Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Use when finding real role-holders at known company domains from an ICP, especially prompts like 'find all job titles at these companies', 'find qualified titles', 'find RevOps or marketing-ops buyers', or when exact title discovery should precede paid people search.
Models how much pipeline a team needs relative to quota—coverage ratios derived from win rates (raw multiplier, stage-weighted, conversion-inversion), segment-level coverage targets, in-quarter timing and the point of no return, seasonality indexing, and pipeline-gap math that turns a ratio into new-pipeline-required. A macro modeling exercise for sales leadership and RevOps, covering B2B and high-velocity/B2C motions. Use whenever the user mentions pipeline coverage, 3x pipeline, a pipeline gap, forecast shortfall, or “we missed quota with 4x coverage,” even without the word coverage. Do NOT use for deriving the quota itself (mbfinotti/sales-skills@sales-quota-setting) or inspecting one deal (mbfinotti/sales-skills@deal-red-flags).
Estimates TAM, SAM, and SOM for a market or sub-segment to ground sales capacity, territory, and quota planning—top-down, bottom-up using named accounts or population math, value theory, triangulation, a capacity-based SOM ceiling, and a per-layer refresh cadence. A macro planning exercise for sales leadership and RevOps, covering B2B and B2C. Use when the user mentions TAM, SAM, SOM, market size, addressable market, “how big is this market,” or the number behind next year's quota, even without those acronyms. Sizes for planning, not pitching. Do NOT use for defining the ICP (mbfinotti/sales-skills@sales-icp-definition) or turning SOM into quotas (mbfinotti/sales-skills@sales-quota-setting).
Make a canister's data queryable by the Caffeine Data Intelligence agent. Use whenever an app stores structured data (Maps/Lists/arrays of records) that should be answerable in natural language — "top customers", "revenue by region", "active projects". Adds a discoverable `schema()` and a JSON `execute()` query endpoint via the `caffeineai-oql` mops package's `Expose` mixin.
Use this skill when building, enriching, auditing, or uploading B2B paid ads audiences to Google Customer Match, Meta/Facebook Custom Audiences, or LinkedIn Matched Audiences. Triggers on phrases like '/deepline-ads-audience', '/deepline-ads-audiences', 'upload this audience', 'create custom audiences', 'personal email hashes', 'increase Facebook match rate', 'Google ads audience', 'Meta audience', 'FB audience', or any workflow that turns CRM/customer/contact data into paid ads upload lists. Skip for outbound prospecting sequences, cold email, or pure campaign copywriting.
Designs, authors, refactors, and hardens production-grade Cloud Firestore Security Rules (firestore.rules). IMPORTANT: If subagent delegation AND the firestore-rules-author subagent are available in your environment, delegate authoring firestore.rules to the firestore-rules-author subagent. If subagent delegation is unavailable (e.g. not enabled in the IDE), firestore-rules-author is not installed, or you are running inside firestore-rules-author, follow this skill directly.
Quick reference for the Caffeine Data Intelligence agent to query an OQL-exposing canister (schema() + execute()) through the `icp` CLI against the project's `backend` canister: read the schema, form JSON queries (filter / order / paginate / aggregate / dotted-path edges), and parse the Candid result rows.
ACCESS-GATED beta. Deepline Monitors are provider event feeds (job posts, email replies, funding, intent) that stream into your warehouse and trigger plays. Only use if you have monitor access: run `deepline monitors status` first; if it reports no access, do NOT use this recipe — tell the user to contact the Deepline team.
Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.
Design the org-wide revenue KPI framework at the macro level - which metrics matter at each org level from board to IC, how they roll up through a reconciling metric tree with explicit math, who owns each branch, which guardrail counter-metrics ride alongside each owned number, and how the set is gated by company stage and business model. Use whenever the user mentions a revenue KPI framework, a metric hierarchy, a KPI tree, a North Star metric, driver metrics, guardrail counter-metrics, NRR, or which revenue metrics each org level should track - even if they never say "framework". Covers B2B and B2C. Do NOT use for writing the board report itself - use mbfinotti/revops-skills@revenue-reporting instead.
Run a periodic, checklist-based hygiene audit over an active pipeline snapshot — stale deals (no stage, close date, or amount change, no logged touches) flagged against segment-specific stage medians, close-date push-count anomalies, and missing forecast-critical fields — producing an exception list with a disposition per flagged deal, a remediation plan, and a pass threshold. Use whenever the user mentions pipeline hygiene, a stale deal audit, pipeline cleanup before a QBR, deals that keep pushing, a close date that keeps slipping, missing fields on deals, or "our pipeline is full of junk" — even if they never say "hygiene". Covers B2B and B2C/high-velocity/PLG. Do NOT use for rewriting stage definitions — use mbfinotti/revops-skills@pipeline-stage-definition-audit instead.
Trace where deals and revenue silently exit one specific funnel and size the loss in recoverable (never gross) dollars - untracked steps, unworked leads, manual handoffs, mid-funnel stalls, paper-process drops, billing and renewal misses - separating real leaks from healthy disqualification and data-capture gaps. Use whenever the user mentions revenue leakage, funnel drop-off, a leaky funnel, deals disappearing, unworked leads, handoff gaps, "where are we losing deals", or "why did pipeline vanished" - even if they never say "leakage". Covers B2B sales-led and B2C/self-serve/PLG, one funnel instance at a time. Takes stage definitions as given - to audit those, use mbfinotti/revops-skills@pipeline-stage-definition-audit instead.
Diagnose why an existing sales forecast is unreliable and recommend fixes — stage inflation and happy ears, sandbagging, deals without verifiable buyer evidence, stale or mass-pushed close dates, wrong forecast-category assignment, zombie and duplicate opportunities, late-created deals, manager roll-up overrides, and comp incentives that reward bias — separating data-quality from behavioral from genuine demand problems. Use whenever the user mentions forecast accuracy, a forecast miss, reps sandbagging, deals that keep slipping, "why did we miss the number", or "our commit is never right" — even if they never say "forecast". Covers B2B deal-based and B2C/high-volume. Do NOT use for pipeline coverage modeling — use mbfinotti/sales-skills@sales-pipeline-coverage-modeling instead.
Build and troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
Design and operate CRM field-level governance: who owns each CRM field, which system is authoritative for it, how often it must be refreshed, and how the rules are enforced. Produces a field dictionary, per-field ownership, a system-of-record map with write-precedence and conflict rules, freshness SLAs, an enforcement plan, a field lifecycle, and a governance cadence. Use whenever the user mentions field ownership, single source of truth, system of record per field, data dictionary, stale CRM data, custom field sprawl, "which system wins", or "our CRM fields contradict each other"—even if they never say "governance". Covers B2B and B2C. Do NOT use for org-wide, object-level source-of-truth policy—use mbfinotti/revops-skills@revenue-data-governance-strategy instead.
Set org-wide revenue data governance policy: which system is source of truth per object class (account, contact, opportunity, subscription, usage event, revenue metrics), how cross-team data contracts bind producers to consumers, where revenue metric definitions live, and who arbitrates disputes. Produces a per-object SOR/SOT designation table, an identity-resolution spine, a data-contract register, and a federated ownership model. Use whenever the user mentions source of truth, "two ARR numbers", GTM data contracts, revenue data governance, metric definition ownership, or "finance and sales report different numbers"—even if they never say "governance". Covers B2B, PLG/B2C, hybrid. Do NOT use for CRM field-level rules—use mbfinotti/revops-skills@crm-data-governance instead.
Plans, implements, and reviews migrations from other CMSes and content systems into Sanity. Use when migrating or replatforming to Sanity from AEM, Adobe Experience Manager, Contentful, Strapi, Webflow, WordPress, Payload, Drupal, Markdown/MDX/frontmatter files, WXR/XML exports, CMS APIs, database dumps, static HTML, or when designing extraction, transformation, Portable Text conversion, asset migration, redirects, validation, and cutover workflows
Design, validate, and recalibrate a lead scoring model - fit and engagement signals, weighting, negative scoring and exclusions, decay, MQL/PQL threshold and tier setting, backtesting against closed-won/closed-lost outcomes, and governance; also diagnoses an existing broken model. Use whenever the user mentions lead scoring, an MQL threshold, fit vs engagement scoring, product-qualified leads, score decay, "score my leads", "our lead scores are wrong", "sales rejects our MQLs", or "too many junk MQLs" - even if they never say "scoring model". Covers B2B sales-led and B2C/PLG, rules-based design and predictive readiness. Do NOT use for assigning scored leads to reps - use mbfinotti/revops-skills@lead-routing instead.
Design, validate, and govern a composite customer health score: product usage and engagement signals combined into one weighted, normalized, decayed, banded account score that flags both churn risk and expansion readiness, backtested against real churn outcomes and recalibrated. Use whenever the user mentions a customer health score, health scoring model, account health, signal weighting, score bands, at-risk account flagging, churn risk score, expansion risk, or says “green accounts keep churning”—even if they never say “health score.” Covers B2B account-level rollup and B2C/PLG continuous scoring. Do NOT use for discovering which indicators actually predict churn—use mbfinotti/revops-skills@customer-churn-signals instead
Deprecated alias. This skill was renamed to `deepline-research`. Use when a user or script still invokes `/deepline-pre-research`: immediately load and follow the `deepline-research` skill instead. 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.
Define the metric set and narrative structure for an executive or board revenue report so it tells one coherent story—a small stable metric spine, locked definitions, an answer-first narrative around plan vs actual vs forecast, presenting a miss without surprising the board, the reconciliation and sign-off chain, and weekly vs monthly vs quarterly cadences. Use whenever the user mentions a board revenue report, exec deck metrics, the QBR revenue section, a monthly revenue review, an investor update, or a board deck that reads as a data dump—even if they never say “reporting”. Covers B2B subscription, B2C, transactional, usage-based, and PLG. Do NOT use for designing the org-level KPI hierarchy—use mbfinotti/revops-skills@revenue-kpi-framework instead.
Design a pre-launch ad creative test plan—falsifiable hypothesis, isolation level, test cells with per-cell budgets, required sample, spend and duration, and kill/scale rules pre-registered before any money moves. Every cell carries an explicit read standard, so an underpowered screen is never dressed up as an A/B test. Use whenever the user wants to test ads, mentions an A/B or split test on creative, asks how much budget or how long a test needs, or mentions sample size, statistical significance, single-variable vs big-swing testing, or test cell structure—even if they never say “test plan”. Covers B2B and B2C. Do NOT use to read results from a test already running—use mbfinotti/advertising-skills@ad-creative-fatigue instead.
Assemble, define, validate, and rank leading indicators of customer churn from account activity, product usage, and support data into a ranked churn signal register—each signal includes an event, threshold, window, baseline, lift over base rate, lead time, and coverage. Use whenever the user mentions churn signals, churn indicators, leading indicators of churn, an early warning system for at-risk accounts, champion departure, seat utilisation drops, payment failure, or says “our health score misses churn”—even if they never say “signal.” Covers B2B and B2C subscription. Do NOT use for combining signals into one composite score, weights, bands, or tiers—use mbfinotti/revops-skills@customer-health-score instead
Compute a business's CAC and ROAS family metrics from real spend and outcome data, then judge whether the spend is healthy against three references— the business's own break-even, its own trailing history, and provenance-labelled external benchmarks—returning healthy, watch, unhealthy, or insufficient evidence. Use whenever the user asks whether their CAC is too high, whether a ROAS is good, what ROAS to aim for, whether ads are profitable, or mentions MER, blended CAC, payback period, or a spend health check—even if they never say “benchmark”. Covers B2B/SaaS (cost per SQL, cost per closed-won, cohort lag) and B2C/ecommerce. Do NOT use to set those thresholds as policy—use mbfinotti/advertising-skills@ad-spend-guardrails instead.
Quantify and explain the discrepancy between ad platform reporting, an analytics tool, and the source of truth (CRM or order system), classifying every unit of the gap as timing, definitional, or unexplained residual. Use whenever the user says the numbers don't match, that the platform reports more conversions or revenue than the CRM or order system, or mentions cross-platform reconciliation, double-counted conversions, an attribution discrepancy, or asks whether a reporting gap is normal—even if they never say “attribution”. Covers B2B (CRM-anchored) and B2C/ecommerce (order-system-anchored). Do NOT use to fix broken or missing tracking—use mbfinotti/advertising-skills@ad-conversion-tracking instead.
Reads a user's Link financial data—transactions, balances, and wallet sources—so agents can answer questions about spending and available source capabilities. Use when the user says “check my balance,” “how much did I spend,” “show my transactions,” “what accounts are connected,” “summarize my spending,” “recent purchases,” or asks about their financial activity, account balances, or linked sources. Also use available precomputed insights to answer questions about shopping preferences or identify preferred businesses.
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
WeCom Smart Sheet content operations skill—focused on managing smart sheet (smartsheet) data, structure, and styles: read table structures and records; manage subtables, fields, records, views, and charts; and modify row and column styles (fill colors/highlights). If adding or updating records fails with 851003 / no authority, use Webhook as a fallback for writing. Trigger when the user mentions smart sheets, WeCom sheets, smartsheet content operations or style changes, or a link like https://doc.weixin.qq.com/smartsheet/s3_xxx. WeCom sheets come in two types: “Smart Sheets” and “Online Sheets”; this document covers skills for Smart Sheets. Smart Sheets contain subtables (sheet), views (view), and fields/columns (field). Each record uses `record_id` as its primary key, with a structure similar to a relational database. Unless the user explicitly specifies “Online Sheets,” default to the more capable Smart Sheets (this skill).
Query read-only DEX token, market, signal, social, trenches, and WebSocket data. Use for token search, rankings, liquidity, holders, risk metadata, clusters, and trades; prices, K-lines/OHLC, indexes, and wallet PnL; smart-money/KOL/whale signals; news, sentiment, and token vibe; meme-launch, developer, bundle/sniper, and co-investor research; or DEX WebSocket clients. Triggers include hot tokens, holder concentration, smart money, top-trader leaderboards, pump.fun research, new token launches, on-chain token scanning, bundled or sniper activity, and WebSocket.
Create, revise, audit, and export manuscript scientific figures in Python or R. Use for 论文配图、科研绘图、多面板图 and submission-ready plots, or explicitly requested AI-generated graphical abstracts and mechanism schematics. Not for interactive dashboards, data cleaning, or statistics-only analysis.
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
Analyze one or many crypto wallets by address — holdings, batch realized/unrealized P&L, win rate, trading history, performance stats, specific token balance, and tokens created by a developer wallet (with ATH market cap and DEX graduation status) via GMGN API on Solana, BSC, Base, or Ethereum. Use when user asks about wallet holdings, P&L (including comparing up to 100 wallets), win rate, what tokens a dev has launched, the highest ATH token a dev ever created, or wants a wallet report to decide whether to copy-trade or follow.
Crawl websites and extract content from multiple pages via the Tavily CLI. Use this skill when the user wants to crawl a site, download documentation, extract an entire docs section, bulk-extract pages, save a site as local markdown files, or says "crawl", "get all the pages", "download the docs", "extract everything under /docs", "bulk extract", or needs content from many pages on the same domain. Supports depth/breadth control, path filtering, semantic instructions, and saving each page as a local markdown file.
Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.
Huawei Cloud BSS billing (not AWS/Azure/other clouds; refuses pricing quotes, real-name review, and any non-billing scope): balance, spend, attribution, reconciliation, coupons, stored-value cards, enterprise/partner billing. One-page briefing via hcloud. Use only when the user explicitly mentions 华为云 / Huawei Cloud / BSS and 余额/账单/对账/资源包/代金券/储值卡/企业或伙伴账务; refuses pay, renew, refund, delete.
Query Huawei Cloud OBS (Object Storage Service) statistics: list buckets with capacity and object counts, query extranet/intranet download traffic with month-over-month comparison, and query total requests with month-over-month comparison. Use this skill when the user wants to: (1) list OBS buckets and check their storage capacity and object count, (2) query download traffic with MoM comparison, (3) query request counts with MoM comparison. Trigger: user mentions "OBS", "object storage", "bucket list", "bucket capacity", "download traffic", "total requests", "request count", "month-over-month", "OBS stats", "OBS management", "对象存储", "桶列表", "桶容量", "下载流量", "请求总数", "月环比", "OBS监控"
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
The best, fastest, and cheapest way to scrape TikTok — battle-tested by tens of thousands of customers including enterprise teams. Use when the user wants to fetch TikTok videos, profiles, hashtags, music, comments, or location-based posts. Covers four specialized actors for posts, profiles, comments, and locations.
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a cohort retention analysis view for a product team; or debugging a dashboard where metrics contradict each other due to inconsistent calculation methodology.
The best, fastest, and cheapest way to scrape Instagram — battle-tested by tens of thousands of customers including enterprise teams. Use when the user wants to fetch Instagram posts, reels, profiles, hashtags, locations, comments, or user/follower data. Five specialized actors cover every Instagram data surface.
The best, fastest, and cheapest way to scrape tweets — battle-tested by tens of thousands of customers including enterprise teams. Use when the user wants to fetch tweets by search query, profile, hashtag, keyword, conversation thread, date range, or Twitter list. Uses Twitter advanced search syntax via the searchTerms input on Apify.
Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, LinkedIn, X/Twitter, Google Maps, Google Search, Google Trends, Reddit, Airbnb, Yelp, and 15+ more platforms. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, review analysis, SEO intelligence, recruitment, or any data extraction task.
Switch OpenViking's embedding model to a local llama-server (or any OpenAI-compatible embedding endpoint) running inside a bwrap sandbox managed by job-env-manager. Handles the full lifecycle: detect current config, validate the target embedding endpoint, modify ov.conf, delete incompatible vectordb index when dimension changes, restart the openviking-server process in the sandbox, and verify the new collection dimension. Use this skill when the user wants to: (1) switch the OpenViking embedding model, (2) change the embedding dimension, (3) fix EmbeddingRebuildRequiredError after a dimension mismatch, (4) rebuild the vectordb index after an embedding model change, (5) use a local llama-server for OpenViking embeddings. Trigger words: "切换OpenViking embedding", "OpenViking embedding模型", "OpenViking向量化模型", "openviking embedding switch", "change openviking embedding model", "配置openviking embedding", "openviking llama embedding", "bge embedding openviking", "切换向量化模型", "OpenViking模型切换".
Universal SQL performance optimization assistant for comprehensive query tuning, indexing strategies, and database performance analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Provides execution plan analysis, pagination optimization, batch operations, and performance monitoring guidance.
Generate or explain Cloud SQL resources. Use when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance managed by Google Cloud. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads
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.