Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Set up Prisma ORM in an application, connect its database, or troubleshoot the connection of an existing Prisma 6, 7, or 8 app. Defaults new applications to Prisma ORM 8 and loads the package-owned prisma-8 skill; keeps existing Prisma 6/7 apps on their version with provider references for PostgreSQL, MySQL, SQLite, SQL Server, CockroachDB, MongoDB, and Prisma Postgres.
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
When the user wants to find, qualify, and build a list of prospects to reach out to, across B2B SaaS, general B2B, or local small businesses. Also use when the user mentions "prospecting," "build a prospect list," "find leads," "lead list," "outbound list," "target account list," "ICP-fit accounts," "find local businesses," "find my first customers," "design partners," "signal-based outbound," "buying signals," "intent data," "job change alerts," "waterfall enrichment," "Clay table," "lookalike accounts," "catch-all emails," "account tiering," or "research this account before I reach out." Always verify emails before they reach a sequence, and never scrape LinkedIn. Define audiences in Sales Navigator and pull contacts from licensed data. Covers list building, signals, enrichment, verification, and account research. For the outreach itself (copy, sending setup, LinkedIn, cadences, replies), see cold-email. For researching competitors, see competitor-profiling.
Integrate Firecrawl `/scrape` into product code for single-page extraction. Use when an app already has a URL and needs markdown, HTML, links, screenshots, metadata, or structured page output. Prefer this skill over broader crawl patterns when the feature is page-level.
Integrate Firecrawl `/search` into product code and agent workflows. Use when an app needs discovery before extraction, when the feature starts with a query instead of a URL, or when the system should search the web and optionally hydrate result content.
Get Firecrawl credentials and SDK setup into a project. Use when an application needs `FIRECRAWL_API_KEY`, when an agent should add Firecrawl to `.env`, when the user wants to authenticate Firecrawl for app code, or when choosing the first SDK and docs for a new Firecrawl integration. This skill includes its own browser auth flow, so it does not depend on the website onboarding skill.
Integrate Firecrawl `/interact` into product code for dynamic pages and browser actions after scraping. Use when a feature needs clicks, form fills, pagination, authentication-aware flows, or other multi-step interactions that plain `/scrape` cannot complete.
Use this skill whenever someone needs a backend, or a task touches InsForge backend or cloud infrastructure through the InsForge CLI: projects, SQL, migrations, RLS policies, functions, storage, backups, deployments, compute, secrets, config, schedules, logs, diagnostics, advisor scans and suppressions, import/export, AI/OpenRouter setup and usage overview, Stripe/Razorpay payments, Apify web scraping / data sources, PostHog product analytics, backend branches, organization membership (invite, leave, delete), agent memory (remember/recall project facts and decisions), reporting InsForge-side bugs or doc discrepancies (feedback), or CLI docs. For app code with InsForge or @insforge/sdk, use the insforge app-integration skill instead.
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
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset existing code.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "attribution," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For A/B test measurement, see ab-test-setup.
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
Use when the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions “A/B test,” “split test,” “experiment,” “test this change,” “variant copy,” “multivariate test,” “hypothesis,” “should I test this,” “which version is better,” “test two versions,” “statistical significance,” “how long should I run this test,” “growth experiments,” “experiment velocity,” “experiment backlog,” “ICE score,” “experimentation program,” or “experiment playbook.” Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.
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
Use Agent Pulse to inspect local AI agent activity across Hermes, Claude Code, Codex, DeepSeek, OpenClaw, Copilot, Aider, Qwen, OpenCode, Goose, Cursor, Antigravity, and Amp logs. Use when the user asks about AI agent sessions, tokens, tool/search calls, model usage, estimated cost, budgets, forecasts, health checks, reports, setup diagnosis, web/API/metrics exports, or MCP integration.
Entry point for discovering and installing Wind financial capabilities. Use for financial data, market quotes, market analysis, today's market overview, market themes, sector rotation, capital flows, valuation, stock selection, review, position sizing, trading plans, backtesting, and related tasks. Must first read the catalog to determine required data skills/workflow skills; if any are missing, show installation options and, after user confirmation, have the AI install them directly. Do not provide only commands or substitute generic analysis.
End-to-end analytics instrumentation workflow for a PR, branch, file, directory, or feature. Reads the code, discovers what events should be tracked, and produces a concrete instrumentation plan — all in one shot. Use this skill whenever a user wants to add analytics to a PR, asks "instrument this PR", "add tracking to this branch", "what analytics does this file need", "instrument the checkout flow", "run the full instrumentation workflow", or any request that implies going from code changes to a tracking plan. Also trigger when the user gives you a PR link, branch name, file path, or feature description and mentions analytics, events, or instrumentation. This is the main entry point for the analytics workflow — prefer it over calling the individual steps (diff-intake, discover-event-surfaces, instrument-events) separately
Reads a PR or branch diff and produces a structured YAML change brief for downstream analytics instrumentation skills. Use this as the first step whenever a user shares a PR link, branch comparison, or raw diff and wants to understand what changed, what needs tracking, or how to instrument a feature. Trigger on phrases like "review this PR", "what changed in this branch", "help me instrument this diff", "check analytics coverage for this change", or any request to start the analytics review workflow
Discovers how analytics tracking calls are actually written in this codebase — the concrete SDK calls, function signatures, and import patterns used to send events. Use this skill whenever you need to understand the existing analytics instrumentation patterns before adding new tracking, when someone asks "how do we track events here?", "show me the analytics setup", "what's the analytics pattern in this codebase?", or any time the instrument-events or discover-event-surfaces skills are about to run and you need to know the correct coding style to follow. Outputs a deduplicated list of patterns with generalized examples and the file paths where each pattern appears, plus the dominant event and property naming conventions inferred from those call sites. Always use this skill before writing any analytics instrumentation code
Given a change_brief YAML (output from diff-intake), generates an exhaustive list of candidate analytics events to instrument. Takes the perspective of an engineer with a PM mindset — surfaces everything worth considering so a PM can decide what actually matters. Use this as step 2 of the analytics instrumentation workflow, immediately after diff-intake produces a change_brief. Trigger whenever a user has a change_brief YAML and wants to know what analytics events to add, or asks "what should I track for this PR", "what events should I instrument", "generate event candidates", or any request to surface analytics coverage gaps for a code change
Given event_candidates YAML (output from discover-event-surfaces), generates a concrete instrumentation plan for priority-3 (critical) events. Acts as a Software Architect: discovers existing analytics patterns in the codebase, reads the hinted files to determine what variables are in scope, designs minimal chart-useful properties, and identifies the exact insertion point for each tracking call. Outputs a structured JSON trackingPlan. Use this as step 3 of the analytics instrumentation workflow, after discover-event-surfaces. Trigger whenever a user has event_candidates and wants to generate tracking code, asks "instrument these events", "generate tracking plan", "add analytics for these events", "where should I put the tracking calls", or any request to turn event candidates into concrete implementation guidance
Source of truth for event taxonomy generation, data auditing, and governance best practices in Amplitude. Use when an agent needs to create, validate, audit, score, or recommend improvements to event tracking plans, naming conventions, property standards, data quality, or deprecation workflows, including checking a plan against the customer's own standard (Data → Settings naming conventions and AI Context). Covers naming rules, property standards, scoring frameworks, safe metadata operations, deprecation procedures, and AI readiness guidance
Comprehensive guide for Go database access — parameterized queries, struct scanning, nullable columns, transactions, isolation levels, SELECT FOR UPDATE, connection pools, batch processing, context propagation, and migration tooling. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite; for database testing; or for questions about database/sql, sqlx, or pgx. Does NOT generate database schemas or migration SQL
When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions “attribution,” “attribution model,” “first-touch vs last-touch,” “multi-touch,” “which channel drives revenue,” “what's my real CAC,” “my dashboards disagree,” “Google/Meta says X but GA says Y,” “media mix model,” “MMM,” “incrementality,” “geo lift,” “holdout test,” “how did you hear about us,” “self-reported attribution,” “dark social,” or wants to instrument attribution themselves — “stitch my bookings to their source,” “SavvyCal/Calendly attribution,” “close the identify gap,” “track conversions on a third-party domain,” “first-party / self-hosted attribution.” For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI and conversion tracking, see conversion-tracking. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo
Check the health of published EAS Update: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel. Use when the user asks how an update is performing, whether a rollout is healthy, how many users are on the embedded build vs OTA, or wants to gate CI on update health.
Comprehensive guide for Firestore Standard Edition, including provisioning, security rules, and SDK usage. Use this skill when the user needs help setting up Firestore, writing security rules, or using the Firestore SDK in their application.
Deprecated alias. This skill was renamed to `deepline-scoring`. Use when a user or script still invokes `/niche-signal-discovery`: immediately load and follow the `deepline-scoring` skill instead. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals.
GTM leads, enrichment, outreach, Plays, developer signals/Reo.dev. Providers: adyntel,affinity,ai_ark,akta,allegrow,amplemarket,apify,attention,attio,aviato,bettercontact,bigquery,bluesky,bounceban,brave,browserbase,builtwith,buyercaddy,cbinsights,clay,clickhouse,cloudflare,contactout,contextdev,crunchbase,crustdata-v3,customer_db,databricks,dataforseo,datagma,dealroom,deepline_ip_to_company,deepline_native,deeplineagent,deepseek,discolike,dnb,edges,emailbison,emailguard,enformion,enigma,exa,findymail,firecrawl,fireflies,firmable,forager,fullenrich,fundable,generic_http,getleads,gong,google_ads_audiences,google_workspace,govfiles,grain,hackernews,harvestapi,heyreach,hubspot,hunter,icypeas,instantly,intercom,ipqs,kaspr,kernel,kickbox,leadmagic,lemlist,limadata,linkedin_ads_audiences,luma,lusha,meta_audiences,millionverifier,nooks,notion,openmart,opensosdata,openwebninja,outreach,parallel,particle,peopledatalabs,pitchbook,podscan,polymarket,postgres,predictleads,prospeo,quickenrich,rapidapi,redshift,…
Comprehensive guide for Firestore Enterprise Native mode, including provisioning, data model, security rules, and SDK usage. Use this skill when the user needs help setting up Firestore Enterprise with Native mode, writing security rules, or using the Firestore SDK in their application.
Choose and create the right Neon branch type for testing and development. Use when users ask about Neon branching, migration testing with real data, isolated test environments, schema-only branch workflows for sensitive data, resetting a branch from its parent, branch expiration and CI/CD branch lifecycles, or branch creation via Neon CLI or Neon MCP. Triggers include "Neon branch", "test migrations safely", "branch production data", "schema-only branch", "reset branch", "branch per PR" and "sensitive data testing".
Research markets and companies with Firecrawl using current, cited figures: market size, growth, trends, financials and earnings. Use when the user asks for market research, an industry overview or a comparison of companies by the numbers, since these figures change faster than training data
Read metrics from web dashboards with Firecrawl browser: public stats, status and transparency pages, as well as analytics tools the user is signed in to. Use when the user wants numbers, KPIs or charts from a dashboard turned into a report, summary or date-range table, even without an explicit request to scrape
Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.
Export documentation portals, help centers and API reference sites into local markdown or JSON with Firecrawl browser, including pages that render with JavaScript, hide content behind collapsible navigation or paginate. Use when the user wants a docs site, help center or support knowledge base saved as files, even without an explicit request to scrape
Extract structured company lists with Firecrawl from any page or site listing companies: accelerator batches, VC portfolios, member and sponsor lists, marketplaces and app directories, YC, Crunchbase, Product Hunt or G2. Use when the user wants those companies as a CSV, JSON, CRM-ready list or research table, even without an explicit request to scrape
Design and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.
Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.
Diagnose and fix excessive Postgres egress (network data transfer) in a codebase. Use when a user mentions high database bills, unexpected data transfer costs, network transfer charges, egress spikes, “why is my Neon bill so high”, “database costs jumped”, SELECT * optimization, query overfetching, reducing Neon costs, optimizing database usage, or wants to reduce data sent from their database to their application. Also use when reviewing query patterns for cost efficiency, even if the user doesn't explicitly mention egress or data transfer.
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.