Media skills

Design and implement real-time audio processing chains using Audio Toolbox streaming objects. Use when building frame-based audio processing loops, multiband filters, dynamic range control, parametric EQ, level metering, loudness metering, SPL metering, octave-band analysis, sample rate conversion, frequency-domain filtering (long impulse responses, custom filter banks), or audio chains in Simulink. Covers visualization (visualize method), interactive tuning (parameterTuner), MIDI control, and Audio Toolbox Simulink blocks. Use when the user says "real-time audio", "streaming audio", "audio filter", "compressor", "equalizer", "level meter", "loudness meter", "SPL meter", "octave bands", "crossover filter", "audio chain", "MIDI control", "convolution reverb", "impulse response streaming", "frequency-domain filter", or asks to process audio frame-by-frame.

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Guide authoring of Audio Toolbox plugins (audioPlugin, audioPluginSource) that pass validateAudioPlugin and generate deployable VST/AU code. Use when creating audio effect or generator plugins, writing classdef files inheriting from audioPlugin, or troubleshooting validateAudioPlugin failures.

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Reference for MATLAB audiostreamer (Audio Toolbox R2025a+). Without this skill, agents consistently default to legacy audioDeviceWriter/audioDeviceReader or base MATLAB sound(), producing less capable code. Use when writing code for audio playback, recording, full-duplex device I/O, real-time audio measurements, or audio I/O processing with callbacks. Also use when debugging audiostreamer errors, dropouts, or latency issues, or migrating from audioDeviceReader, audioDeviceWriter, audioPlayerRecorder, or audioplayer/audiorecorder.

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Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both.

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Load this first for any MATLAB task involving images, photos, scans, frames, volumes, or visual data — reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, counting objects, removing backgrounds, measuring, registering, stitching, aligning, cropping, resizing, or transforming. Covers image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color, morphology, deep learning for images, quality assessment, and 3-D volumes. Also for image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration (folder of images). Includes medical imaging I/O (DICOM, NIfTI, NRRD, medicalVolume) and large/blocked images (blockedImage, whole-slide). Skip if the user just wants you to look at an image, with no MATLAB work. Not for Simulink video-block models, C/C++/CUDA code generation or GPU deployment of a network, or point clouds. Hyperspectral and visual inspection have sibling skills.

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Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.

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Display images and annotations for image processing, computer vision, and visual inspection. Use when displaying images with imageshow, creating image viewers with viewer2d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming video frames, or building apps with image display.

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Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.

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Build, import, analyze, tolerate, and optimize optical systems using the Optical Design and Simulation Library. Use when the user asks about optical systems, ray tracing, geometric optics, Zemax import, optical coatings, tolerancing, or optical design optimization.

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Work with hyperspectral and multispectral images in MATLAB. Covers reading/writing (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), ECOSTRESS spectral libraries, processing (calibration, atmospheric correction, denoising, sharpening, dimensionality reduction, endmember extraction, unmixing, target/anomaly detection, spectral indices, segmentation), labeling (Spectral Image Labeler app, ground truth objects, automation algorithms), and deep learning (pixel classification CNNs, unmixing autoencoders, transfer learning). Use when reading, writing, processing, analyzing, classifying, labeling, or applying deep learning to hyperspectral or multispectral images.

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Build OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.

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把一条视频重建成「只有画面和声音」的干净文件——**源片的元数据一概不搬**： GPS、设备型号、账号 ID、创建时间、章节、GoPro 的遥测轨，全部留在原地。 走的是白名单而不是黑名单：不列要删什么，只说带什么过去（画面一条流、声音一条流）， 隐私保证来自结构，不来自枚举。 难点不在容器 tag，在**看不见的那几层**：x264 把完整编码参数写成 SEI 塞在码流里、 AAC 把版本号写进 DSE、avc1 的 compressorname 里还有一份——`ffprobe` 一个都看不见。 五个藏身处逐一堵死，每一处都有实测。 默认 `--mode copy`：**画面逐字节照搬**（cmp 验证过），只重编音频，53 秒的片子 1.3 秒跑完； `--mode encode` 完整重编码，多杀掉码流域的东西。 验收不靠声称：把源片所有元数据字符串当「针」，在输出文件里做**字节级扫描**， 扎到一根就红。12 道门全部由脚本确定性检查。 零依赖、零 API key，只要 node 和 ffmpeg。 Use when asked to 清元数据、去元数据、抹掉视频信息、视频隐私、去水印信息、 strip video metadata、remove exif from video、scrub video。

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拉片：把一条成片拆成逐镜头的分析表——每个镜头的时长、景别、类别、运镜、画面。 分工刻在骨子里：**能量的都由代码量**（切点来自 ffmpeg 场景检测，时长是切点相减， 运动量是逐帧差分的中位数），模型只判断它真正该判断的那几件事（景别 / 类别 / 运镜 / 画面 / 节奏）， 然后每一条判断都被代码当场对账——**声称推拉摇移却实测几乎不动，门直接拦**。 看片走联系表（每镜起手帧 + 收尾帧各拼一张大图，一屏二十几个镜头，a/b 对照就是运镜）， 不是一张张翻。检测漏刀多刀用 recut 补刀并刀，自动重编号重算时长，手改边界过不了门。 产出 shots.json + Markdown 镜头表 + **单页交互式拉片报告**：内嵌播放器（播放时同步高亮镜头、 点镜头跳转）、镜头节奏带、可搜索可筛选可排序的镜头表（列表 / 卡片两种视图、首尾关键帧并排、 点图开大图）、景别类别运镜分布、出场人物、质量门、导出 JSON。单文件零依赖，离线双击能开。 15 道质量门全部由脚本确定性检查。 零依赖、零 API key，只要 node 和 ffmpeg。 Use when asked to 拉片、拆镜头、分析视频镜头、镜头时长、景别、运镜、镜头表、 video shot breakdown、shot list from video。

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把拉片数据和原片合成一条**能直接看的视频**：一边是画面，一边是这一镜的分镜信息 （镜号、起止、时长、景别、类别、运镜、画面描述、台词），**镜头切了信息跟着切**， 镜头表自动滚动并高亮当前这一镜。 版式只看原片的宽高比：**横版 / 方版 → 画面在上、信息在下；竖版 → 画面在左、信息在右**， 画面永远原样缩放，不裁不拉。 镜头表**随播放滚动**：切点处滚一小段把当前镜头带到锚点、随后停住，高亮条同步滑过去。 实现上只截三张图（底板 + 铺开的长图 ×2），滚动与高亮由 ffmpeg 按时间裁窗， **布局全在 scripts/panel.css 里，改它就能改版式**，不用碰脚本。 吃 video-shots 产出的 shots.json（有 frames/ 就把关键帧当缩略图用）。 零 npm 依赖，用 ffmpeg 合成、无头浏览器渲面板。 Use when asked to 导出视频、合成视频、分镜视频、带分镜信息的视频、解说版视频、 video with shot info、annotated shot video。

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Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording text, or asks to process a video or audio source. Do NOT use for text that is already prose, for summarising a transcript, or for the ingestion itself.

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Use when bringing 3D assets into Roblox Studio or preparing avatar items: import from Blender, GLB, FBX or OBJ files, AI-generated or image-to-3D meshes (Scenario or other tools), wrong scale or size in studs, mesh faces the wrong way, too many triangles, mesh fails validation, holes or non-watertight meshes, textures too big or blurry, SurfaceAppearance and PBR maps, metalness or normal maps look wrong, rigid accessories and hats, UGC items, Accessory Fitting Tool, out of bounds, AvatarCreationService validation, EditableMesh or EditableImage previews, or generate_mesh.

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Use when making or fixing Roblox visual effects or game audio: ParticleEmitter, Beam, Trail, flipbook sprite sheets, slash, hit, impact, explosion, fireball, aura or projectile VFX; effects only one player sees, effects that lag or pile up, particles invisible, popping or too bright; the Audio API (AudioPlayer, Wire, AudioEmitter, AudioListener, AudioDeviceOutput, effects), "no sound", "3D sound not working", music, ducking, footsteps, SoundGroup volumes; or importing Scenario-generated sound effects, music, voice or VFX sheets into a Roblox game.

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Use when color grading, color correcting, or relighting an existing video or still with deterministic code instead of a generative model: cinematic looks (teal and orange, film stock, bleach bypass, noir, duotone, faded 70s), golden hour, blue hour or day-for-night, log footage (S-Log3) to Rec.709, matching a reference image's colors, black and white with one kept or swapped color, .cube LUT export, a look comparison grid, or a grading showreel.

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Use when someone wants seamless looping motion graphics for video in their own brand colors: animated backgrounds behind titles, filler for the dead seconds between AI shots, bumpers, transitions or shape animations, built as code and rendered to MP4, optionally with their mascot, product or character from Scenario composited over the loop. Keywords: seamless loop, motion background, canvas animation, motion graphics, loop seam, motion blur, H.264 export, alpha composite.

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Use when recreating a trending reel, TikTok or Short with a different performer: the same dance, moves, timing and camera, performed by the user's own character from a reference sheet, optionally in another outfit or a redecorated room, keeping the original song in sync. Keywords: reel recast, trend remake, TikTok dance, replace the dancer, character swap, choreography transfer, video to video, keep the music, Seedance edit mode.

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Use when a 2D side-scroller shot, platformer cutscene, game trailer or parallax animation has to be built in Blender from Scenario-generated pictures (flat image cards, an orthographic camera, keyed jumps, rigid-body props), or when a finished shot needs a new theme or look without re-animating. Also use when image planes in Blender float above the ground, a tiled parallax layer shows a seam, or physics props leave the picture plane.

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Use when an agent works in an open Blender scene through the Scenario for Blender extension's local MCP server (scenario-blender on 127.0.0.1:9876): connecting Claude Code, Cursor, Claude Desktop or Codex to Blender, the headless scenario_blender command, quoting and generating Scenario images, PBR materials, 3D meshes, video, audio or 360 environments from the scene, capturing the viewport or selected meshes as references, placing saved results, Render Video camera paths, or execute_python.

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Use when exploring several Seedance video takes before paying for a final: cheap 480p previews, variations of one shot to compare, reviewing and ranking takes before a pick, or finalizing a chosen preview to 1080p without regenerating it, including within its 7-day window. Keywords: Seedance draft, draft mode, draft complete, finalize, 480p preview, variations, takes, review, rank, pick, Seedance 2.5.

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Use when creating or revising finished video thumbnails or cover images with Scenario: game trailers, boss reveals, patch updates, devlogs, or community videos needing readable composition, exact headline text and consistent characters. Not for title ideas or critique alone, in-video title cards, or a generic image without a thumbnail deliverable.

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