
ASCII Video Production
FreeTransform videos into striking ASCII art animations.
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 source1. Install with the skills CLI
npx skills add nousresearch/hermes-agent/ascii-video --agent claude-code2. 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 nousresearchASCII 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
| Mode | Input | Output | Reference |
|---|---|---|---|
| Video-to-ASCII | Video file | ASCII recreation of source footage | references/inputs.md § Video Sampling |
| Audio-reactive | Audio file | Generative visuals driven by audio features | references/inputs.md § Audio Analysis |
| Generative | None (or seed params) | Procedural ASCII animation | references/effects.md |
| Hybrid | Video + audio | ASCII video with audio-reactive overlays | Both input refs |
| Lyrics/text | Audio + text/SRT | Timed text with visual effects | references/inputs.md § Text/Lyrics |
| TTS narration | Text quotes + TTS API | Narrated testimonial/quote video with typed text | references/inputs.md § TTS Integration |
Stack
Single self-contained Python script per project. No GPU required.
| Layer | Tool | Purpose |
|---|---|---|
| Core | Python 3.10+, NumPy | Math, array ops, vectorized effects |
| Signal | SciPy | FFT, peak detection (audio modes) |
| Imaging | Pillow (PIL) | Font rasterization, frame decoding, image I/O |
| Video I/O | ffmpeg (CLI) | Decode input, encode output, mux audio |
| Parallel | concurrent.futures | N workers for batch/clip rendering |
| TTS | ElevenLabs API (optional) | Generate narration clips |
| Optional | OpenCV | Video frame sampling, edge detection |
Pipeline Architecture
Every mode follows the same 6-stage pipeline:
INPUT → ANALYZE → SCENE_FN → TONEMAP → SHADE → ENCODE
- INPUT — Load/decode source material (video frames, audio samples, images, or nothing)
- ANALYZE — Extract per-frame features (audio bands, video luminance/edges, motion vectors)
- SCENE_FN — Scene function renders to pixel canvas (
uint8 H,W,3). Composes multiple character grids via_render_vf()+ pixel blend modes. Seereferences/composition.md - TONEMAP — Percentile-based adaptive brightness normalization. See
references/composition.md§ Adaptive Tonemap - SHADE — Post-processing via
ShaderChain+FeedbackBuffer. Seereferences/shaders.md - ENCODE — Pipe raw RGB frames to ffmpeg for H.264/GIF encoding
Creative Direction
Aesthetic Dimensions
| Dimension | Options | Reference |
|---|---|---|
| Character palette | Density ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), project-specific | architecture.md § Palettes |
| Color strategy | HSV, OKLAB/OKLCH, discrete RGB palettes, auto-generated harmony, monochrome, temperature | architecture.md § Color System |
| Background texture | Sine fields, fBM noise, domain warp, voronoi, reaction-diffusion, cellular automata, video | effects.md |
| Primary effects | Rings, spirals, tunnel, vortex, waves, interference, aurora, fire, SDFs, strange attractors | effects.md |
| Particles | Sparks, snow, rain, bubbles, runes, orbits, flocking boids, flow-field followers, trails | effects.md § Particles |
| Shader mood | Retro CRT, clean modern, glitch art, cinematic, dreamy, industrial, psychedelic | shaders.md |
| Grid density | xs(8px) through xxl(40px), mixed per layer | architecture.md § Grid System |
| Coordinate space | Cartesian, polar, tiled, rotated, fisheye, Möbius, domain-warped | effects.md § Transforms |
| Feedback | Zoom tunnel, rainbow trails, ghostly echo, rotating mandala, color evolution | composition.md § Feedback |
| Masking | Circle, ring, gradient, text stencil, animated iris/wipe/dissolve | composition.md § Masking |
| Transitions | Crossfade, wipe, dissolve, glitch cut, iris, mask-based reveal | shaders.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):
- Hardware detection + quality profile —
references/optimization.md - Input loader — mode-dependent;
references/inputs.md - Feature analyzer — audio FFT, video luminance, or synthetic
- Grid + renderer — multi-density grids with bitmap cache;
references/architecture.md - Character palettes — multiple per project;
references/architecture.md§ Palettes - Color system — HSV + discrete RGB + harmony generation;
references/architecture.md§ Color - Scene functions — each returns
canvas (uint8 H,W,3);references/scenes.md - Tonemap — adaptive brightness normalization;
references/composition.md - Shader pipeline —
ShaderChain+FeedbackBuffer;references/shaders.md - Scene table + dispatcher — time → scene function + config;
references/scenes.md - Parallel encoder — N-worker clip rendering with ffmpeg pipes
- Main — orchestrate full pipeline
Step 4: Quality Verification
- Test frames first: render single frames at key timestamps before full render
- Brightness check:
canvas.mean() > 8for 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
| Component | Budget |
|---|---|
| Feature extraction | 1-5ms |
| Effect function | 2-15ms |
| Character render | 80-150ms (bottleneck) |
| Shader pipeline | 5-25ms |
| Total | ~100-200ms/frame |
References
| File | Contents |
|---|---|
references/architecture.md | Grid system, resolution presets, font selection, character palettes (20+), color system (HSV + OKLAB + discrete RGB + harmony generation), _render_vf() helper, GridLayer class |
references/composition.md | Pixel blend modes (20 modes), blend_canvas(), multi-grid composition, adaptive tonemap(), FeedbackBuffer, PixelBlendStack, masking/stencil system |
references/effects.md | Effect 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.md | ShaderChain, _apply_shader_step() dispatch, 38 shader catalog, audio-reactive scaling, transitions, tint presets, output format encoding, terminal rendering |
references/scenes.md | Scene 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.md | Audio analysis (FFT, bands, beats), video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing) |
references/optimization.md | Hardware detection, quality profiles, vectorized patterns, parallel rendering, memory management, performance budgets |
references/troubleshooting.md | NumPy 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
- Pick a domain unrelated to the visual goal (weather systems, microbiology, architecture, fluid dynamics, textile weaving)
- List its core visual/structural elements (erosion → gradual reveal; mitosis → splitting duplication; weaving → interlocking patterns)
- Map those elements onto ASCII characters and animation patterns
- Synthesize — what does "erosion" or "crystallization" look like in a character grid?
Conceptual Blending
- Name two distinct visual/conceptual spaces (e.g., ocean waves + sheet music)
- Map correspondences (crests = high notes, troughs = rests, foam = staccato)
- Blend selectively — keep the most interesting mappings, discard forced ones
- Develop emergent properties that exist only in the blend
Oblique Strategies
- 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"
- Interpret the directive against the current ASCII animation challenge
- Apply the lateral insight to the visual design before writing code
Frequently asked questions about ASCII Video Production
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