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Found 78 Skills
Retrieve the latest Runway API reference from docs.dev.runwayml.com and use it as the authoritative source before any integration work
Build, modify, debug, or verify Runway Model Routers: inspect live config and eligible models via MCP, manage approved settings, integrate routed SDK calls, and inspect routing results. Use with +runway-dev. Not for direct per-model endpoints or agent REST CLI.
Help users create Runway Characters (GWM-1 avatars) and integrate real-time conversational sessions into their apps
Analyze a user's codebase to verify it can use Runway's public API (server-side requirement)
Foundation for building, modifying, debugging, or verifying Runway Dev Platform integrations in an application: connect Dev MCP, use llms.txt to find current resources, resolve project context, use SDK wait helpers, and handle errors. Load with relevant runway-dev-* surface skills. Not for direct media generation scripts or REST CLI shortcuts.
Build, modify, debug, or verify Runway Characters integrations: inspect preset or custom avatars with MCP, manage live Sessions via SDK, and connect the application UI. Use with +runway-dev. UI says Characters; MCP/API use avatars. Not for avatar video generation endpoints.
Prompt and direct Runway (Gen-4.5, Aleph) — the control-grade generative video tool for camera moves, character consistency, and edit-grade shots. Use when someone wants "a specific camera move," "the same character across multiple shots," "to edit/restyle existing footage," "a branded product/ad shot," "film-style previz," "image to video with Runway" (animate a still), or names Runway / Gen-4 / Gen-4.5 / Aleph / Act-Two / Motion Brush / References. Runway generates clips; a human assembles/edits; audio comes from ai-voiceover; WoopSocial schedules/publishes. Below the ai-video router, sibling to veo-3 (all-round + native audio), kling (native 4K/multi-shot/motion), and luma (cinematic HDR/mood). Consented performances only (Act-Two); AI disclosure mandatory.
Generate AI videos with Google Veo, Seedance, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 1.5 Pro, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative
Best practices and techniques for writing effective AI video generation prompts. Covers: Veo, Seedance, Wan, Grok, Kling, Runway, Pika, Sora prompting strategies. Learn: shot types, camera movements, lighting, pacing, style keywords, negative prompts. Use for: improving video quality, getting consistent results, professional video prompts. Triggers: video prompt, how to prompt video, veo prompts, video generation tips, better ai video, video prompt engineering, video prompt guide, video prompt template, ai video tips, video prompt best practices, video prompt examples, cinematography prompts
Set the organisation's top-level paid-media spend guardrails as written policy - maximum allowable CAC, minimum ROAS/MER floor, kill-switch thresholds, counter-metrics, who owns them and who may override them - each derived from contribution margin, payback, and cash runway rather than inherited from a dashboard. Use whenever the user asks what CAC they can afford, mentions a ROAS floor, spend guardrails, a kill switch, when to stop spending, or who approves a budget increase - even if they never say 'guardrails'. Covers B2B and B2C. Do NOT use to judge whether current CAC/ROAS is actually good (mbfinotti/advertising-skills@cac-roas-benchmark) or to monitor daily pacing (mbfinotti/advertising-skills@ad-budget-pacing).
This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or requests 3-5 year financial planning for a startup.
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".