Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Stage-by-stage pipeline health check - coverage, aging, conversion, and at-risk deals - from CRM opportunity data. Use when the user asks "review my pipeline", "pipeline health", "where's my pipeline stuck", "weekly pipeline review", "which deals should I focus on this week", "any stale or stuck deals", "which deals have past close dates", "which deals are single-threaded", or "check my pipeline coverage against my number".
Convert HTML and Markdown content into Portable Text blocks for Sanity. Use when migrating content from legacy CMSs, importing HTML or Markdown into Sanity, building content pipelines that ingest external content, converting rich text between formats, or programmatically creating Portable Text documents. Covers @portabletext/markdown (markdownToPortableText), @portabletext/block-tools (htmlToBlocks), custom deserializers, and the Portable Text specification for manual block construction.
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.
[DEPRECATED — use deepline-gtm] Use this skill for GTM prospecting, enrichment, qualification, and outbound workflows, especially when users mention Deepline, CSV processing, lead/account/contact research, waterfall enrichment, email or LinkedIn lookup, personalization, scoring, or campaign activation. Route CSV-heavy and provider-driven requests through this skill, then rely on linked sub-docs and provider playbooks for execution details. Available providers: adyntel, ai_ark, apify, apollo, attio, bettercontact, bloomberry, builtwith, cloudflare, contactout, crustdata, crustdata-v2, customer_db, dataforseo, datagma, deepline_native, deeplineagent, discolike, dropleads, exa, findymail, firecrawl, forager, fullenrich, generic_http, heyreach, hubspot, hunter, icypeas, instantly, ipqs, leadmagic, lemlist, linkedin_ads_audiences, linkedin_scraper, lusha, openwebninja, parallel, peopledatalabs, predictleads, prospeo, rocketreach, salesforce, serper, slack, smartlead, snowflake, theirstack, trestle, wiza, zerobounce.
Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.
Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.
Defines and tracks UX success through metrics, measurement frameworks, and experimentation. Part of the Intent design strategy system. Connects design decisions to observable evidence — did the thing we built actually help? Guards against measurement becoming manipulation. Trigger when: defining success metrics, designing A/B tests, building measurement frameworks, analyzing funnels, reviewing metric dashboards, questioning whether the right things are being measured, or when someone says "how do we know if this worked," "what should we measure," "let's run a test," or "the numbers look good but something feels off." Also trigger for ethical measurement reviews and counter-metric definition.
Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.
Search PubMed for scientific literature, including published clinical trials. Fetch abstracts and full text. Link published research to biological databases (gene, protein, nucleotide, PubChem) to discover associations between papers and specific compounds or genes. Verify medical spelling, match raw citations, and cache result sets for bulk processing. Interfaces NCBI E-utilities and PMC BioC APIs.
Analyze Grafana Cloud k6 test run trends over time. Detects slow metric drift (e.g., P95 latency creeping up while still passing thresholds), computes headroom to thresholds, flags anomalies, and recommends threshold tightening. Use when the user asks about test performance trends, wants to know if metrics are degrading, asks whether thresholds should be tightened, or wants a health check across recent runs for a specific test. Trigger on phrases like "how is my test trending," "is P95 getting worse," "check for performance regression," "should I tighten thresholds," "are my tests degrading," "show me trends for test X," "analyze my k6 test runs," or "is my test getting slower." Also trigger when a user asks to check all tests in a project — run this skill once per test and synthesize.
Generate a weekly performance summary from closed trader-memory-core theses — win rate, expectancy, profit factor, R-multiple, MAE/MFE, and win/loss pattern analysis by source skill, exit reason, thesis type, sector, and mechanism. No API required; pure local calculation.
Generate headcount, attrition, diversity, or org health reports. Use when pulling a headcount snapshot for leadership, analyzing turnover trends by team, preparing diversity representation metrics, or assessing span of control and flight risk across the org.
Score any wallet address across three angles — profitability (a real track-record score: is this trader actually good?), copy-tradeability (can YOU actually capture what it makes, plus a latency/slippage/gas backtest), and Dev reputation (if it's mostly a token launcher, how trustworthy are its launches) — plus trading-style tags, all computed deterministically from GMGN portfolio data. Use when the user asks about a wallet's profitability ("这个钱包盈利能力怎么样", "钱包战绩怎么样", "is this wallet profitable"), copy-trade worthiness ("is this wallet worth following", "跟单评分", "钱包评分", "值不值得跟单", "if I copy this wallet what's my real return"), or token-launch/Dev reputation ("这个钱包发盘情况怎么样", "是不是发币方钱包", "dev 信誉怎么样", "is this a token-creator wallet"), or gives a wallet address and wants any of these judgments. A one-line recap of the record or next-step advice (复盘, 一句话总结战绩, 下一步怎么打) goes to gmgn-wallet-review
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.
Log querying, filtering, pattern analysis, and error rate calculation. Use when searching application or infrastructure logs, analyzing error patterns, or correlating log data. Trigger: "show error logs", "search logs for keyword", "log error rate", "recent errors", "logs from last hour", "find log entries", "top error messages", "log patterns", "parse JSON logs", "logs by process group", "log trends over time", "log entry counts per minute". Do NOT use for explaining existing queries, product documentation questions, distributed tracing or span analysis (use dt-obs-tracing).
Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.
Build, review, or improve Core Data persistence in apps that have not adopted SwiftData. Use when working with NSManagedObject subclasses, NSFetchedResultsController for list-driven UI, NSBatchInsertRequest / NSBatchDeleteRequest / NSBatchUpdateRequest for bulk operations, NSPersistentHistoryChangeRequest for persistent history tracking and multi-target sync, NSStagedMigrationManager for staged schema migrations (iOS 17+), NSCompositeAttributeDescription for composite attributes (iOS 17+), or when integrating Core Data threading with Swift Concurrency. For Core Data + SwiftData coexistence or migration, see the swiftdata skill instead.
A short trading portrait of ONE wallet address — a single style tag (磨损户 / P小将 / 印钞机 … plus a speed subtitle), one sentence recapping its record and one sentence of next-step advice, for 1D / 7D / 30D / 全部, voiced 你 for the user's own wallet and 他 for anyone else's; SOL and all 8 EVM chains, chain auto-detected for a 0x address; can render it as a page. USE WHEN a wallet address comes with a request for a brief recap or for what to change — 「帮我复盘一下这个钱包」, 「一句话总结他的战绩」, 「我的打法有什么问题」, 「我钱包下一步该怎么打」, 「交易画像」, 「钱包风格加建议，简短点」, "sum up this wallet in one line". NOT FOR — a bare address, 「能不能跟」 or 「跟着他买我能吃到吗」 (gmgn-wallet-analysis); the full tag-and-badge breakdown or 「是不是机器人」 (gmgn-wallet-style); scores or copy-trade backtests (gmgn-wallet-score); a creator's launches (gmgn-dev-score); current holdings (gmgn-portfolio); a token address or a buy (gmgn-contract-dd, gmgn-token-buy). TIE-BREAK — 打法 / 风格 / 战绩 paired with 问题, 建议, 复盘 or 一句话 routes here; asked alone, they go to the skills above.
Tag any wallet's trading style — one main title from a 20-cell frequency×P&L grid (印钞机, 磨损户, P小将, 一击必杀, and 16 others), one holding-speed subtitle (秒杀流/日内流/波段流/长持流), and any of 14 orthogonal badges plus a wash-trader flag. Every tag states the number that triggered it. Use when the user asks what kind of trader a wallet is — 「这个钱包什么风格」, 「交易风格」, 「打法」, 「是不是机器人」, 「是不是狙击盘的」, 「帮我给这个钱包打标签」, "what's this wallet's trading style", "tag this wallet". A bare wallet address with no question goes to gmgn-wallet-analysis. If the user also wants the tag shown on a real GMGN screenshot or as a clickable interactive page ("生成标签预览", "把标签叠加到这张截图上", "1:1还原钱包页并加标签", "可交互交易风格标签"), answer with Part A below and then also do Part B — the same algorithm, rendered. When 打法 / 风格 comes with 问题, 建议, 复盘 or 一句话总结, hand off to gmgn-wallet-review, which returns one tag plus a one-line recap and next step.
Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.
World-class systematic trading research - backtesting, alpha generation, factor models, statistical arbitrage. Transform hypotheses into edges. Use when "backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, walk forward, " mentioned.
Use when a Lightning Web Component data need is described in ambiguous natural language — turn "get contact info" or "show account data" into a clear, PRD-ready data-requirements spec. TRIGGER when the user says "define data requirements for this LWC", "turn this PRD data section into validated object/field names", "recommend GraphQL vs UIAPI for this data need", "validate these Salesforce API names", or "spec out the LDS adapter for this component", or references LWC bundle files (`.js`, `.js-meta.xml`) whose data layer is not yet specified. DO NOT TRIGGER when the data layer is already fully specified, when authoring the actual query or adapter code from a known spec, or when implementing an LWC end-to-end (use experience-lwc-generate).
Build and maintain a Stockbee-style daily 20% mover study for US equities by scanning +20%/-20% movers, classifying catalysts and setup context, updating forward outcomes, and summarizing cohort patterns. Use when the user asks to run a daily 20% study, backfill historical 20% movers, find recurring edge patterns, or build a model book of explosive market moves.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.
Query Coralogix's Service Catalog (APM v2 entities) with the `cx service-catalog` CLI — discover entity types, list known entities, check their schema, and pull aggregated or timeseries data for services, databases, operations, JVMs, and Kubernetes pods. Use when the user asks to "list services", "what entity types exist", "show me service latency", "check error rate for a service", "which pods are using the most memory", "database operation performance", "JVM GC pauses", "service health over time", "compare services by latency", "what columns are available for this entity type", "service catalog schema", or wants to explore APM entities and their metrics.
Internal capability, not user-invocable: the orchestrator calls it while running blog-write or blog-rewrite to turn research data into dark-mode compatible inline SVG charts (horizontal bar, grouped bar, donut, line, lollipop, area, radar) for HTML or MDX. Produces accessible markup (role=img, aria-labelledby, unique ids), source attribution and transparent backgrounds. Agents cannot call it.
Changes a live Convex schema without downtime: make a field optional, backfill in batches, flip the validator, then clean up. Uses the @convex-dev/migrations component where it fits. Use when renaming or removing a field, changing a type, splitting a table, or when deploy fails with a schema validation error on existing documents.
Retrieve Data Lake Object (DLO) and Data Model Object (DMO) schema information from Salesforce Data Cloud using REST APIs. Use this skill when you need to inspect DLO or DMO field definitions, data types, or metadata. Takes org alias and optional DLO/DMO name as parameters.
Real User Monitoring (RUM) on Dynatrace — web and mobile frontends. Core Web Vitals, user sessions, page performance, mobile crashes, frontend errors, and frontend-backend linking. Query via `user.events`, `user.sessions`, and `dt.frontend.*` metrics. Does NOT cover synthetic monitoring (HTTP/browser/network checks) — that's a separate domain.
Develop and deploy Data Cloud Code Extensions using SF CLI plugin. Use this skill when creating custom Python transformations for Data Cloud, deploying code extensions, or testing data transformations. Supports init, run, scan, and deploy operations.
Credentialed Google API calls for a blog: PageSpeed Insights, CrUX history, Search Console performance and URL Inspection, GA4 organic traffic, Cloud NLP entities, YouTube video search, Google Ads Keyword Planner, and the Indexing API (job and livestream pages only). Uses the user's Google credentials, quotas, and sometimes billing, and asks for consent before the first call. Runs only on an explicit request: /blog google, "run PageSpeed on this blog URL", "pull my Search Console data", "inspect this URL's index status", "set up Google API credentials". Shares config with claude-seo at ~/.config/claude-seo/google-api.json.
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
Track reviews, ratings, sentiment, and brand mentions across Google Maps, Booking.com, TripAdvisor, Facebook, Instagram, YouTube, and TikTok. Use when user asks to monitor brand reputation, analyze reviews, track mentions, or gather customer feedback.
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.