通过1Password CLI,将保存的密码和API密钥用于命令及配置模板,完成安装、认证、秘密值读取,并通过环境变量向命令传递凭据。
- 安装并配置1Password CLI
- 使用已保存的API密钥运行命令
- 向配置模板填入秘密值
将视频或图片转换为彩色字符艺术,也可制作随音乐变化的动画。支持叠加歌词和文字、调整颜色与字符、组合视觉效果,并导出MP4、GIF或连续图片。
ASCII video: convert video/audio to colored ASCII MP4/GIF.
将视频或图片转换为ASCII艺术视频,用字符表现形状和明暗。支持随音乐强弱和频率变化的画面、无需素材而通过计算生成的动画,以及视频与音频响应相结合的表现形式。可显示歌词和文字,调整颜色与字符种类,组合粒子、残影及场景切换,导出MP4、GIF或PNG图片序列。
适合制作类似终端界面的字符画面、复古风格视频、音乐可视化画面,或以文字为主角的实验作品。制作时先确定氛围、颜色和场景走向,再编写Python脚本,并通过关键时刻的静态画面检查亮度与构图。
使用Python 3.10或更高版本、NumPy、Pillow和ffmpeg,音频分析使用SciPy。无需GPU,支持linux、macos和windows。语音朗读可选用ElevenLabs API。字符能否显示取决于字体,须确认字体支持所用字符。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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.
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.
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.
| 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 |
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 |
Every mode follows the same 6-stage pipeline:
INPUT → ANALYZE → SCENE_FN → TONEMAP → SHADE → ENCODE
uint8 H,W,3). Composes multiple character grids via _render_vf() + pixel blend modes. See references/composition.mdreferences/composition.md § Adaptive TonemapShaderChain + FeedbackBuffer. See references/shaders.md| 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 |
Never use the same config for the entire video. For each section/scene:
For every project, invent at least one of:
Don't just pick from the catalog. The catalog is vocabulary — you write the poem.
Before any code, articulate the creative concept:
Map the user's prompt to aesthetic choices. A "chill lo-fi visualizer" demands different everything from a "glitch cyberpunk data stream."
references/optimization.mdSingle Python file. Components (with references):
references/optimization.mdreferences/inputs.mdreferences/architecture.mdreferences/architecture.md § Palettesreferences/architecture.md § Colorcanvas (uint8 H,W,3); references/scenes.mdreferences/composition.mdShaderChain + FeedbackBuffer; references/shaders.mdreferences/scenes.mdcanvas.mean() > 8 for all ASCII content. If dark, lower gammatonemap(), Not Linear MultipliersThis 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.
macOS Pillow: textbbox() returns wrong height. Use font.getmetrics(): cell_height = ascent + descent. See references/troubleshooting.md.
Never stderr=subprocess.PIPE with long-running ffmpeg — buffer fills at 64KB and deadlocks. Redirect to file. See references/troubleshooting.md.
Not all Unicode chars render in all fonts. Validate palettes at init — render each char, check for blank output. See references/troubleshooting.md.
For segmented videos (quotes, scenes, chapters), render each as a separate clip file for parallel rendering and selective re-rendering. See references/scenes.md.
| Component | Budget |
|---|---|
| Feature extraction | 1-5ms |
| Effect function | 2-15ms |
| Character render | 80-150ms (bottleneck) |
| Shader pipeline | 5-25ms |
| Total | ~100-200ms/frame |
| 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 |
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.
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通过1Password CLI,将保存的密码和API密钥用于命令及配置模板,完成安装、认证、秘密值读取,并通过环境变量向命令传递凭据。
创建用公式联动利润表、资产负债表和现金流量表的 Excel 模型,分阶段填入历史实际数据与预测假设,比较不同情景,并检查三张报表之间的一致性。
以少量修改让现有PyTorch训练代码适用于多GPU或多台机器,指导混合精度、节省内存的DeepSpeed与FSDP配置,以及训练启动方法。
帮助配置Hermes,通过Actual Computer在自己的设备上运行AI模型。支持加密中继与本地连接,涵盖模型选择、响应测试,以及请求报错或空响应的排查。
扮演不擅长技术、容易不满的用户测试应用,发现难懂的操作和导致放弃使用的障碍。将实际可改进的问题与个人抱怨分开,整理截图、修复建议和任务单。
从中立立场解决两个智能体分别修改代码产生的Git冲突,对照双方差异和意图,记录每处冲突的取舍理由,并通过构建或测试验证合并结果。