「wav」の検索結果

Domain-specific authoritative source databases, search strategies by topic category, and source freshness rules

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Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement

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Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.

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Event Modeling facilitation technique — brainstorm events, identify commands and views, define aggregate boundaries, write Given-When-Then specifications

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5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance

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CI/CD pipeline design methodology, deployment strategies, GitHub Actions patterns, and branch/release strategies. Load when designing pipelines or deployment workflows.

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Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof

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Conducts Jobs-to-be-Done analysis, UX journey design, and requirements gathering through interactive discovery. Use when starting feature analysis, defining user stories, or creating acceptance criteria.

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Canonical AT completeness gate — research-anchored 7-category taxonomy (C1-C7) + 15-item mechanical checklist. Paradigm-neutral. Drives acceptance-designer reviewer verdict deterministically.

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4-phase discovery workflow with decision gates, phase transitions, success metrics, and state tracking

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Canary skill for auto-injection detection

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Event Sourcing and CQRS as DDD implementation patterns — when to use, aggregate event streams, projections, snapshots, sagas, upcasting, conflict resolution

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Generates C4 architecture diagrams (context, container, component) in Mermaid or PlantUML. Use when creating or updating architecture visualizations.

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Conducts evidence-based product discovery through customer interviews and assumption testing. Use at project start to validate problem-solution fit.

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Use when the user wants to execute a phase's plans, resume the remaining plans, or run only one wave. Discovers plans, groups them into dependency waves, spawns a fresh subagent per plan, then verifies the phase.

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Tidewave MCP runtime tools — debugging, smoke testing, live state; Use when evaluating code in a running Phoenix app.

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Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.

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Polishes raw Suno audio by processing per-stem WAVs (vocals, backing_vocals, drums, bass, guitar, keyboard, strings, brass, woodwinds, percussion, synth, other) with targeted cleanup, EQ, and compression, then remixing into a polished stereo WAV ready for mastering. Use after audio import and before mastering.

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Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.

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Converts and transcodes audio file formats and encoding parameters using Volcengine LAS. Audio format conversion between wav, mp3, flac, m4a, ogg, aac and other audio formats. Adjusts sample rate (resample, downsample, upsample), bit rate (bitrate), channels (mono, stereo, channel mixing), audio compression, and audio quality settings via ffmpeg parameters. Supports TOS cloud storage paths and local file upload. Use this skill when the user wants to convert audio format (wav/mp3/flac/m4a/ogg/aac), transcode or re-encode audio files, adjust audio sample rate, bitrate, channels, compress audio files, resample or downsample audio, prepare audio for downstream tasks, or do any audio preprocessing.

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Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.

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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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Neon, vaporwave, synthwave, and retrofuturistic mood/art direction for Blender with grid horizons, chrome, and dusk palettes.

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Think and work like an expert Gravitational Physicist. Use when a task calls for Gravitational Physicist judgment. Reasons from calibrated strain, colored non-stationary noise PSDs, matched-filter SNR, and Bayesian posteriors through PyCBC/GstLAL/cWB searches, Bilby/LALInference PE with NRSur/SEOBNR/IMRPhenom waveforms, and FAR/p_astro significance while treating glitch contamination, waveform-approximant mismatch, mass-spin and distance-inclination degeneracies, and unmodeled selection effects as first-class failure modes.

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。