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Agent skill

Prismer Ascii Video

by Prismer-AI in Prismer-AI/PrismerCloud

ASCII video: convert video/audio to colored ASCII MP4/GIF. An agent skill from Prismer-AI/PrismerCloud.

MITAuto-check passedMedia & Creative

Install Prismer Ascii Video

skills CLI
$ npx skills add Prismer-AI/PrismerCloud --skill prismer-ascii-video -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Prismer-AI/PrismerCloud prismer-ascii-video --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sdk/cloud/catalog/skills/prismer-ascii-video .claude/skills/prismer-ascii-video && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
prismer-ascii-video
GitHub stars
1.6k
Used in
2 other repos
Token cost
~3.7k tokens
SKILL.md length
1,680 words
Files
14 (incl. references)
Skills in repo
88
Repo updated
First seen
Licence
MIT

At a glance

ASCII video: convert video/audio to colored ASCII MP4/GIF. An agent skill from Prismer-AI/PrismerCloud.

  • Works in 4 steps: Creative Vision → Technical Design → Build the Script → …
  • Media & Creative work in your project
  • SKILL.md covers When to use, What's inside, Creative Standard and Modes, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Prismer Ascii Video is an agent skill from Prismer-AI/PrismerCloud. ASCII video: convert video/audio to colored ASCII MP4/GIF.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including reference files (for example `PROVENANCE.md`, `README.md` and `provenance/category-DESCRIPTION.md`).

It sits in Media & Creative. The licence is MIT.

When your agent uses it

  • Media & Creative work in your project

Example prompts

  • “/prismer-ascii-video”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Creative Vision
  2. Technical Design
  3. Build the Script
  4. Quality Verification

What it can do on your machine

Read from SKILL.md and the folder at commit 0d240dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Prismer Ascii Video loads about 3.7k tokens when it runs, and up to ~78k if it reads all its reference files. Until then it costs about 20 tokens; SKILL.md has 1,680 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~78k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Prismer-AI/PrismerCloud at commit 0d240dd, republished under its MIT licence (© Prismer-AI). 1,680 words, ~3,748 tokens.

Download SKILL.mdSave it as .claude/skills/prismer-ascii-video/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.
name
prismer-ascii-video
description
ASCII video: convert video/audio to colored ASCII MP4/GIF.
scope
common
category
creative
version
1.0.0
author
SHL0MS, Hermes Agent
license
MIT
platforms
linux, macos, windows
metadata.nativeReplaces
ascii-video
metadata.requiresExplicitGrant
true

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.

Iterate against rendered evidence. Inspect the first render and revise before delivery. 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 and technical correctness are both required. 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
Show full SKILL.md (714 more words)Show less
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:

python
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

© Prismer-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 13 other files (references) in sdk/cloud/catalog/skills/prismer-ascii-video of Prismer-AI/PrismerCloud.

  • SKILL.md
  • LICENSE
  • LICENSE.hermes
  • PROVENANCE.md
  • README.md
  • provenance/category-DESCRIPTION.md
  • references/architecture.md
  • references/composition.md
  • references/effects.md
  • references/inputs.md
  • references/optimization.md
  • references/scenes.md
  • references/shaders.md
  • references/troubleshooting.md

Open the folder on GitHubat commit 0d240dd

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Prismer-AI/PrismerCloud, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Prismer Ascii Video

What does Prismer Ascii Video do?

ASCII video: convert video/audio to colored ASCII MP4/GIF. An agent skill from Prismer-AI/PrismerCloud. Prismer Ascii Video is an agent skill from Prismer-AI/PrismerCloud. ASCII video: convert video/audio to colored ASCII MP4/GIF.

When should I use Prismer Ascii Video?

Prismer Ascii Video fits situations like: media & Creative work in your project.

How do I install Prismer Ascii Video in Claude Code?

Run `npx skills add Prismer-AI/PrismerCloud --skill prismer-ascii-video -a claude-code`. Or copy the skill folder (sdk/cloud/catalog/skills/prismer-ascii-video in Prismer-AI/PrismerCloud) into .claude/skills/prismer-ascii-video in your project. Claude Code loads it when a task matches its description.

How do I install Prismer Ascii Video in Codex?

Run `npx skills add Prismer-AI/PrismerCloud --skill prismer-ascii-video -a codex`. Or copy the skill folder (sdk/cloud/catalog/skills/prismer-ascii-video in Prismer-AI/PrismerCloud) into .agents/skills/prismer-ascii-video in your project. Codex loads it when a task matches its description.

Can I use Prismer Ascii Video in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Prismer-AI/PrismerCloud --skill prismer-ascii-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prismer-ascii-video, .gemini/skills/prismer-ascii-video, .github/skills/prismer-ascii-video and .opencode/skills/prismer-ascii-video in your project.

What does Prismer Ascii Video need to run?

SKILL.md names no scripts, command-line tools or credentials: Prismer Ascii Video is instructions for the agent only. Our summary lists: Python 3.

Does Prismer Ascii Video access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Prismer Ascii Video safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Prismer Ascii Video use?

Prismer Ascii Video is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Prismer Ascii Video use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 74k tokens, read only when the agent opens those files.

What are the alternatives to Prismer Ascii Video?

Skills that share tags, products or a category with Prismer Ascii Video: Guizang Social Cards (op7418/guizang-social-card-skill, 7.4k stars), Weekly Changelog Video (heygen-com/hyperframes, 60k stars), Anthropic Brand Styling (anthropics/skills, 180k stars) and MoneyPrinterTurbo Video Generator (harry0703/MoneyPrinterTurbo, 130k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Prismer Ascii Video?

Prismer-AI (a GitHub organization) maintains it in Prismer-AI/PrismerCloud, which has 1,555 GitHub stars. The repository holds 88 skills in this directory. The repository was last updated on October 11, 2026.

Source: Prismer-AI/PrismerCloud on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.