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ASCII Video Production

Free

Transform videos into striking ASCII art animations.

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Free · Opens the source repo

What ASCII Video Production does

The ASCII Video Production skill enables users to convert video and audio inputs into visually engaging ASCII art outputs, including MP4 and GIF formats. This skill is particularly useful for artists and developers looking to create retro-style video content or unique visualizations that leverage ASCII characters as the primary medium. The production pipeline supports various modes, allowing for video-to-ASCII conversion, audio-reactive visuals, and generative animations, making it versatile for different creative projects.

The process begins with input analysis, where the skill extracts features from the source material, such as video luminance and audio frequencies. This information is then utilized to render scenes using a series of customizable effects and shaders. Users can create hybrid outputs that combine video and audio, enhancing the overall experience with synchronized visuals and sound. The skill emphasizes a cohesive aesthetic, ensuring that all scenes maintain a unified visual language, which is crucial for delivering high-quality artistic outputs.

This skill is designed for those who appreciate the artistic potential of ASCII art and want to push the boundaries of traditional video production. It encourages users to think creatively about their projects, focusing on the mood and visual storytelling rather than merely transcribing content. With a strong emphasis on first-render excellence, users are prompted to innovate and extend the skill's capabilities to meet their specific artistic visions. The included references provide guidance on various aspects of ASCII art production, from color strategies to shader effects, making it a comprehensive tool for creative exploration.

When to use it

Use this skill when you want to create artistic ASCII video content, such as animated text art or audio-visualizers, that stands out in a retro or unique style.

When not to use it

This skill may not be suitable for projects requiring high-definition video outputs or complex graphics beyond ASCII art.

What you can build with it

Create ASCII Music Visualizers

Transform audio tracks into dynamic ASCII visualizations that react to sound frequencies, enhancing the listening experience.

Produce Retro-Style Video Content

Convert standard video footage into ASCII art, giving it a nostalgic feel suitable for artistic projects or social media.

Generate Custom ASCII Animations

Use generative inputs to create unique ASCII animations that can be tailored to specific themes or concepts.

How to install ASCII Video Production

View source

1. Install with the skills CLI

npx skills add nousresearch/hermes-agent/ascii-video --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by nousresearch

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

ModeInputOutputReference
Video-to-ASCIIVideo fileASCII recreation of source footagereferences/inputs.md § Video Sampling
Audio-reactiveAudio fileGenerative visuals driven by audio featuresreferences/inputs.md § Audio Analysis
GenerativeNone (or seed params)Procedural ASCII animationreferences/effects.md
HybridVideo + audioASCII video with audio-reactive overlaysBoth input refs
Lyrics/textAudio + text/SRTTimed text with visual effectsreferences/inputs.md § Text/Lyrics
TTS narrationText quotes + TTS APINarrated testimonial/quote video with typed textreferences/inputs.md § TTS Integration

Stack

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

LayerToolPurpose
CorePython 3.10+, NumPyMath, array ops, vectorized effects
SignalSciPyFFT, peak detection (audio modes)
ImagingPillow (PIL)Font rasterization, frame decoding, image I/O
Video I/Offmpeg (CLI)Decode input, encode output, mux audio
Parallelconcurrent.futuresN workers for batch/clip rendering
TTSElevenLabs API (optional)Generate narration clips
OptionalOpenCVVideo frame sampling, edge detection

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

DimensionOptionsReference
Character paletteDensity ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), project-specificarchitecture.md § Palettes
Color strategyHSV, OKLAB/OKLCH, discrete RGB palettes, auto-generated harmony, monochrome, temperaturearchitecture.md § Color System
Background textureSine fields, fBM noise, domain warp, voronoi, reaction-diffusion, cellular automata, videoeffects.md
Primary effectsRings, spirals, tunnel, vortex, waves, interference, aurora, fire, SDFs, strange attractorseffects.md
ParticlesSparks, snow, rain, bubbles, runes, orbits, flocking boids, flow-field followers, trailseffects.md § Particles
Shader moodRetro CRT, clean modern, glitch art, cinematic, dreamy, industrial, psychedelicshaders.md
Grid densityxs(8px) through xxl(40px), mixed per layerarchitecture.md § Grid System
Coordinate spaceCartesian, polar, tiled, rotated, fisheye, Möbius, domain-warpedeffects.md § Transforms
FeedbackZoom tunnel, rainbow trails, ghostly echo, rotating mandala, color evolutioncomposition.md § Feedback
MaskingCircle, ring, gradient, text stencil, animated iris/wipe/dissolvecomposition.md § Masking
TransitionsCrossfade, wipe, dissolve, glitch cut, iris, mask-based revealshaders.md § Transitions

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 profilereferences/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 pipelineShaderChain + 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

ComponentBudget
Feature extraction1-5ms
Effect function2-15ms
Character render80-150ms (bottleneck)
Shader pipeline5-25ms
Total~100-200ms/frame

References

FileContents
references/architecture.mdGrid system, resolution presets, font selection, character palettes (20+), color system (HSV + OKLAB + discrete RGB + harmony generation), _render_vf() helper, GridLayer class
references/composition.mdPixel blend modes (20 modes), blend_canvas(), multi-grid composition, adaptive tonemap(), FeedbackBuffer, PixelBlendStack, masking/stencil system
references/effects.mdEffect building blocks: value field generators, hue fields, noise/fBM/domain warp, voronoi, reaction-diffusion, cellular automata, SDFs, strange attractors, particle systems, coordinate transforms, temporal coherence
references/shaders.mdShaderChain, _apply_shader_step() dispatch, 38 shader catalog, audio-reactive scaling, transitions, tint presets, output format encoding, terminal rendering
references/scenes.mdScene protocol, Renderer class, SCENES table, render_clip(), beat-synced cutting, parallel rendering, design patterns (layer hierarchy, directional arcs, visual metaphors, compositional techniques), complete scene examples at every complexity level, scene design checklist
references/inputs.mdAudio analysis (FFT, bands, beats), video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing)
references/optimization.mdHardware detection, quality profiles, vectorized patterns, parallel rendering, memory management, performance budgets
references/troubleshooting.mdNumPy broadcasting traps, blend mode pitfalls, multiprocessing/pickling, brightness diagnostics, ffmpeg issues, font problems, common mistakes

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")

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

Frequently asked questions about ASCII Video Production

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