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Found 9,290 Skills
Build unified cross-chain USDC balance management with Circle Unified Balance Kit SDK via App Kit (`@circle-fin/app-kit`) or standalone (`@circle-fin/unified-balance-kit`). Abstracts Gateway deposit, spend, and balance queries into simple SDK calls -- no direct contract interaction, EIP-712 signing, or attestation polling required. App Kit is recommended for extensibility across swap, bridge, send, and unified balance. Standalone Unified Balance Kit provides the same API surface in a lighter package. Neither requires a kit key for unified balance operations. Supports EVM chains and Solana via adapter packages. Use when: depositing USDC into a unified balance, spending from a unified balance to any supported chain, checking unified balance across chains, setting up Unified Balance Kit adapters (Viem, Solana, Circle Wallets), managing delegates for account separation, or building chain-abstracted USDC payment flows. Triggers on: unified balance, Unified Balance Kit, UnifiedBalanceKit, @circle-fin/unified-balance-kit, deposit USDC unified, spend unified balance, getBalances, cross-chain USDC SDK, chain abstraction SDK, adapter-viem, adapter-solana, depositFor, addDelegate, unified USDC.
Buffett-style single-stock moat diagnostic — "Would Buffett buy this stock?" Five dimensions: business & moat / financial health / management & capital allocation / valuation & margin of safety / long-term visibility. Data from Longbridge CLI first, MCP fallback, WebSearch only for gaps. Runs cross-statement reconciliation (勾稽校验) BEFORE scoring; data-source appendix closes with a one-line reconciliation summary. Output: star-rated radar card, dimension detail, Buffett-voice narrative, mandatory holding-period education block. Triggers: "巴菲特", "护城河", "巴菲特会买吗", "价值投资", "好生意", "宽护城河", "定价权", "诊股", "巴菲特诊股", "巴菲特视角", "长期持有", "護城河", "巴菲特會買嗎", "價值投資", "寬護城河", "定價權", "診股", "巴菲特診股", "巴菲特視角", "長期持有", "Buffett", "Warren Buffett", "moat", "economic moat", "wide moat", "pricing power", "value investing", "owner earnings", "would Buffett buy", "Berkshire-style", "quality compounder".
ETF analysis framework via Longbridge — product screening (AUM/expense ratio/index), tracking error, liquidity (bid-ask spread/volume), premium/discount (NAV vs market price), and A-share ETF allocation insights. Triggers: "ETF分析", "ETF选择", "ETF跟踪误差", "ETF溢价", "ETF流动性", "ETF费率", "ETF规模", "宽基ETF", "行业ETF", "指数基金", "ETF分析", "ETF選擇", "ETF追蹤誤差", "ETF溢價", "ETF流動性", "ETF費率", "ETF規模", "指數基金", "ETF analysis", "ETF selection", "tracking error", "ETF premium discount", "ETF liquidity", "expense ratio", "broad market ETF", "sector ETF", "index fund".
Options strategy framework via Longbridge — covered call, protective put, straddle, strangle, bull spread, bear spread selection and comparison based on market view and IV level. Triggers: "期权策略", "备兑开仓", "保护性看跌", "跨式策略", "宽跨式", "牛市价差", "熊市价差", "期权组合", "卖出期权", "买入期权", "期權策略", "備兌開倉", "保護性看跌", "跨式策略", "牛市價差", "熊市價差", "期權組合", "options strategy", "covered call", "protective put", "straddle", "strangle", "bull spread", "bear spread", "options combination".
Run any model on RunComfy from the command line. The `runcomfy` CLI is one binary, one auth, hundreds of model endpoints — image generation, image edit, video generation, image-to-video, lip-sync, face swap, video edit, inpainting, outpainting, extend, ControlNet, relight, upscale, LoRA training and more. Submit a request, poll for status, download the output. This skill teaches the agent how to install, authenticate, discover model schemas, invoke models, stream / poll / no-wait, script in JSON output mode, and handle errors. Triggers on "runcomfy cli", "install runcomfy", "runcomfy login", "runcomfy run", "runcomfy whoami", "runcomfy api", or any explicit ask to call a RunComfy model from a script or terminal. Sibling skills (ai-image-generation, ai-video-generation, image-edit, video-edit, face-swap, lipsync, image-to-video, image-inpainting, image-outpainting, video-extend, controlnet-pose, relight) all dispatch through this CLI.
Deep formal test smell audit based on academic research taxonomy (testsmells.org). Detects 19 categorized smell types — conditional logic, mystery guests, sensitive equality, eager tests, and more — with calibrated severity and research-backed remediation. Use for comprehensive test suite health assessments. For a quick pragmatic review, use test-anti-patterns instead. DO NOT USE FOR: writing new tests (use writing-mstest-tests), evaluating assertion quality specifically (use assertion-quality), or finding test duplication and boilerplate (use exp-test-maintainability).
Reference Documentation for Jiekou AI Model Services, covering LLM API (OpenAI-compatible), Image/Video/Audio APIs, integration solutions, authentication/billing/pricing/rate limiting, and troubleshooting. Suitable for questions like "How to integrate Jiekou AI into tools such as OpenAI SDK / LangChain?" and issues like Jiekou AI request failures.
Generate podcast clip visualization video prompts for Seedance 2.0 on Higgsfield. Use for podcast clip videos, audio-to-visual content, audiogram alternatives, podcast highlight reels, interview clip visuals, or any video that transforms audio content into engaging visual format. Triggers on podcast, audio clip, audiogram, interview clip, sound bite, audio visual, podcast video, episode highlight, podcast clip.
Java coding standards for Spring Boot and Quarkus services: naming, immutability, Optional usage, streams, exceptions, generics, CDI, reactive patterns, and project layout. Automatically applies framework-specific conventions.
Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.
Publish Rust binaries to npm using the optionalDependencies platform package pattern. Covers the full publish pipeline, version sync, workspace:* protocol, and platform package architecture. Use when: (1) publishing Rust binaries to npm, (2) setting up the platform package pattern (main + per-OS packages), (3) debugging publish failures, (4) managing version sync across pnpm + Cargo workspaces, (5) working with workspace:* protocol. Triggers on "publish", "platform packages", "optionalDependencies", "bin.js", "version sync", "workspace protocol", "npm tag", or "prepare-publish".
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).