ascii-video skill

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. SCENEFN — 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.

• 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")