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Found 113 Skills
Build and validate cron expressions from natural language. Convert between human-readable schedules and cron syntax with next run preview.
Convert natural language questions into SQL queries. Activates when users ask data questions in plain English like "show me users who signed up last week" or "find orders over $100".
This Skill supports screening qualified stocks based on stock selection criteria (such as market indicators, financial indicators, etc.); it allows querying stocks, listed companies within specified industries/sectors, as well as component stocks of sector indices; it also supports related tasks such as stock, listed company, and sector/index recommendations, avoiding the use of outdated information by large models during stock selection.
Resolves experiment references from natural language to concrete experiment IDs. Handles name lookups, fuzzy descriptions ('the signup experiment', 'my latest experiment'), status filtering, and disambiguation when multiple experiments match. TRIGGER when: user refers to an experiment by name, description, or relative reference ('latest', 'most recent', 'the one I created yesterday') and you don't already have the experiment ID. DO NOT TRIGGER when: user provides an experiment ID directly, or you already resolved the experiment earlier in the conversation.
NeoData Financial Search — A universal natural language financial data search service. Query full-category financial data such as stocks, funds, indices, sectors, macroeconomics, foreign exchange, and commodities using natural language, covering market quotes, financial statements, capital flows, research report ratings, event announcements, etc. It supports two recall modes: structured API data and financial articles, providing instant answers. Use when the user asks about financial data, stock quotes, financial statements, earnings reports, market data, fund info, macroeconomics, forex, commodities, or needs to query the NeoData API.
Search ClinicalTrials.gov with natural language queries. Find clinical trials, enrollment, and outcomes using Valyu semantic search.
Convert natural language queries to SQL. Use for database queries, data analysis, and reporting.
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
AI-powered E2E testing for any app. Test 8 platforms with natural language — no test code needed.
Interactive debugging via DAP-MCP for multiple languages with natural language commands
Resolves a PostHog experiment reference from natural language to a concrete experiment ID by browsing `experiment-list` (not feature-flag tools), with disambiguation when multiple experiments match. Use when the user names or quotes an experiment ("split test demo", "the File engagement boost experiment", "onboarding retention test", "landing page hero experiment", "pricing experiment"), describes it loosely ("the signup experiment", "my pricing test", "the one with the new checkout"), uses a relative reference ("latest", "most recent", "the one I created yesterday"), filters by status (running, draft, stopped, archived), or otherwise refers to an experiment by anything other than its concrete ID.
Stock Screener - Screen all market stocks based on conditions to identify targets that match your strategy