「wav」の検索結果

Use this skill to run a holistic regrounding pass on the entire microsoft/apm documentation corpus against current source code, page-by-page, and emit surgical fixes for stale claims. Activate when the maintainer wants a WHOLE-CORPUS audit (not per-PR review) -- typical triggers include "audit the docs", "reground the corpus", "check every page against code", "pre-release docs sweep", "the docs have drifted everywhere", or "we just reshaped the TOC, find dead links". Wave-batched and S7-verified; scales to the full ~112-page corpus in ~10 minutes wall-time. This is a SIBLING to docs-sync, not a replacement: docs-sync is per-PR (triggered by a diff); this skill is per-corpus (triggered by a maintainer ask). They share agent personas, schemas, and the docs index, but their triggers MUST NOT collide. Does NOT auto-merge, does NOT push without maintainer review, and does NOT replace per-PR drift detection.

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Create complete specs (requirements, design, tasks) for all features in roadmap.md sequentially by dependency wave in Cascade.

日本語の概要は準備中です。原文の説明を表示しています。

Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel subagent dispatch by dependency wave.

日本語の概要は準備中です。原文の説明を表示しています。

Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

日本語の概要は準備中です。原文の説明を表示しています。

Create complete specs for roadmap features by dependency wave using Devin subagents when available, with permission-aware sequential and inline fallbacks.

日本語の概要は準備中です。原文の説明を表示しています。

Execute all plans in a phase with wave-based parallelization

日本語の概要は準備中です。原文の説明を表示しています。

文字转语音配音：把文案/脚本合成为 AI 语音口播、旁白、朗读音频。**配了 VOICE_PROVIDER 默认走闭源云 TTS（CosyVoice2 等，有情感、像真人），edge 仅无 key 时兜底**（edge 偏机械/AI 味）；同步输出分句 SRT 字幕、mp3/wav/m4a。合成后可与 BGM 混音或加到视频作旁白。当用户说“配音”“文字转语音”“TTS”“AI 配音”“口播语音”“旁白”“朗读”“把这段文字读出来”“生成语音”“语音合成”时使用。

日本語の概要は準備中です。原文の説明を表示しています。

音频可视化视频：把纯音频（播客片段、音乐、口播金句、电台）渲染成带动态波形/频谱的视频，配封面和标题，好发到抖音/B站/视频号等只收视频的平台。当用户说 音频可视化、音频转视频、播客做成视频、音频波形视频、音乐可视化、给音频配画面、声波视频、频谱视频、把音频发到视频平台、电台切片视频 时使用。基于 shared/scripts/audio_viz.py（ffmpeg showwaves/showcqt/showspectrum）。与 audio-mix 区别：那个输出音频，本 SKILL 输出视频；与 slideshow-video 区别：那个用图片，本 SKILL 用音频驱动画面。

日本語の概要は準備中です。原文の説明を表示しています。

通用音频处理：音频剪辑/裁剪、格式转码（mp3/wav/m4a/aac）、音量归一化、从视频提取音轨、多段拼接、淡入淡出、变速（保音高）。当用户说“剪音频”“裁一段”“转成 mp3”“调音量/响度”“提取音轨/扒音频”“拼接音频”“淡入淡出”“加速/减速音频”“变速不变调”时使用。与 audio-denoise 的区别：audio-denoise 专做降噪，audio-editing 做除降噪外的通用音频操作（也内置 denoise 作为兜底）。

日本語の概要は準備中です。原文の説明を表示しています。

Analyze and execute a multi-workload migration into or across Adobe services with contract evidence, dependency waves, dual-run reconciliation, cost controls, and rollback. Use when the task requires adobe platform migration program. Trigger with "Adobe migration plan", "move workflow to Adobe", or "Adobe platform consolidation".

日本語の概要は準備中です。原文の説明を表示しています。

Local speech-to-text transcription on Apple Silicon macOS. Supports wav directly and other audio formats via ffmpeg.

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Build and iterate playable Three.js game systems: starter scaffold, architecture, design briefs, core loops, level and encounter design, entities, input, camera, collision and physics, scoring, objectives, and game feel. Use for first playable slices, new Vite/TypeScript/Three.js setups, level/arena/track/wave/hole/puzzle design, combat encounters, difficulty tuning, and juice.

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Apply when introducing a new internal API while old callers still exist. Migrate callers and delete the old API in the same wave instead of preserving compatibility layers.

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Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.

日本語の概要は準備中です。原文の説明を表示しています。

Three.js OBJExporter utility for exporting 3D geometry to Wavefront OBJ format. Use when converting Three.js scenes, meshes, or geometries to OBJ files for use in other 3D software like Blender, Maya, or MeshLab.

日本語の概要は準備中です。原文の説明を表示しています。

Extract audio from video files to WAV format. Use when you need to analyze audio from video, prepare audio for energy calculation, or convert video audio to standard format for processing.

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Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.

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Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries data.

日本語の概要は準備中です。原文の説明を表示しています。

Apply when introducing a new internal API while old callers still exist. Migrate callers and delete the old API in the same wave instead of preserving compatibility layers.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare for an industry analyst briefing (Gartner, Forrester, IDC and similar). Use when asked to prep an analyst briefing, write an AR briefing document, build talking points for an analyst call, or prepare a Magic Quadrant / Wave submission narrative. Produces a briefing kit — objective, company/product narrative, differentiation, proof points, the demo storyline, anticipated questions, and follow-up commitments.

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Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.

日本語の概要は準備中です。原文の説明を表示しています。