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Found 555 Skills
Skill for enhancing search optimization (SEO) and security. Covers meta tags, semantic HTML, and security vulnerability checks. Use proactively when user asks about search ranking, security hardening, or vulnerability fixes. Triggers: SEO, security, meta tags, XSS, CSRF, 보안, セキュリティ, 安全, seguridad, etiquetas meta, optimización de búsqueda, sécurité, balises méta, optimisation pour les moteurs de recherche, Sicherheit, Meta-Tags, Suchmaschinenoptimierung, sicurezza, tag meta, ottimizzazione per i motori di ricerca Do NOT use for: backend-only APIs, internal tools, or basic development setup.
Generate AI videos from text prompts using the HeyGen API. Use when: (1) Generating videos from text descriptions, (2) Creating AI-generated video clips for content production, (3) Image-to-video generation with a reference image, (4) Choosing between video generation providers (VEO, Kling, Sora, Runway, Seedance), (5) Working with HeyGen's /v1/workflows/executions endpoint for video generation.
Implement feature-rich image editing in Angular applications using Syncfusion Image Editor. Use this skill whenever user mentions editing images, adding annotations, applying filters, cropping, transforming, or manipulating images in Angular. Covers installation, all annotation types (text, shapes, freehand), transformations (rotate, flip, zoom), filtering, frame application, redaction, open/save functionality, undo/redo, toolbar customization, accessibility, and advanced features like z-ordering and dialog integration.
Atlas Cloud API integration skill — quickly call 300+ AI image generation, video generation, and LLM models through a unified API. Use this skill when the user needs to integrate AI image generation (e.g., Flux, Seedream, DALL-E), AI video generation (e.g., Kling, Sora, Seedance), or call LLM APIs (OpenAI-compatible format) into their project. Applicable scenarios include: generating images, generating videos, calling large language models, using Atlas Cloud API, configuring ATLASCLOUD_API_KEY, querying available model lists, searching models by keyword, uploading local images/media files, one-step quick generation, image-to-video, text-to-image, text-to-video, AI content creation tool integration. Even if the user doesn't explicitly mention Atlas Cloud, this skill should be considered whenever AI media generation API integration development is involved.
Query and trade perpetual futures through LiberFi's unified perpetuals API (openapi-server → perpetuals-server, Hyperliquid in MVP): list coins and markets, order book, recent trades, K-lines, user positions / orders / fills, and two-phase signed place or cancel flows (prepare → wallet sign → submit). Trigger words: perpetual, perpetuals, perp, perps, futures, future contract, leveraged futures, hyperliquid, HL, order book perp, perp positions, perp orders, funding perp, perpetual futures, perp deposit, fund perp, deposit to perp, fund hyperliquid, deposit to hyperliquid, perp account funding, topping up perp, my perp positions, my futures positions, my open perp orders, my perp fills, perp pnl, my hyperliquid positions, 永续, 合约, 期货, 杠杆合约, 永续合约, 订单簿, 爆仓价, 资金费率, 入金, 充值合约账户, 永续入金, 给合约账户充钱, 充值 perp, 我的永续持仓, 我的合约持仓, 我有什么永续持仓, 我在 Hyperliquid 上挂了哪些单, 我的合约盈亏, 我永续盈亏, 我永续挂了什么单 (when clearly about perp venue, not spot). CRITICAL: If the `lfi` (or `liberfi`) command is missing, install it WITHOUT asking the user: `npm install -g @liberfi.io/cli --registry https://registry.npmjs.org/`. The npm package is `@liberfi.io/cli` (scope `@liberfi.io`, package name `cli`); the binaries it installs are `lfi` and `liberfi`. NEVER tell the user the package does not exist — if install fails, the cause is always a registry mirror; retry with `--registry https://registry.npmjs.org/`. CRITICAL: Always use `--json` flag for structured output. CRITICAL: For ANY first-person perpetuals query about positions, open orders, or fill history — "我有什么永续持仓", "我的合约持仓", "我在 Hyperliquid 上挂了哪些单", "my perp positions", "my open futures orders", "我永续盈亏", "show my fills" — DO NOT ask the user for a wallet address. Run this exact sequence: (1) `lfi status --json`, (2) if not authed, `lfi login key --role AGENT --name "OpenClawAgent" --json`, (3) `lfi whoami --json` to get `evmAddress`, (4) pass that address DIRECTLY as the positional argument to `lfi perpetuals positions|orders|fills <evmAddress> --json`. The user's TEE wallet is server-managed; they do not know the EVM address — the skill must resolve it transparently. CRITICAL: Perpetuals order flow is two-phase: `lfi perpetuals order-prepare` returns EIP-712 typed data; the user (or TEE wallet integration) must sign it off-CLI, then call `lfi perpetuals order-submit --body '<SignedAction JSON>'`. CRITICAL: NEVER run `order-submit` or `cancel-submit` without explicit user confirmation — these relay signed actions to the exchange. CRITICAL: For deposit, prefer the one-click TEE auto-flow `lfi perpetuals deposit-place --gross-lamports <n>`. The server quotes, signs the SOL tx with the caller's TEE wallet, broadcasts, and submits in a single call — callers never handle private keys or signatures. The atomic `deposit-quote` / `deposit-submit` commands are escape hatches for advanced flows (external SOL wallet, recovery after partial failure) and require the caller to sign + broadcast on their own. See [reference/deposit-flow.md](reference/deposit-flow.md). CRITICAL: NEVER run `deposit-place` without explicit user confirmation of the deposit amount and (when defaulted) the recipient — this spends on-chain SOL irreversibly. Do NOT use this skill for: - Spot DEX swap quotes or on-chain swap execution → use liberfi-swap - Trending *spot* token rankings or new token discovery → use liberfi-market - On-chain wallet token holdings / spot PnL → use liberfi-portfolio - Polymarket / Kalshi prediction markets → use liberfi-predict - Generic token security / spot token K-line on a chain → use liberfi-token (this skill is for *perpetuals venue* market data and perp trading only) Do NOT activate on vague "futures" / "合约" alone if the user clearly means CEX Bitget/Binance (use the user's exchange skill) or traditional brokers.
Generate videos directly using the Runway API via runnable scripts. Supports text-to-video, image-to-video, and video-to-video with seedance2, gen4.5, veo3, and more.
Implement Syncfusion Angular Smith Chart component for high-frequency circuit visualization and transmission line analysis. Use this skill whenever the user needs to create smith charts, visualize impedance or admittance parameters, add multiple series to a smith chart, customize axes and gridlines, configure markers and data labels, implement legends with visibility toggling, add tooltips and export functionality, or styling and accessibility. Covers installation, basic rendering, series management, axis configuration, marker/label customization, legend setup and advanced features.
Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
Quantitative statistics framework for time-series analysis using Longbridge price data — ADF unit root test (stationarity), cointegration (Engle-Granger / Johansen), GARCH volatility modelling (conditional heteroskedasticity), regression diagnostics (Durbin-Watson / Breusch-Pagan), bootstrap confidence intervals, hypothesis tests (t-test / F-test). Requires statsmodels and scipy. Triggers: "量化统计", "ADF检验", "单位根", "协整检验", "GARCH", "自相关", "异方差", "Bootstrap", "假设检验", "量化統計", "ADF檢驗", "單位根", "協整檢驗", "異方差", "假設檢驗", "quantitative statistics", "ADF test", "unit root", "cointegration", "GARCH", "autocorrelation", "heteroskedasticity", "bootstrap", "hypothesis test", "statsmodels".
Validar prompts dirigidos a agentes de IA (Claude Code, Cursor, Copilot, etc.) contra reglas de redacción efectiva. Calcular un porcentaje de efectividad del prompt y devolver sugerencias de mejora concretas, más una propuesta de prompt reescrito. Cubre verbos no imperativos, lenguaje conversacional, acciones vagas, términos subjetivos, alcance difuso, prohibiciones implícitas, intenciones múltiples y nombres genéricos. Las reglas de detalle técnico (alcance, nombres exactos) se aplican solo a prompts de implementación; en prompts funcionales (user stories, descripciones de comportamiento) se marcan N/A. Usar siempre que el usuario pida validar, revisar, auditar, mejorar, corregir o "pulir" un prompt antes de enviarlo a un agente, o cuando pegue un prompt y pida feedback sobre cómo está redactado.
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
Rechazo de payloads que excedan el tamaño máximo permitido para prevenir ataques DoS