developer-platform-skills@bulk-data-sharing-design instead.
Design bulk file and lake export as a B2B product surface: the two-camp choice between Parquet file drops on object storage and native warehouse/lake sharing (Snowflake shares, Delta Sharing, linked datasets), the partitioning and schema-evolution contract across batches, delivery cadence and freshness commitments (full dump vs incremental vs CDC vs streaming), shared-bucket security with per-recipient credentials, egress cost allocation, and cross-border data residency. Use whenever the user mentions bulk export, data dumps to S3, Parquet, Snowflake shares, Delta Sharing, or export freshness SLAs—even if they never say “bulk data sharing.” Do NOT use for live SQL endpoints—use samber/developer-platform-skills@sql-jdbc-access-design instead
Write, validate, and optimize PromQL for Prometheus / Grafana Mimir / Grafana Cloud Metrics. Covers `rate` vs `irate` vs `increase`, label matchers and regex, `sum / avg / topk / by / without` aggregation, classic + native `histogram_quantile`, ratios with divide-by-zero guards, `absent` / `changes` for staleness, time offsets and `predict_linear`, recording-rule naming, SLO + burn-rate math, and a cardinality-hunting playbook. Use when writing a metric query, fixing wrong p95s, building an error-budget alert, debugging "query is slow", finding the noisy label that blew up cardinality, or migrating a dashboard query to a recording rule — even when the user says "calculate the error rate", "p99 latency", "sum by service", "why is this query slow", or "what's filling Mimir" without naming PromQL.
Guides general use of AWS messaging and streaming services. Covers Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK). Use when reasoning about messaging and streaming patterns. Also identifies which AWS service owns each customer communication channel: email (Amazon SES), and WhatsApp, SMS, MMS, RCS, voice and mobile push (the AWS End User Messaging family of services). Routes the request to the specialized skill for that channel. Defers to the channel's specialized skill when the user already named a specific channel. In general, use specific skills or documentation searches for detailed service-specific questions. Do NOT use for MSK or Managed Service for Apache Flink questions, prefer specific skills. Does not configure customer communication channels; defers to specific skills.
A Twitter API alternative and X API alternative on fetcher.sh — pay-per-call in USDC via x402, or prepaid credits with a Bearer key, no OAuth and no developer application. Use when the user wants to search tweets by keyword, hashtag, or advanced operators (from:, to:, since:, until:, min_faves:, filter:), scrape a Twitter/X profile by handle, pull a user's tweets, replies, followers, or followings, fetch a single tweet with its replies or retweeters, read a Twitter List's members or tweets, check trending topics by country, or search for X accounts by name. Also covers Twitter data pipelines, competitor monitoring, hashtag tracking, sentiment analysis input, or follower export without the official X API's pricing tiers or app-review process.
Use this skill when the user asks about: 'any crypto news', 'latest news', 'market update', 'daily briefing', 'BTC news', 'ETH news', 'news on SOL', 'search SEC ETF', 'regulation news', 'Bitcoin halving', 'is BTC bullish or bearish', 'coin sentiment', 'sentiment trend', 'trending coins', 'top bullish coins', 'bearish sentiment', 'social buzz', 'market mood', 'fear and greed', 'full article', 'read more', 'economic calendar', '经济日历', 'macro data', '宏观数据', 'NFP', 'nonfarm', '非农', 'CPI', 'GDP', 'FOMC', '利率决议', 'interest rate decision', 'PMI', 'unemployment rate', '失业率', 'economic events', '经济事件', or any request for crypto news, market intelligence, sentiment analysis, social trend data, or macro-economic calendar. Requires API credentials. Do NOT use for market prices/candles (okx-cex-market), placing orders (okx-cex-trade), or account info (okx-cex-portfolio).
Scrape public Instagram data without logging in and without a Meta developer account — profiles, posts, reels, stories, comments, hashtags, locations, and mentions. Use when the user wants to fetch an Instagram profile's follower count, bio, or post count, pull a profile's recent posts or reels, read the comments on a post, find posts under a hashtag or at a location, search for accounts by keyword, track a competitor's Instagram content, vet or discover influencers, export engagement metrics, monitor a brand's mentions, or build an Instagram data pipeline. Runs on Apify's Instagram Scraper; needs a free Apify account (API token), which this skill will help set up on first use.
Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
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.