1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。
- 1Password CLIの導入と設定
- 保存済みAPIキーでコマンド実行
- 設定テンプレートへの秘密情報の埋め込み
動画や画像を色付きの文字アートに変換し、音楽に反応するアニメーションも制作します。歌詞やテキストを重ね、MP4やGIF、連番画像として書き出せます。
ASCII video: convert video/audio to colored ASCII MP4/GIF.
動画や画像を、文字で形や明暗を表すASCIIアートの映像に変換します。音楽の強弱や周波数に反応する映像、素材を使わず計算で作るアニメーション、動画と音声反応を組み合わせた表現にも対応します。歌詞やテキストの表示、色や文字の種類の調整、粒子や残像、場面の切り替えを組み合わせ、MP4、GIF、PNGの連番画像に書き出します。
端末画面のような文字表現や、レトロな雰囲気の映像を作りたいときに向いています。音楽用のビジュアライザーや、文字を主役にした実験的な作品にも使えます。制作では雰囲気や色、場面の流れを決めてからPythonスクリプトを作り、主要な時点の静止画で明るさや構図を確認します。
Python 3.10以上、NumPy、Pillow、ffmpegを使用し、音声の分析にはSciPyを使います。GPUは不要で、linux、macos、windowsに対応します。読み上げには任意でElevenLabs APIを利用します。文字の表示可否はフォントに依存するため、使う文字の確認が必要です。
この紹介文は、公開されている SKILL.md をもとに AI(Claude Haiku)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。
損益計算書・貸借対照表・キャッシュフロー計算書を数式で連動させるExcelモデルを作ります。実績と予測の入力、前提を変えた比較、財務三表の整合性確認を段階的に進めます。
Run PyTorch training across GPUs with minimal changes.
日本語の概要は準備中です。原文の説明を表示しています。
Set up Actual Computer (actual.inc) inference in Hermes.
日本語の概要は準備中です。原文の説明を表示しています。
技術が苦手で不満を抱きやすい利用者になりきってアプリを試し、操作の分かりにくさや離脱の原因を発見。実際の改善課題と個人的な不満を分け、修正案をまとめます。
2つのエージェントが別々に加えた変更のGit競合を、双方の差分と意図から中立的に解決。競合箇所ごとの判断理由を記録し、ビルドやテストで統合結果を確認します。