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Found 9,262 Skills
This skill must be used when initializing, maintaining, and executing by-harness workflows. It applies to scenarios where users mention by-harness, harness, initialization, continuous task decomposition, executing feat, plan/build/qa/fix, session_close, automatic resumption, runtime upgrade, or need to issue Java Gate, Distributed Java Gate, and three-tier frontend specifications to constrain model coding. This skill generates independent closed-loop scaffolding, sharded task storage, session closure tools, runtime upgrade tools, and issues Java hard rule gates, distributed Java coding contracts, three-tier frontend specifications, and BYAI HTML visual references; feature_list is only used as a legacy compatibility mirror.
Build, debug, or plan work with The Prompting Company through its API, MCP Server, CLI, or SDK entrypoints. Use when the user needs public routes, OpenAPI schema guidance, TypeScript SDK integration, CLI workflows, MCP setup, content APIs, app publishing APIs, public markdown access, simulations, visibility analytics, authentication, or API key scopes.
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
让 agent zoom out，并给出更广的 context 或更高层 perspective。Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
Summarizes WeChat group chat highlights into a structured digest using the local wx-cli binary (https://github.com/jackwener/wx-cli). Generates a normal digest by default; a roast (毒舌) version is opt-in. Maintains per-group history (history.json + history-digests.jsonl) and per-user profiles across runs, with privacy guardrails baked in. Use when the user asks to "总结群聊", "群聊精华", "群聊摘要", "summarize group chat", "group chat digest", mentions a WeChat group name with a time range, says "帮我看看 XX 群最近聊了什么", "XX 群有什么值得看的", or asks to "回溯画像" / "初始化画像" / "backfill profiles". Adds the roast version when the user says "毒舌版", "roast 版", "再来个毒舌的", or similar.
Review orchestrator: assess your application and recommend the right combination of design, security, privacy, compliance, resilience, performance, SEO, and GEO reviews.
MCP Server Construction Methodology — Systematically build production-grade MCP tools to enable AI assistants to connect to external capabilities
Shopping price comparison using Bright Data's web scraping infrastructure. Finds where a product is sold, for how much, and whether it's in stock — across Amazon, Walmart, eBay, Best Buy, Google Shopping, and any retailer URL — then ranks the offers into a single buy-recommendation table. Use this skill when the user wants to compare prices, find the cheapest place to buy something, do a price check, see "how much does X cost on Amazon vs Walmart", track an item's price, or decide where to buy a product. Handles product names, ASINs, and direct URLs, and is region-aware (country affects price, availability, and which retailers apply). This is consumer purchase-decision research — for analyzing a competitor's pricing *strategy*, use competitive-intel instead.
Build the optimal 250-byte Amazon backend search term string. Collects every candidate keyword, removes anything already indexed in the title and bullets, strips duplicates and Amazon-prohibited terms, prioritizes by search value, and packs the field to the byte limit. Use when a user asks about backend keywords, search terms, the hidden keyword field, generic keywords, "Search Terms" in the listing back end, or wants to fix wasted backend space. Trigger phrases: "backend keywords", "search terms field", "hidden keywords", "250 bytes", "generic keywords". Works with zero tools. the user pastes the current title, bullets, and any keyword list.
Builds a week-by-week Q4 restock plan from August through November with FBA inbound delay buffers, peak velocity multipliers, and FBM fallback triggers. Q4 is 30-40% of annual revenue and FBA receiving takes 2-3 weeks during peak. Standard restock math stocks out at the worst possible moment. Use when a user asks about Q4 planning, Black Friday inventory, or Prime Big Deal Days restock. Trigger phrases: "Q4 restock plan", "Black Friday inventory", "Prime Big Deal Days restock", "Christmas Amazon inventory", "FBM fallback for FBA". Works with zero tools.
Break a feature spec into intentional waves and bite-sized tasks grouped by dependency. Use after a spec is written to prepare for easy-to-track implementation.