\\\")`\\n- Avoid sharing one working directory across parallel OpenCode sessions.\\n- Enter may need to be pressed twice to submit in the TUI (once to finalize text, once to send).\\n\\n## Verification\\n\\nSmoke test:\\n\\n```\\nterminal(command=\\\"opencode run 'Respond with exactly: OPENCODE_SMOKE_OK'\\\")\\n```\\n\\nSuccess criteria:\\n- Output includes `OPENCODE_SMOKE_OK`\\n- Command exits without provider/model errors\\n- For code tasks: expected files changed and tests pass\\n\\n## Rules\\n\\n1. Prefer `opencode run` for one-shot automation — it's simpler and doesn't need pty.\\n2. Use interactive background mode only when iteration is needed.\\n3. Always scope OpenCode sessions to a single repo/workdir.\\n4. For long tasks, provide progress updates from `process` logs.\\n5. Report concrete outcomes (files changed, tests, remaining risks).\\n6. Exit interactive sessions with Ctrl+C or kill, never `/exit`.\\n\", \"path\": \"autonomous-ai-agents/opencode/SKILL.md\", \"skill_dir\": \"/home/kuhnn/.hermes/skills/autonomous-ai-agents/opencode\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"Coding-Agent\", \"OpenCode\", \"Autonomous\", \"Refactoring\", \"Code-Review\"], \"related_skills\": [\"claude-code\", \"codex\", \"hermes-agent\"]}}}",
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"content": "{\"success\": true, \"name\": \"architecture-diagram\", \"description\": \"Dark-themed SVG architecture/cloud/infra diagrams as HTML.\", \"tags\": [\"architecture\", \"diagrams\", \"SVG\", \"HTML\", \"visualization\", \"infrastructure\", \"cloud\"], \"related_skills\": [\"concept-diagrams\", \"excalidraw\"], \"content\": \"---\\nname: architecture-diagram\\ndescription: \\\"Dark-themed SVG architecture/cloud/infra diagrams as HTML.\\\"\\nversion: 1.0.0\\nauthor: Cocoon AI (hello@cocoon-ai.com), ported by Hermes Agent\\nlicense: MIT\\ndependencies: []\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [architecture, diagrams, SVG, HTML, visualization, infrastructure, cloud]\\n related_skills: [concept-diagrams, excalidraw]\\n---\\n\\n# Architecture Diagram Skill\\n\\nGenerate professional, dark-themed technical architecture diagrams as standalone HTML files with inline SVG graphics. No external tools, no API keys, no rendering libraries — just write the HTML file and open it in a browser.\\n\\n## Scope\\n\\n**Best suited for:**\\n- Software system architecture (frontend / backend / database layers)\\n- Cloud infrastructure (VPC, regions, subnets, managed services)\\n- Microservice / service-mesh topology\\n- Database + API map, deployment diagrams\\n- Anything with a tech-infra subject that fits a dark, grid-backed aesthetic\\n\\n**Look elsewhere first for:**\\n- Physics, chemistry, math, biology, or other scientific subjects\\n- Physical objects (vehicles, hardware, anatomy, cross-sections)\\n- Floor plans, narrative journeys, educational / textbook-style visuals\\n- Hand-drawn whiteboard sketches (consider `excalidraw`)\\n- Animated explainers (consider an animation skill)\\n\\nIf a more specialized skill is available for the subject, prefer that. If none fits, this skill can also serve as a general SVG diagram fallback — the output will just carry the dark tech aesthetic described below.\\n\\nBased on [Cocoon AI's architecture-diagram-generator](https://github.com/Cocoon-AI/architecture-diagram-generator) (MIT).\\n\\n## Workflow\\n\\n1. User describes their system architecture (components, connections, technologies)\\n2. Generate the HTML file following the design system below\\n3. Save with `write_file` to a `.html` file (e.g. `~/architecture-diagram.html`)\\n4. User opens in any browser — works offline, no dependencies\\n\\n### Output Location\\n\\nSave diagrams to a user-specified path, or default to the current working directory:\\n```\\n./[project-name]-architecture.html\\n```\\n\\n### Preview\\n\\nAfter saving, suggest the user open it:\\n```bash\\n# macOS\\nopen ./my-architecture.html\\n# Linux\\nxdg-open ./my-architecture.html\\n```\\n\\n## Design System & Visual Language\\n\\n### Color Palette (Semantic Mapping)\\n\\nUse specific `rgba` fills and hex strokes to categorize components:\\n\\n| Component Type | Fill (rgba) | Stroke (Hex) |\\n| :--- | :--- | :--- |\\n| **Frontend** | `rgba(8, 51, 68, 0.4)` | `#22d3ee` (cyan-400) |\\n| **Backend** | `rgba(6, 78, 59, 0.4)` | `#34d399` (emerald-400) |\\n| **Database** | `rgba(76, 29, 149, 0.4)` | `#a78bfa` (violet-400) |\\n| **AWS/Cloud** | `rgba(120, 53, 15, 0.3)` | `#fbbf24` (amber-400) |\\n| **Security** | `rgba(136, 19, 55, 0.4)` | `#fb7185` (rose-400) |\\n| **Message Bus** | `rgba(251, 146, 60, 0.3)` | `#fb923c` (orange-400) |\\n| **External** | `rgba(30, 41, 59, 0.5)` | `#94a3b8` (slate-400) |\\n\\n### Typography & Background\\n- **Font:** JetBrains Mono (Monospace), loaded from Google Fonts\\n- **Sizes:** 12px (Names), 9px (Sublabels), 8px (Annotations), 7px (Tiny labels)\\n- **Background:** Slate-950 (`#020617`) with a subtle 40px grid pattern\\n\\n```svg\\n\\n\\n \\n\\n```\\n\\n## Technical Implementation Details\\n\\n### Component Rendering\\nComponents are rounded rectangles (`rx=\\\"6\\\"`) with 1.5px strokes. To prevent arrows from showing through semi-transparent fills, use a **double-rect masking technique**:\\n1. Draw an opaque background rect (`#0f172a`)\\n2. Draw the semi-transparent styled rect on top\\n\\n### Connection Rules\\n- **Z-Order:** Draw arrows *early* in the SVG (after the grid) so they render behind component boxes\\n- **Arrowheads:** Defined via SVG markers\\n- **Security Flows:** Use dashed lines in rose color (`#fb7185`)\\n- **Boundaries:**\\n - *Security Groups:* Dashed (`4,4`), rose color\\n - *Regions:* Large dashed (`8,4`), amber color, `rx=\\\"12\\\"`\\n\\n### Spacing & Layout Logic\\n- **Standard Height:** 60px (Services); 80-120px (Large components)\\n- **Vertical Gap:** Minimum 40px between components\\n- **Message Buses:** Must be placed *in the gap* between services, not overlapping them\\n- **Legend Placement:** **CRITICAL.** Must be placed outside all boundary boxes. Calculate the lowest Y-coordinate of all boundaries and place the legend at least 20px below it.\\n\\n## Document Structure\\n\\nThe generated HTML file follows a four-part layout:\\n1. **Header:** Title with a pulsing dot indicator and subtitle\\n2. **Main SVG:** The diagram contained within a rounded border card\\n3. **Summary Cards:** A grid of three cards below the diagram for high-level details\\n4. **Footer:** Minimal metadata\\n\\n### Info Card Pattern\\n```html\\n\\n \\n
\\n - • Item one
\\n - • Item two
\\n
\\n
` tags. Fetch the page with curl, then extract art with a small Python snippet.\\n\\n**URL pattern:** `https://ascii.co.uk/art/{subject}`\\n\\n**Step 1 — Fetch the page:**\\n\\n```bash\\ncurl -s 'https://ascii.co.uk/art/cat' -o /tmp/ascii_art.html\\n```\\n\\n**Step 2 — Extract art from pre tags:**\\n\\n```python\\nimport re, html\\nwith open('/tmp/ascii_art.html') as f:\\n text = f.read()\\narts = re.findall(r']*>(.*?)', text, re.DOTALL)\\nfor art in arts:\\n clean = re.sub(r'<[^>]+>', '', art)\\n clean = html.unescape(clean).strip()\\n if len(clean) > 30:\\n print(clean)\\n print('\\\\n---\\\\n')\\n```\\n\\n**Available subjects** (use as URL path):\\n- Animals: `cat`, `dog`, `horse`, `bird`, `fish`, `dragon`, `snake`, `rabbit`, `elephant`, `dolphin`, `butterfly`, `owl`, `wolf`, `bear`, `penguin`, `turtle`\\n- Objects: `car`, `ship`, `airplane`, `rocket`, `guitar`, `computer`, `coffee`, `beer`, `cake`, `house`, `castle`, `sword`, `crown`, `key`\\n- Nature: `tree`, `flower`, `sun`, `moon`, `star`, `mountain`, `ocean`, `rainbow`\\n- Characters: `skull`, `robot`, `angel`, `wizard`, `pirate`, `ninja`, `alien`\\n- Holidays: `christmas`, `halloween`, `valentine`\\n\\n**Tips:**\\n- Preserve artist signatures/initials — important etiquette\\n- Multiple art pieces per page — pick the best one for the user\\n- Works reliably via curl, no JavaScript needed\\n\\n### Source B: GitHub Octocat API (fun easter egg)\\n\\nReturns a random GitHub Octocat with a wise quote. No auth needed.\\n\\n```bash\\ncurl -s https://api.github.com/octocat\\n```\\n\\n## Tool 8: Fun ASCII Utilities (via curl)\\n\\nThese free services return ASCII art directly — great for fun extras.\\n\\n### QR Codes as ASCII Art\\n\\n```bash\\ncurl -s \\\"qrenco.de/Hello+World\\\"\\ncurl -s \\\"qrenco.de/https://example.com\\\"\\n```\\n\\n### Weather as ASCII Art\\n\\n```bash\\ncurl -s \\\"wttr.in/London\\\" # Full weather report with ASCII graphics\\ncurl -s \\\"wttr.in/Moon\\\" # Moon phase in ASCII art\\ncurl -s \\\"v2.wttr.in/London\\\" # Detailed version\\n```\\n\\n## Tool 9: LLM-Generated Custom Art (Fallback)\\n\\nWhen tools above don't have what's needed, generate ASCII art directly using these Unicode characters:\\n\\n### Character Palette\\n\\n**Box Drawing:** `╔ ╗ ╚ ╝ ║ ═ ╠ ╣ ╦ ╩ ╬ ┌ ┐ └ ┘ │ ─ ├ ┤ ┬ ┴ ┼ ╭ ╮ ╰ ╯`\\n\\n**Block Elements:** `░ ▒ ▓ █ ▄ ▀ ▌ ▐ ▖ ▗ ▘ ▝ ▚ ▞`\\n\\n**Geometric & Symbols:** `◆ ◇ ◈ ● ○ ◉ ■ □ ▲ △ ▼ ▽ ★ ☆ ✦ ✧ ◀ ▶ ◁ ▷ ⬡ ⬢ ⌂`\\n\\n### Rules\\n\\n- Max width: 60 characters per line (terminal-safe)\\n- Max height: 15 lines for banners, 25 for scenes\\n- Monospace only: output must render correctly in fixed-width fonts\\n\\n## Decision Flow\\n\\n1. **Text as a banner** → pyfiglet if installed, otherwise asciified API via curl\\n2. **Wrap a message in fun character art** → cowsay\\n3. **Add decorative border/frame** → boxes (can combine with pyfiglet/asciified)\\n4. **Art of a specific thing** (cat, rocket, dragon) → ascii.co.uk via curl + parsing\\n5. **Convert an image to ASCII** → ascii-image-converter or jp2a\\n6. **QR code** → qrenco.de via curl\\n7. **Weather/moon art** → wttr.in via curl\\n8. **Something custom/creative** → LLM generation with Unicode palette\\n9. **Any tool not installed** → install it, or fall back to next option\\n\", \"path\": \"creative/ascii-art/SKILL.md\", \"skill_dir\": \"/home/kuhnn/.hermes/skills/creative/ascii-art\", \"linked_files\": null, \"usage_hint\": null, \"required_environment_variables\": [], \"required_commands\": [], \"missing_required_environment_variables\": [], \"missing_credential_files\": [], \"missing_required_commands\": [], \"setup_needed\": false, \"setup_skipped\": false, \"readiness_status\": \"available\", \"metadata\": {\"hermes\": {\"tags\": [\"ASCII\", \"Art\", \"Banners\", \"Creative\", \"Unicode\", \"Text-Art\", \"pyfiglet\", \"figlet\", \"cowsay\", \"boxes\"], \"related_skills\": [\"excalidraw\"]}}}",
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"content": "{\"success\": true, \"name\": \"ascii-video\", \"description\": \"ASCII video: convert video/audio to colored ASCII MP4/GIF.\", \"tags\": [], \"related_skills\": [], \"content\": \"---\\nname: ascii-video\\ndescription: \\\"ASCII video: convert video/audio to colored ASCII MP4/GIF.\\\"\\nplatforms: [linux, macos, windows]\\n---\\n\\n# ASCII Video Production Pipeline\\n\\n## When to use\\n\\nUse 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.\\n\\n## What's inside\\n\\nProduction 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.\\n\\n## Creative Standard\\n\\nThis is visual art. ASCII characters are the medium; cinema is the standard.\\n\\n**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.\\n\\n**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.\\n\\n**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.\\n\\n**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.\\n\\n**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.\\n\\n**Dense, layered, considered.** Every frame should reward viewing. Never flat black backgrounds. Always multi-grid composition. Always per-scene variation. Always intentional color.\\n\\n## Modes\\n\\n| Mode | Input | Output | Reference |\\n|------|-------|--------|-----------|\\n| **Video-to-ASCII** | Video file | ASCII recreation of source footage | `references/inputs.md` § Video Sampling |\\n| **Audio-reactive** | Audio file | Generative visuals driven by audio features | `references/inputs.md` § Audio Analysis |\\n| **Generative** | None (or seed params) | Procedural ASCII animation | `references/effects.md` |\\n| **Hybrid** | Video + audio | ASCII video with audio-reactive overlays | Both input refs |\\n| **Lyrics/text** | Audio + text/SRT | Timed text with visual effects | `references/inputs.md` § Text/Lyrics |\\n| **TTS narration** | Text quotes + TTS API | Narrated testimonial/quote video with typed text | `references/inputs.md` § TTS Integration |\\n\\n## Stack\\n\\nSingle self-contained Python script per project. No GPU required.\\n\\n| Layer | Tool | Purpose |\\n|-------|------|---------|\\n| Core | Python 3.10+, NumPy | Math, array ops, vectorized effects |\\n| Signal | SciPy | FFT, peak detection (audio modes) |\\n| Imaging | Pillow (PIL) | Font rasterization, frame decoding, image I/O |\\n| Video I/O | ffmpeg (CLI) | Decode input, encode output, mux audio |\\n| Parallel | concurrent.futures | N workers for batch/clip rendering |\\n| TTS | ElevenLabs API (optional) | Generate narration clips |\\n| Optional | OpenCV | Video frame sampling, edge detection |\\n\\n## Pipeline Architecture\\n\\nEvery mode follows the same 6-stage pipeline:\\n\\n```\\nINPUT → ANALYZE → SCENE_FN → TONEMAP → SHADE → ENCODE\\n```\\n\\n1. **INPUT** — Load/decode source material (video frames, audio samples, images, or nothing)\\n2. **ANALYZE** — Extract per-frame features (audio bands, video luminance/edges, motion vectors)\\n3. **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`\\n4. **TONEMAP** — Percentile-based adaptive brightness normalization. See `references/composition.md` § Adaptive Tonemap\\n5. **SHADE** — Post-processing via `ShaderChain` + `FeedbackBuffer`. See `references/shaders.md`\\n6. **ENCODE** — Pipe raw RGB frames to ffmpeg for H.264/GIF encoding\\n\\n## Creative Direction\\n\\n### Aesthetic Dimensions\\n\\n| Dimension | Options | Reference |\\n|-----------|---------|-----------|\\n| **Character palette** | Density ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), project-specific | `architecture.md` § Palettes |\\n| **Color strategy** | HSV, OKLAB/OKLCH, discrete RGB palettes, auto-generated harmony, monochrome, temperature | `architecture.md` § Color System |\\n| **Background texture** | Sine fields, fBM noise, domain warp, voronoi, reaction-diffusion, cellular automata, video | `effects.md` |\\n| **Primary effects** | Rings, spirals, tunnel, vortex, waves, interference, aurora, fire, SDFs, strange attractors | `effects.md` |\\n| **Particles** | Sparks, snow, rain, bubbles, runes, orbits, flocking boids, flow-field followers, trails | `effects.md` § Particles |\\n| **Shader mood** | Retro CRT, clean modern, glitch art, cinematic, dreamy, industrial, psychedelic | `shaders.md` |\\n| **Grid density** | xs(8px) through xxl(40px), mixed per layer | `architecture.md` § Grid System |\\n| **Coordinate space** | Cartesian, polar, tiled, rotated, fisheye, Möbius, domain-warped | `effects.md` § Transforms |\\n| **Feedback** | Zoom tunnel, rainbow trails, ghostly echo, rotating mandala, color evolution | `composition.md` § Feedback |\\n| **Masking** | Circle, ring, gradient, text stencil, animated iris/wipe/dissolve | `composition.md` § Masking |\\n| **Transitions** | Crossfade, wipe, dissolve, glitch cut, iris, mask-based reveal | `shaders.md` § Transitions |\\n\\n### Per-Section Variation\\n\\nNever use the same config for the entire video. For each section/scene:\\n- **Different background effect** (or compose 2-3)\\n- **Different character palette** (match the mood)\\n- **Different color strategy** (or at minimum a different hue)\\n- **Vary shader intensity** (more bloom during peaks, more grain during quiet)\\n- **Different particle types** if particles are active\\n\\n### Project-Specific Invention\\n\\nFor every project, invent at least one of:\\n- A custom character palette matching the theme\\n- A custom background effect (combine/modify existing building blocks)\\n- A custom color palette (discrete RGB set matching the brand/mood)\\n- A custom particle character set\\n- A novel scene transition or visual moment\\n\\nDon't just pick from the catalog. The catalog is vocabulary — you write the poem.\\n\\n## Workflow\\n\\n### Step 1: Creative Vision\\n\\nBefore any code, articulate the creative concept:\\n\\n- **Mood/atmosphere**: What should the viewer feel? Energetic, meditative, chaotic, elegant, ominous?\\n- **Visual story**: What happens over the duration? Build tension? Transform? Dissolve?\\n- **Color world**: Warm/cool? Monochrome? Neon? Earth tones? What's the dominant hue?\\n- **Character texture**: Dense data? Sparse stars? Organic dots? Geometric blocks?\\n- **What makes THIS different**: What's the one thing that makes this project unique?\\n- **Emotional arc**: How do scenes progress? Open with energy, build to climax, resolve?\\n\\nMap the user's prompt to aesthetic choices. A \\\"chill lo-fi visualizer\\\" demands different everything from a \\\"glitch cyberpunk data stream.\\\"\\n\\n### Step 2: Technical Design\\n\\n- **Mode** — which of the 6 modes above\\n- **Resolution** — landscape 1920x1080 (default), portrait 1080x1920, square 1080x1080 @ 24fps\\n- **Hardware detection** — auto-detect cores/RAM, set quality profile. See `references/optimization.md`\\n- **Sections** — map timestamps to scene functions, each with its own effect/palette/color/shader config\\n- **Output format** — MP4 (default), GIF (640x360 @ 15fps), PNG sequence\\n\\n### Step 3: Build the Script\\n\\nSingle Python file. Components (with references):\\n\\n1. **Hardware detection + quality profile** — `references/optimization.md`\\n2. **Input loader** — mode-dependent; `references/inputs.md`\\n3. **Feature analyzer** — audio FFT, video luminance, or synthetic\\n4. **Grid + renderer** — multi-density grids with bitmap cache; `references/architecture.md`\\n5. **Character palettes** — multiple per project; `references/architecture.md` § Palettes\\n6. **Color system** — HSV + discrete RGB + harmony generation; `references/architecture.md` § Color\\n7. **Scene functions** — each returns `canvas (uint8 H,W,3)`; `references/scenes.md`\\n8. **Tonemap** — adaptive brightness normalization; `references/composition.md`\\n9. **Shader pipeline** — `ShaderChain` + `FeedbackBuffer`; `references/shaders.md`\\n10. **Scene table + dispatcher** — time → scene function + config; `references/scenes.md`\\n11. **Parallel encoder** — N-worker clip rendering with ffmpeg pipes\\n12. **Main** — orchestrate full pipeline\\n\\n### Step 4: Quality Verification\\n\\n- **Test frames first**: render single frames at key timestamps before full render\\n- **Brightness check**: `canvas.mean() > 8` for all ASCII content. If dark, lower gamma\\n- **Visual coherence**: do all scenes feel like they belong to the same video?\\n- **Creative vision check**: does the output match the concept from Step 1? If it looks generic, go back\\n\\n## Critical Implementation Notes\\n\\n### Brightness — Use `tonemap()`, Not Linear Multipliers\\n\\nThis is the #1 visual issue. ASCII on black is inherently dark. **Never use `canvas * N` multipliers** — they clip highlights. Use adaptive tonemap:\\n\\n```python\\ndef tonemap(canvas, gamma=0.75):\\n f = canvas.astype(np.float32)\\n lo, hi = np.percentile(f[::4, ::4], [1, 99.5])\\n if hi - lo < 10: hi = lo + 10\\n f = np.clip((f - lo) / (hi - lo), 0, 1) ** gamma\\n return (f * 255).astype(np.uint8)\\n```\\n\\nPipeline: `scene_fn() → tonemap() → FeedbackBuffer → ShaderChain → ffmpeg`\\n\\nPer-scene gamma: default 0.75, solarize 0.55, posterize 0.50, bright scenes 0.85. Use `screen` blend (not `overlay`) for dark layers.\\n\\n### Font Cell Height\\n\\nmacOS Pillow: `textbbox()` returns wrong height. Use `font.getmetrics()`: `cell_height = ascent + descent`. See `references/troubleshooting.md`.\\n\\n### ffmpeg Pipe Deadlock\\n\\nNever `stderr=subprocess.PIPE` with long-running ffmpeg — buffer fills at 64KB and deadlocks. Redirect to file. See `references/troubleshooting.md`.\\n\\n### Font Compatibility\\n\\nNot all Unicode chars render in all fonts. Validate palettes at init — render each char, check for blank output. See `references/troubleshooting.md`.\\n\\n### Per-Clip Architecture\\n\\nFor segmented videos (quotes, scenes, chapters), render each as a separate clip file for parallel rendering and selective re-rendering. See `references/scenes.md`.\\n\\n## Performance Targets\\n\\n| Component | Budget |\\n|-----------|--------|\\n| Feature extraction | 1-5ms |\\n| Effect function | 2-15ms |\\n| Character render | 80-150ms (bottleneck) |\\n| Shader pipeline | 5-25ms |\\n| **Total** | ~100-200ms/frame |\\n\\n## References\\n\\n| File | Contents |\\n|------|----------|\\n| `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 |\\n| `references/composition.md` | Pixel blend modes (20 modes), `blend_canvas()`, multi-grid composition, adaptive `tonemap()`, `FeedbackBuffer`, `PixelBlendStack`, masking/stencil system |\\n| `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 |\\n| `references/shaders.md` | `ShaderChain`, `_apply_shader_step()` dispatch, 38 shader catalog, audio-reactive scaling, transitions, tint presets, output format encoding, terminal rendering |\\n| `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 |\\n| `references/inputs.md` | Audio analysis (FFT, bands, beats), video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing) |\\n| `references/optimization.md` | Hardware detection, quality profiles, vectorized patterns, parallel rendering, memory management, performance budgets |\\n| `references/troubleshooting.md` | NumPy broadcasting traps, blend mode pitfalls, multiprocessing/pickling, brightness diagnostics, ffmpeg issues, font problems, common mistakes |\\n\\n---\\n\\n## Creative Divergence (use only when user requests experimental/creative/unique output)\\n\\nIf the user asks for creative, experimental, surprising, or unconventional output, select the strategy that best fits and reason through its steps BEFORE generating code.\\n\\n- **Forced Connections** — when the user wants cross-domain inspiration (\\\"make it look organic,\\\" \\\"industrial aesthetic\\\")\\n- **Conceptual Blending** — when the user names two things to combine (\\\"ocean meets music,\\\" \\\"space + calligraphy\\\")\\n- **Oblique Strategies** — when the user is maximally open (\\\"surprise me,\\\" \\\"something I've never seen\\\")\\n\\n### Forced Connections\\n1. Pick a domain unrelated to the visual goal (weather systems, microbiology, architecture, fluid dynamics, textile weaving)\\n2. List its core visual/structural elements (erosion → gradual reveal; mitosis → splitting duplication; weaving → interlocking patterns)\\n3. Map those elements onto ASCII characters and animation patterns\\n4. Synthesize — what does \\\"erosion\\\" or \\\"crystallization\\\" look like in a character grid?\\n\\n### Conceptual Blending\\n1. Name two distinct visual/conceptual spaces (e.g., ocean waves + sheet music)\\n2. Map correspondences (crests = high notes, troughs = rests, foam = staccato)\\n3. Blend selectively — keep the most interesting mappings, discard forced ones\\n4. Develop emergent properties that exist only in the blend\\n\\n### Oblique Strategies\\n1. 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\\\"\\n2. Interpret the directive against the current ASCII animation challenge\\n3. 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"role": "tool",
"name": "skill_view",
"content": "{\"success\": true, \"name\": \"baoyu-comic\", \"description\": \"Knowledge comics (知识漫画): educational, biography, tutorial.\", \"tags\": [\"comic\", \"knowledge-comic\", \"creative\", \"image-generation\"], \"related_skills\": [], \"content\": \"---\\nname: baoyu-comic\\ndescription: \\\"Knowledge comics (知识漫画): educational, biography, tutorial.\\\"\\nversion: 1.56.1\\nauthor: 宝玉 (JimLiu)\\nlicense: MIT\\nplatforms: [linux, macos, windows]\\nmetadata:\\n hermes:\\n tags: [comic, knowledge-comic, creative, image-generation]\\n homepage: https://github.com/JimLiu/baoyu-skills#baoyu-comic\\n---\\n\\n# Knowledge Comic Creator\\n\\nAdapted from [baoyu-comic](https://github.com/JimLiu/baoyu-skills) for Hermes Agent's tool ecosystem.\\n\\nCreate original knowledge comics with flexible art style × tone combinations.\\n\\n## When to Use\\n\\nTrigger this skill when the user asks to create a knowledge/educational comic, biography comic, tutorial comic, or uses terms like \\\"知识漫画\\\", \\\"教育漫画\\\", or \\\"Logicomix-style\\\". The user provides content (text, file path, URL, or topic) and optionally specifies art style, tone, layout, aspect ratio, or language.\\n\\n## Reference Images\\n\\nHermes' `image_generate` tool is **prompt-only** — it accepts a text prompt and an aspect ratio, and returns an image URL. It does **NOT** accept reference images. When the user supplies a reference image, use it to **extract traits in text** that get embedded in every page prompt:\\n\\n**Intake**: Accept file paths when the user provides them (or pastes images in conversation).\\n- File path(s) → copy to `refs/NN-ref-{slug}.{ext}` alongside the comic output for provenance\\n- Pasted image with no path → ask the user for the path via `clarify`, or extract style traits verbally as a text fallback\\n- No reference → skip this section\\n\\n**Usage modes** (per reference):\\n\\n| Usage | Effect |\\n|-------|--------|\\n| `style` | Extract style traits (line treatment, texture, mood) and append to every page's prompt body |\\n| `palette` | Extract hex colors and append to every page's prompt body |\\n| `scene` | Extract scene composition or subject notes and append to the relevant page(s) |\\n\\n**Record in each page's prompt frontmatter** when refs exist:\\n\\n```yaml\\nreferences:\\n - ref_id: 01\\n filename: 01-ref-scene.png\\n usage: style\\n traits: \\\"muted earth tones, soft-edged ink wash, low-contrast backgrounds\\\"\\n```\\n\\nCharacter consistency is driven by **text descriptions** in `characters/characters.md` (written in Step 3) that get embedded inline in every page prompt (Step 5). The optional PNG character sheet generated in Step 7.1 is a human-facing review artifact, not an input to `image_generate`.\\n\\n## Options\\n\\n### Visual Dimensions\\n\\n| Option | Values | Description |\\n|--------|--------|-------------|\\n| Art | ligne-claire (default), manga, realistic, ink-brush, chalk, minimalist | Art style / rendering technique |\\n| Tone | neutral (default), warm, dramatic, romantic, energetic, vintage, action | Mood / atmosphere |\\n| Layout | standard (default), cinematic, dense, splash, mixed, webtoon, four-panel | Panel arrangement |\\n| Aspect | 3:4 (default, portrait), 4:3 (landscape), 16:9 (widescreen) | Page aspect ratio |\\n| Language | auto (default), zh, en, ja, etc. | Output language |\\n| Refs | File paths | Reference images used for style / palette trait extraction (not passed to the image model). See [Reference Images](#reference-images) above. |\\n\\n### Partial Workflow Options\\n\\n| Option | Description |\\n|--------|-------------|\\n| Storyboard only | Generate storyboard only, skip prompts and images |\\n| Prompts only | Generate storyboard + prompts, skip images |\\n| Images only | Generate images from existing prompts directory |\\n| Regenerate N | Regenerate specific page(s) only (e.g., `3` or `2,5,8`) |\\n\\nDetails: [references/partial-workflows.md](references/partial-workflows.md)\\n\\n### Art, Tone & Preset Catalogue\\n\\n- **Art styles** (6): `ligne-claire`, `manga`, `realistic`, `ink-brush`, `chalk`, `minimalist`. Full definitions at `references/art-styles/\\n\\n\\n\\n\\n