125 skills · sorted by relevance
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
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Diffusion-based docking that predicts protein-ligand poses without a predefined site. Use for blind docking, when traditional docking fails, or exploring multiple binding modes. Pipeline: prep protein (PDB) and ligand (SMILES/SDF), run inference, analyze confidence-ranked poses.
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Routes official FlashVSR diffusion-based streaming video super-resolution setup, model preparation, and CUDA inference for v1 and v1.1.
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Use and troubleshoot the Dream Textures Blender add-on for Stable Diffusion image generation, texture projection, render passes, model setup, and backend APIs.
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Use Dipy for diffusion MRI IO, preprocessing, reconstruction, tractography, registration, visualization-aware workflows, and command-line operations.
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Use the DALLE2-pytorch package for DALL-E 2 style text-to-image model APIs, diffusion prior/decoder training, WebDataset data layouts, and tracker/checkpoint workflows.
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Routes ACT++, ACT, Diffusion Policy, VINN, and MuJoCo simulation workflows for bimanual ALOHA episode data and imitation-learning tasks.
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為使用者產生高品質的 AI 生圖、生影片、生音樂提示詞，並在需要時透過瀏覽器自動化實際送到目標平台。涵蓋 OiiOii、Kling 3.0/O-series、Seedance 2.0/2.5、Suno v5.5、Seedream 5.0/4.0、Vidu Q3、Midjourney V8.1、Flux 1.1 Pro / Kontext、Runway Gen-4.5 / Aleph、Google Veo 3.1、Ideogram 3、Nano Banana Pro、Stable Diffusion 3.5（⚠️ OpenAI Sora 2 已於 2026-04-26 停運，API 撐到 2026-09-24，預設改推 Runway/Veo/Kling）。只要使用者提到「AI 生圖」「AI 影片」「AI 音樂」「做 MV」「做 storyboard」「寫 prompt 給 XXX」「我想用 Kling/Suno/Midjourney/Runway/Veo...」「幫我操作 OiiOii / 即夢 / 可靈」「txt2img / img2video / 文生圖 / 文生影片 / 圖生影片」「角色一致性」「多鏡頭分鏡」「運鏡」「結果有瑕疵 / 不夠精緻 / 怎麼修」，或任何跟上述平台或影像/影片/音樂生成工作流相關的任務，都要用這個 skill。即使他們沒講明平台，只要任務是要餵給某個生成模型的 prompt，就用這個 skill 幫他們選對的平台、寫對的格式。
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Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.
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Pixel Path turns any two places (optionally via waypoints) into a pixel-art fly-through of the real road. It routes the trip, bakes the real buildings, land cover, landmarks, hills and horizon from OpenStreetMap and elevation data, generates scenery art with RetroDiffusion at a chosen level of detail (sparse, moderate or detailed; estimated up front, quoted exactly for free, budget-capped; free placeholder art also works), builds a page where you zoom along the route and scroll to go faster or slower, and runs a self-check that screenshots the result. Use when someone asks for a "pixel path", a "pixel road trip", to "drive" or "fly" from X to Y in pixel art, or a retro first-person view along a real route.
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Think and work like an expert Catalysis Scientist. Use when a task calls for Catalysis Scientist judgment. Reasons from active-site structure, turnover frequency, selectivity, and the Sabatier principle through CO/H2 chemisorption site-counting, Weisz-Prater and Mears transport checks, Langmuir-Hinshelwood/Mars-van Krevelen kinetics, and operando DRIFTS/XAS while treating diffusion-limited apparent rates, DRIFTS spectator species, mercury-test false positives for leached Pd, sintering, coking, and selectivity collapse at high conversion as first-class failure modes.
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Think and work like an expert Applied Mathematician. Use when a task calls for Applied Mathematician judgment. Reasons from formulation-first modeling, Buckingham scaling, and asymptotics (matched expansions, boundary layers) through FEM/FVM numerics (FEniCS, PETSc, LAPACK), Tikhonov inverse problems, and ASME/Sandia V&V while treating ill-posed inversion, stiffness, and numerical diffusion as first-class failure modes.
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Operate Stability AI image generation, image-to-image, edit, control, background, and upscale APIs safely and reproducibly. Use when selecting or calling Stable Image Ultra, Core, Stable Diffusion 3.5, v2beta image edit/control services, or Stability open-weight image models; when debugging schemas, moderation, rate limits, cost, lifecycle, licenses, privacy, provenance, or migrations; and when planning production QA for Stability-generated images.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
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Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.
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World-class character and art style consistency for AI-generated images and videos - ensures visual coherence across series, maintains character identity, and provides rigorous QA before deliveryUse when "character consistency, art style, same character, consistent character, visual continuity, series, turnaround sheet, character sheet, reference image, character bible, style guide, anime character, consistent look, face consistency, outfit consistency, lora training, ip-adapter, flux kontext, visual qa, art quality, generation review, style drift, character drift, character-consistency, art-style, visual-qa, ai-art, image-generation, video-generation, anime, illustration, lora, ip-adapter, flux, midjourney, stable-diffusion" mentioned.
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Expert patterns for AI-powered image editing including inpainting, outpainting, ControlNet, image-to-image, and API integration with Replicate, Stability AI, and FalUse when "ai image editing, inpainting, outpainting, controlnet, image to image, remove object from image, extend image, flux inpaint, sdxl editing, image-editing, inpainting, outpainting, controlnet, stable-diffusion, flux, replicate, stability-ai, comfyui" mentioned.
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Builds ML pipelines: EDA, features, leakage checks, evaluation. Use when doing data science or explaining fairness, privacy, speech, vision-language, or diffusion mechanics.
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When the user wants to create, generate, edit, or optimize images for marketing — blog heroes, social graphics, product mockups, profile banners, listing visuals, or brand assets. Also use when the user mentions 'AI image generation,' 'generate an image,' 'create a graphic,' 'product mockup,' 'hero image,' 'social media graphic,' 'banner image,' 'cover photo,' 'profile banner,' 'listing screenshot,' 'Flux,' 'Flux Kontext,' 'Midjourney,' 'DALL-E,' 'GPT Image,' 'ChatGPT Images,' 'Ideogram,' 'Gemini image,' 'Nano Banana,' 'Recraft,' 'Stable Diffusion,' 'Canva,' 'Figma,' 'image optimization,' 'compress images,' 'WebP,' or 'OG image.' Use this for general-purpose marketing image creation and optimization. For paid ad image creative and platform-specific ad specs, see ad-creative. For video production, see video.
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Expert guide for AI image generation (Flux, DALL-E, Stable Diffusion), video generation (Sora, Runway), voice synthesis (ElevenLabs TTS), and speech recognition (Whisper STT) integration / Panduan ahli integrasi AI generasi gambar, video, suara (TTS), dan pengenalan suara (STT).
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Study of human activities, spatial patterns, and social processes on Earth's surface. Covers population and migration, cultural diffusion and landscapes, urbanization and city systems, economic geography and development, political geography and borders, and social/identity geographies. Use when reasoning about why people live where they do, how cultures spread, how cities grow, how economies are spatially organized, or how power operates through space.
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Physical principles underlying diodes, bipolar junction transistors, and MOSFETs — carrier concentrations, drift and diffusion, the pn junction, forward and reverse bias, the Shockley diode equation, BJT operating regions, MOSFET threshold and saturation, small-signal models, and temperature effects. Use when reasoning about device-level behavior, designing bias networks, selecting transistor operating points, interpreting datasheets, or diagnosing nonlinear device failures.
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Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
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DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.
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