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Found 770 Skills
AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, and personalized outreach. Use when the user wants to find, qualify, and reach high-value contacts.
Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills in the right order at the right depth. Auto-activates on any coding task. Centralizes the decision logic for which skills to use, how deep each goes, and how artifacts pass between them. Handles three pipeline variants: standard (plan-interview, intent-framed-agent, context-surfing, simplify-and-harden, self-improvement), team-based (agent-teams-simplify-and-harden), and CI (simplify-and-harden-ci, self-improvement-ci). Use this skill whenever starting any coding work — it determines the appropriate pipeline depth and variant automatically. Does not replace individual skills; dispatches to them.
AI image generation with OpenAI, Google, OpenRouter, DashScope, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Set up and connect a Shopify store from your AI assistant. Use when the user wants to: set up my Shopify store, connect my store, install Shopify plugin, get started with Shopify, manage my store, add products to my store, merchant onboarding, start selling online, Shopify setup help, create my first store, how do I set up an online store, shopify.com/SKILL.md, import products, migrate from Square, migrate from WooCommerce, migrate from Etsy, migrate from Amazon, migrate from eBay, migrate from Wix, import from Google Merchant Center, migrate from Clover, migrate from Lightspeed, move products to Shopify, import catalog, replatform to Shopify. This is for store owners — not developers.
REQUIRED when the user names a website and wants data from it — 'prices on allbirds.com', 'flights on kayak', 'listings from zillow'. Replaces scraping with clean JSON endpoints. For adding new sites, use hermai-contribute.
Comprehensive reference for LINE Messaging API — webhook setup, message sending, Flex Message design, Rich Menu management, audience targeting, insights, coupons, and channel access tokens. This skill should be used when the user asks to "build a LINE Bot", "set up a webhook", "send a push message", "design a Flex Message", "create a Rich Menu", "manage audience targeting", "get messaging insights", "create a coupon campaign", "debug webhook signature verification", or mentions LINE Messaging API, LINE OA chatbot, reply/push/multicast/narrowcast/broadcast, Flex Message JSON, Rich Menu, group chat bot, channel access token, or URL schemes. Always use this skill whenever the user mentions LINE bots, chatbots, LINE OA, or any messaging-related LINE integration, even if they don't explicitly say "Messaging API".
Audit all Kafka topic configurations against production best practices using the Lenses MCP server. Checks replication factor, retention, partitions, compaction, naming conventions, orphaned topics and missing metadata. Use when user says "audit my topics", "check topic configs", "topic health check" or asks about retention, replication or partition settings. Do NOT use for creating, deleting or modifying topics.
Universal AI image generation supporting OpenAI DALL·E / gpt-image, Google Gemini Image / Imagen, Replicate (Flux / SDXL / any model), Stability AI, FAL, Ark (Seedream 4.5), Bailian (qwen-image / wanx), and SiliconFlow. Use this skill whenever the user asks to generate, create, draw, illustrate, render, or synthesize images from text prompts or reference images. Typical phrases include "draw a ...", "generate an image of ...", "画一张 ...", "给我来张图", "make a poster of ...", "create an illustration ...", or any mention of image-generation model families like DALL·E, gpt-image, Flux, SDXL, Seedream, Imagen, Gemini image, Kolors, or Wanx. Always use this skill even if the user does not name a specific model — pick a provider based on their EXTEND.md defaults or available API keys in the environment. Do NOT use this skill when the user explicitly mentions 即梦 / Dreamina / Jimeng — those go to happy-dreamina instead.
Use this skill when working with Unreal Engine async operations, threading, parallel execution, or concurrency. Also use when the user mentions 'FRunnable', 'FAsyncTask', 'TaskGraph', 'UE::Tasks', 'ParallelFor', 'TFuture', 'TPromise', 'Async()', 'thread safety', 'FCriticalSection', 'FRWLock', 'background thread', 'game thread dispatch', or 'thread pool'. For networking async (RPCs, replication), see ue-networking-replication. For asset streaming, see ue-data-assets-tables.
Analyze a batch of functional specs from a ***plain spec file to determine which pairs conflict. Replaces the pair-by-pair `analyze-2-func-specs` flow when a caller wants to check many specs at once (e.g. a new spec against every existing spec, or a freshly inserted batch against itself).
Guides technical support engineering—customer ticket investigation, reproduction, log and API analysis, root-cause isolation, workaround communication, engineering escalation with evidence, and knowledge-base fixes for product bugs and integration issues. Use when debugging a customer-reported issue, writing a repro for engineering, analyzing API errors, drafting technical replies, or improving support runbooks—not for CS program design, renewals, or billing ops (customer-ops-specialist), production incident command (incident-management-engineer), building product features (fullstack-software-engineer), or company-wide crisis statements and launch announcements (communication-lead), or exec/VIP and community escalation program design (community-executive-escalations-program-manager). Product how-to, macros, and ticket triage without deep debugging: product-support-specialist.