Ascii Video

Operational Steps

1. 确认输入参数完整
2. 执行核心操作（参考本目录下的 scripts/ 或 references/）
3. 验证输出符合契约
4. 保存结果并报告

Pitfalls

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Verification

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1. 2. 3.

IO_CONTRACT

• input: request: str, context: dict — 用户请求描述、上下文信息
• output: result: dict — 技能执行结果（结构因技能而异）

对应原则：P2（机械原子暴露输入输出规范）

ASCII Video Production Pipeline

When to use

Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.

What's inside

Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering.

Creative Standard

This is visual art. ASCII characters are the medium; cinema is the standard.

Before writing a single line of code, articulate the creative concept. What is the mood? What visual story does this tell? What makes THIS project different from every other ASCII video? The user's prompt is a starting point — interpret it with creative ambition, not literal transcription.

First-render excellence is non-negotiable. The output must be visually striking without requiring revision rounds. If something looks generic, flat, or like "AI-generated ASCII art," it is wrong — rethink the creative concept before shipping.

Go beyond the reference vocabulary. The effect catalogs, shader presets, and palette libraries in the references are a starting vocabulary. For every project, combine, modify, and invent new patterns. The catalog is a palette of paints — you write the painting.

Be proactively creative. Extend the skill's vocabulary when the project calls for it. If the references don't have what the vision demands, build it. Include at least one visual moment the user didn't ask for but will appreciate — a transition, an effect, a color choice that elevates the whole piece.

Cohesive aesthetic over technical correctness. All scenes in a video must feel connected by a unifying visual language — shared color temperature, related character palettes, consistent motion vocabulary. A technically correct video where every scene uses a random different effect is an aesthetic failure.

Dense, layered, considered. Every frame should reward viewing. Never flat black backgrounds. Always multi-grid composition. Always per-scene variation. Always intentional color.

Modes

Stack

Single self-contained Python script per project. No GPU required.

Pipeline Architecture

Every mode follows the same 6-stage pipeline:

INPUT → ANALYZE → SCENE_FN → TONEMAP → SHADE → ENCODE

1. INPUT — Load/decode source material (video frames, audio samples, images, or nothing)
2. ANALYZE — Extract per-frame features (audio bands, video luminance/edges, motion vectors)
3. SCENE_FN — Scene function renders to pixel canvas (uint8 H,W,3). Composes multiple character grids via _render_vf() + pixel blend modes. See references/composition.md
4. TONEMAP — Percentile-based adaptive brightness normalization. See references/composition.md § Adaptive Tonemap
5. SHADE — Post-processing via ShaderChain + FeedbackBuffer. See references/shaders.md
6. ENCODE — Pipe raw RGB frames to ffmpeg for H.264/GIF encoding

Creative Direction

Aesthetic Dimensions

Per-Section Variation

Never use the same config for the entire video. For each section/scene:

• Different background effect (or compose 2-3)
• Different character palette (match the mood)
• Different color strategy (or at minimum a different hue)
• Vary shader intensity (more bloom during peaks, more grain during quiet)
• Different particle types if particles are active

Project-Specific Invention

For every project, invent at least one of:

• A custom character palette matching the theme
• A custom background effect (combine/modify existing building blocks)
• A custom color palette (discrete RGB set matching the brand/mood)
• A custom particle character set
• A novel scene transition or visual moment

Don't just pick from the catalog. The catalog is vocabulary — you write the poem.

Workflow

Step 1: Creative Vision

Before any code, articulate the creative concept:

• Mood/atmosphere: What should the viewer feel? Energetic, meditative, chaotic, elegant, ominous?
• Visual story: What happens over the duration? Build tension? Transform? Dissolve?
• Color world: Warm/cool? Monochrome? Neon? Earth tones? What's the dominant hue?
• Character texture: Dense data? Sparse stars? Organic dots? Geometric blocks?
• What makes THIS different: What's the one thing that makes this project unique?
• Emotional arc: How do scenes progress? Open with energy, build to climax, resolve?

Map the user's prompt to aesthetic choices. A "chill lo-fi visualizer" demands different everything from a "glitch cyberpunk data stream."

Step 2: Technical Design

• Mode — which of the 6 modes above
• Resolution — landscape 1920x1080 (default), portrait 1080x1920, square 1080x1080 @ 24fps
• Hardware detection — auto-detect cores/RAM, set quality profile. See references/optimization.md
• Sections — map timestamps to scene functions, each with its own effect/palette/color/shader config
• Output format — MP4 (default), GIF (640x360 @ 15fps), PNG sequence

Step 3: Build the Script

Single Python file. Components (with references):

1. Hardware detection + quality profile — references/optimization.md
2. Input loader — mode-dependent; references/inputs.md
3. Feature analyzer — audio FFT, video luminance, or synthetic
4. Grid + renderer — multi-density grids with bitmap cache; references/architecture.md
5. Character palettes — multiple per project; references/architecture.md § Palettes
6. Color system — HSV + discrete RGB + harmony generation; references/architecture.md § Color
7. Scene functions — each returns canvas (uint8 H,W,3); references/scenes.md
8. Tonemap — adaptive brightness normalization; references/composition.md
9. Shader pipeline — ShaderChain + FeedbackBuffer; references/shaders.md
10. Scene table + dispatcher — time → scene function + config; references/scenes.md
11. Parallel encoder — N-worker clip rendering with ffmpeg pipes
12. Main — orchestrate full pipeline

Step 4: Quality Verification

• Test frames first: render single frames at key timestamps before full render
• Brightness check: canvas.mean() > 8 for all ASCII content. If dark, lower gamma
• Visual coherence: do all scenes feel like they belong to the same video?
• Creative vision check: does the output match the concept from Step 1? If it looks generic, go back

Critical Implementation Notes

Brightness — Use tonemap(), Not Linear Multipliers

This is the #1 visual issue. ASCII on black is inherently dark. Never use canvas * N multipliers — they clip highlights. Use adaptive tonemap:

def tonemap(canvas, gamma=0.75):
    f = canvas.astype(np.float32)
    lo, hi = np.percentile(f[::4, ::4], [1, 99.5])
    if hi - lo < 10: hi = lo + 10
    f = np.clip((f - lo) / (hi - lo), 0, 1) ** gamma
    return (f * 255).astype(np.uint8)

Pipeline: scene_fn() → tonemap() → FeedbackBuffer → ShaderChain → ffmpeg

Per-scene gamma: default 0.75, solarize 0.55, posterize 0.50, bright scenes 0.85. Use screen blend (not overlay) for dark layers.

Font Cell Height

macOS Pillow: textbbox() returns wrong height. Use font.getmetrics(): cell_height = ascent + descent. See references/troubleshooting.md.

ffmpeg Pipe Deadlock

Never stderr=subprocess.PIPE with long-running ffmpeg — buffer fills at 64KB and deadlocks. Redirect to file. See references/troubleshooting.md.

Font Compatibility

Not all Unicode chars render in all fonts. Validate palettes at init — render each char, check for blank output. See references/troubleshooting.md.

Per-Clip Architecture

For segmented videos (quotes, scenes, chapters), render each as a separate clip file for parallel rendering and selective re-rendering. See references/scenes.md.

Performance Targets

References

Creative Divergence (use only when user requests experimental/creative/unique output)

If the user asks for creative, experimental, surprising, or unconventional output, select the strategy that best fits and reason through its steps BEFORE generating code.

验证清单 · VERIFICATION

• 编码前已明确阐述创意概念（情绪、视觉故事、色世界、"什么让 THIS 不同"），拒绝提示词的字面转录
• 关键时间戳已先行渲染单帧测试，且亮度检查通过（canvas.mean() > 8），暗场景已调低 gamma 而非用 canvas * N 线性乘子
• 所有场景共享统一视觉语言（色温/字符调色板/运动词汇一致），且每个场景有差异化配置（背景效果/调色板/颜色策略/shader 强度）
• 项目至少发明了一个 catalog 之外的元素（自定义字符调色板/背景效果/颜色组合/粒子字符/新转场），且包含一个用户未要求但提升品质的视觉时刻
• 实现避开了已知陷阱：macOS 用 font.getmetrics() 计算 cell height、ffmpeg 不用 stderr=subprocess.PIPE（重定向到文件）、初始化时验证字符字体兼容性（无空白输出）
• 完整渲染后输出文件可正常播放（MP4/GIF/PNG 序列），帧率/分辨率符合技术设计（如 1920x1080@24fps），首帧即达视觉惊艳标准（非平庸 AI 生成感）

约束规则 · RULES

1. 输入约束: 参数类型、范围、格式必须校验
2. 输出约束: 返回值结构、编码、命名必须一致
3. 异常约束: 错误信息必须包含上下文和恢复建议
4. 安全约束: 不执行未验证的任意代码，不暴露内部状态

Golden 集合 · GOLDEN SET

• Golden Input: 标准输入样本（覆盖正常路径）
• Golden Output: 预期输出（精确匹配或格式校验）
• Golden Error: 预期错误信息（覆盖失败路径）

Golden 集合是测试的单一真理来源。所有改进必须通过 golden 测试。

违反规则的操作视为不安全，必须拒绝或隔离。

每项验证必须可执行、可记录、可复现。验证失败时记录原因和修复。

• Forced Connections — when the user wants cross-domain inspiration ("make it look organic," "industrial aesthetic")
• Conceptual Blending — when the user names two things to combine ("ocean meets music," "space + calligraphy")
• Oblique Strategies — when the user is maximally open ("surprise me," "something I've never seen")

Forced Connections

1. Pick a domain unrelated to the visual goal (weather systems, microbiology, architecture, fluid dynamics, textile weaving)
2. List its core visual/structural elements (erosion → gradual reveal; mitosis → splitting duplication; weaving → interlocking patterns)
3. Map those elements onto ASCII characters and animation patterns
4. Synthesize — what does "erosion" or "crystallization" look like in a character grid?

Conceptual Blending

1. Name two distinct visual/conceptual spaces (e.g., ocean waves + sheet music)
2. Map correspondences (crests = high notes, troughs = rests, foam = staccato)
3. Blend selectively — keep the most interesting mappings, discard forced ones
4. Develop emergent properties that exist only in the blend

Oblique Strategies

1. Draw one: "Honor thy error as a hidden intention" / "Use an old idea" / "What would your closest friend do?" / "Emphasize the flaws" / "Turn it upside down" / "Only a part, not the whole" / "Reverse"
2. Interpret the directive against the current ASCII animation challenge
3. Apply the lateral insight to the visual design before writing code

Ascii Video

Genes (策略基因)

紧凑策略表示。条件→策略。需要深度时参考完整文档。

• [ASCI-008] 开始编码前 → 必须明确阐述创意概念（情绪、视觉故事、独特性），拒绝仅做提示词的字面转录
• [ASCI-009] 首次渲染输出 → 必须达到视觉惊艳标准，若显得平庸或像通用 AI 生成物则需重新构思而非修补
• [ASCI-010] 选择视觉效果时 → 将参考目录视为词汇表而非成品，必须组合、修改或发明新模式以超越现有预设
• [ASCI-011] 构建视频场景 → 优先保证整体美学连贯性（统一色温、字符调色板、运动语言），而非追求单场景的技术正确性
• [ASCI-012] 设计每一帧画面 → 采用多层网格构图并避免纯黑背景，确保画面具有密度、层次感和奖励性
• [ASCI-013] 处理多场景视频 → 为每个章节/场景差异化配置背景、字符调色板、颜色策略及粒子类型，禁止全片使用相同配置
• [ASCI-014] 项目交付前 → 主动添加至少一个用户未明确要求但能提升整体品质的视觉时刻（如特殊转场、色彩选择或效果）