Communitygithub.com

2000年代音乐可视化专家

精通2000年代音乐可视化(Milkdrop、AVS、Geiss)及现代WebGL实现,专注于Butterchurn集成、Web Audio API AnalyserNode FFT数据、音频响应GLSL着色器和迷幻生成艺术。

2000年代音乐可视化专家 是什么?

该技能专为开发者和AI代理用户设计,旨在利用现代WebGL和Web Audio API重现2000年代(Milkdrop、AVS、Geiss)经典的迷幻音乐可视化效果。它支持Butterchurn库(WebGL版Milkdrop)的完整集成,能提取Web Audio API AnalyserNode的FFT和波形数据,实现实时音频响应视觉。通过GLSL片段着色器,用户可以创建复杂的音频驱动效果,并利用预设系统实现无缝过渡。该技能特别适用于沉浸式全屏音乐体验、节拍检测和迷幻生成艺术,但不适用于简单的频谱柱状图、静态波形或非音频视觉内容。它提供了详细的集成清单、反模式(如忽略AudioContext状态、线性频率显示)和性能优化建议,确保跨平台兼容性和高效渲染。

兼容平台~Claude Code~Codex CLI✓Cursor
npx skills add https://github.com/curiositech/windags-skills/tree/HEAD/skills/2000s-visualization-expert

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

2000年代音乐可视化专家 是做什么的?

精通使用现代WebGL和Web Audio API重现传奇的2000年代音乐可视化时代——Milkdrop、AVS、Geiss。

何时使用

✅ 适用于:

  • 实现Milkdrop风格的迷幻可视化效果
  • Butterchurn库集成(WebGL版Milkdrop)
  • Web Audio API AnalyserNode的FFT/波形提取
  • 用于音频响应效果的GLSL片段着色器
  • 全屏沉浸式音乐体验
  • 实时节拍检测和音频分析
  • 预设系统和可视化过渡

❌ 不适用于:

  • 简单的频谱柱状图(使用Canvas 2D)
  • 静态音频波形显示
  • 视频编辑或处理
  • 非音频生成艺术
  • 音频播放/流媒体问题(使用音频工程师技能)

黄金时代(概述)

时代关键软件创新
1998-2000Geiss简单等离子效果,DirectX
2001-2007Milkdrop 1 & 2逐像素方程,预设系统
2007-2015衰落流媒体服务兴起
2018-至今ButterchurnWebGL复兴

Milkdrop的魔力:将简单效果(模糊、缩放、旋转、色彩偏移)与音频响应参数分层叠加。

→ 参见 references/butterchurn-guide.md 了解完整历史和集成方法。

核心技术

Butterchurn(WebGL版Milkdrop)

  • GitHub星标1.7k,MIT许可
  • 与原始Milkdrop的预设完全兼容
  • npm包:butterchurn、butterchurn-presets
import butterchurn from 'butterchurn';
const visualizer = butterchurn.createVisualizer(audioContext, canvas, {
  width: window.innerWidth,
  height: window.innerHeight,
  pixelRatio: window.devicePixelRatio || 1,
});
visualizer.connectAudio(audioNode);
visualizer.loadPreset(preset, 2.0);  // 2秒混合过渡

Web Audio API FFT

const analyser = audioContext.createAnalyser();
analyser.fftSize = 2048;
analyser.smoothingTimeConstant = 0.8;
const frequencyData = new Uint8Array(analyser.frequencyBinCount);
analyser.getByteFrequencyData(frequencyData);

关键点:FFT bin是线性的,但人耳听觉是对数的!请使用对数映射。

→ 参见 references/web-audio-fft.md 了解频带提取方法。

GLSL着色器

将音频数据作为1D纹理传递,使用uniform变量表示低音/中音/高音:

uniform float u_bass;
float glow = smoothstep(0.5 - u_bass * 0.3, 0.0, dist);

→ 参见 references/glsl-shaders.md 获取完整模式。

应避免的反模式

1. 忽略AudioContext状态

表现:可视化静默失败 原因:AudioContext初始为暂停状态,需要用户交互 修复:点击时恢复:await audioContext.resume()

2. 线性频率显示

表现:低音占主导,高音不可见 原因:FFT bin是线性的;前100个bin可能只覆盖0-2kHz 修复:使用对数bin映射(代码见参考资料)

3. 无平滑处理

表现:画面抖动,易引发癫痫 原因:原始FFT数据帧间噪音大 修复:analyserNode.smoothingTimeConstant = 0.7

4. 使用requestAnimationFrame但未清理

表现:内存泄漏,多个渲染循环 修复:存储动画ID,在组件卸载时调用 cancelAnimationFrame

5. 硬编码Canvas尺寸

表现:在Retina屏幕上模糊,宽高比错误 修复:乘以 devicePixelRatio,处理resize事件

6. 阻塞主线程

表现:音频卡顿,帧率下降 原因:在UI线程上进行繁重的着色器编译 修复:在加载时编译着色器,而非播放期间

预设推荐

迷幻/致幻风格:

  • Flexi, martin + geiss - dedicated to the sherwin maxawow
  • Rovastar - Fractopia

柔和/放松风格:

  • Flexi - predator-prey-spirals
  • Geiss - Cosmic Strings 2

高能量风格:

  • Flexi + Martin - disconnected
  • shifter - tumbling cubes

集成检查清单

  • AudioContext已创建并在用户交互时恢复
  • AnalyserNode已连接到音频源
  • Canvas尺寸正确(考虑 devicePixelRatio)
  • 使用 requestAnimationFrame 的渲染循环
  • 组件卸载时清理(cancelAnimationFrame)
  • 带混合时间的预设加载
  • 处理resize事件
  • 全屏支持,ESC退出
  • 曲目信息叠加层(z-index高于canvas)
  • 鼠标闲置后隐藏光标

性能提示

  1. 降低纹理比例以适应旧GPU:textureRatio: 0.5
  2. 减少fftSize如果不需要:512或1024替代2048
  3. 在canvas上使用 will-change: transform
  4. 渲染循环期间避免DOM更新
  5. 使用Chrome DevTools GPU时间线进行分析

参考资料

→ references/butterchurn-guide.md - 完整的Butterchurn集成 → references/web-audio-fft.md - FFT提取和频率分析 → references/glsl-shaders.md - 音频响应着色器模式


本技能涵盖:Butterchurn/Milkdrop | Web Audio FFT | GLSL着色器 | 全屏可视化 | 音频响应艺术

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

curiositech/windags-skills

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

curiositech/windags-skills

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

curiositech/windags-skills

Implement WCAG 2.2 AA/AAA compliance with automated testing, keyboard navigation, screen reader support, and focus management. Activate on: accessibility audit, WCAG compliance, keyboard navigation, screen reader, aria attributes, axe-core, focus trap. NOT for: design-level accessibility review (use design-accessibility-auditor), color contrast only (use css-in-js-architect).

curiositech/windags-skills

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

curiositech/windags-skills

Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.

curiositech/windags-skills

>- Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

curiositech/windags-skills

Extend and modify the admin dashboard, developer portal, and operations console. Use when adding new admin tabs, metrics, monitoring features, or internal tools. Activates for dashboard development, analytics, user management, and internal tooling.

curiositech/windags-skills

Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).

curiositech/windags-skills

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

curiositech/windags-skills

AI-powered calendar management and agent-based scheduling coordination. Covers calendar APIs (Google Calendar, CalDAV/iCal), AI scheduling assistants (Reclaim, Clockwise, Motion, Cal.com), building custom calendar agents with MCP, multi-calendar merging, timezone management, focus block protection, meeting fatigue detection, and agent-to-agent meeting negotiation protocols. Activate on: "calendar agent", "AI scheduling", "calendar coordination", "meeting scheduling", "calendar API", "focus time protection", "calendar optimization", "Google Calendar MCP", "Reclaim", "Clockwise", "Motion", "Cal.com", "smart scheduling", "calendar-aware agent", "timezone scheduling", "agent negotiation meetings". NOT for: manual calendar UI component design (use form-validation-architect), project management scheduling or Gantt charts (use project-management-guru-adhd), general time-tracking or pomodoro apps (use adhd-daily-planner for time-awareness), building the agent itself from scratch (use agent-creator).

curiositech/windags-skills

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

curiositech/windags-skills

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

curiositech/windags-skills

Automated discovery and matching of agent skills for dynamic task routing and capability assessment

curiositech/windags-skills

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

curiositech/windags-skills

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

curiositech/windags-skills

Logic-based agent programming language implementing BDI architecture for practical autonomous agent development

curiositech/windags-skills

>- Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

curiositech/windags-skills

Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

curiositech/windags-skills

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

curiositech/windags-skills

license: Apache-2.0 NOT for unrelated tasks outside this domain.

相关技能