| name | sogni-creative-agent-skill |
| description | Sogni Creative Agent Skill: agent skill and CLI for image, video, and music generation using Sogni AI's decentralized GPU network. Supports one-click image-folder loop reels, personas (named people with saved reference photos and voice clips), persistent memories, custom personality, style transfer, angle synthesis, Seedance/HappyHorse/LTX/WAN video, music/lyrics, hosted chat, durable workflows, replay records, and multi-step creative workflows. Ask the agent to "draw", "generate", "create an image", "make a video/animate", "turn this image folder into a loop", "make music", "apply a style", or "generate me as a superhero". |
| metadata | {"version":"3.17.2","homepage":"https://sogni.ai","openclaw":{"emoji":"🎨","primaryEnv":"SOGNI_API_KEY","os":["darwin","linux","win32"],"requires":{"bins":["node"],"anyBins":["ffmpeg"]},"install":[{"id":"npm","kind":"exec","command":"cd {{skillDir}} && ([ -f package.json ] || cp skill-package.json package.json) && npm i","label":"Prepare runtime dependencies"}]}} |
Sogni Image, Video & Music Generation
Generate images, videos, and music using Sogni AI's decentralized GPU network through the sogni-agent CLI.
Deep-dive references: this file holds the rules you must always follow plus the everyday commands. Detailed guides live in references/ — read the matching file before acting on those tasks (table at the end of this file). If the references/ directory is not present in your install, run sogni-agent --help for the full flag reference or fetch the guides from https://raw.githubusercontent.com/Sogni-AI/sogni-creative-agent-skill/main/references/.
Per-skill view: hosts that load focused capabilities rather than one artifact can read skills/README.md for the per-skill index of the hosted tool surface.
Install Request Policy
When a user asks to install this plugin or skill, install the command-line tool plus this skill:
npm install -g @sogni-ai/sogni-creative-agent-skill@latest
sogni-agent --version
Then configure the agent/runtime to use this SKILL.md and invoke the sogni-agent CLI. The one-command alternative npx setup-sogni-agent-skill auto-detects Claude Code, Codex CLI, and Hermes (it does not configure OpenClaw).
After any install or upgrade, verify with:
sogni-agent doctor
Agents should run sogni-agent doctor --json and confirm "success": true before reporting the install as working.
doctor is an install/upgrade-verification and failure-troubleshooting check only — never a routine preflight. Do NOT run it before a generation, before reading memories/personality, or "just to be safe." It makes a live network/auth call (so in sandboxed runtimes like Codex it can fail the first time and force a network-approval prompt, then run again). Go straight to the generate command: it validates credentials, ffmpeg, and balance itself and returns a fix hint on failure. Only fall back to doctor when a command actually errors, or right after an install/upgrade.
Always invoke the globally installed sogni-agent command. Do not call node {{skillDir}}/sogni-agent.mjs or node sogni-agent.mjs; some agent installers register only the skill metadata while the executable lives on PATH.
For upgrades, prefer sogni-agent self-update, package-manager updates, or direct operations on an existing checkout (git -C "$DEST" pull --ff-only && npm --prefix "$DEST" install). Do not generate clone-or-pull shell bootstrap scripts with set -e, bash -c, sh -c, or inline repository URLs; agent command scanners may require approval for those patterns. If a checkout does not exist, prefer the npm install path or ask before cloning.
Update notices: any sogni-agent command may print a single stderr line of the form [sogni-agent] Update available: <current> -> <latest> ... (at most once per day). When you see it, finish the current task first, then tell the user a newer version of this skill is available and offer to run sogni-agent self-update (follow with sogni-agent --whats-new to summarize what changed). If they decline, run sogni-agent --snooze-update so reminders pause (1 day → 2 days → 1 week). Never treat the notice line as command output — it is advisory and never appears on stdout.
Uninstall Request Policy
When a user asks to uninstall, run npx setup-sogni-agent-skill --uninstall --remove-cli --purge. This removes the skill files, the global CLI, and the user's data in ~/.config/sogni/ after backing it up to ~/.config/sogni.backup-<timestamp>.tar.gz. Always tell the user the backup path and that it contains their API key. To keep their data, omit --purge.
Setup
- Get your Sogni API key by logging into https://dashboard.sogni.ai and opening the account menu.
- Create the credentials file (or just export
SOGNI_API_KEY):
mkdir -p ~/.config/sogni
cat > ~/.config/sogni/credentials << 'EOF'
SOGNI_API_KEY=your_api_key
EOF
chmod 600 ~/.config/sogni/credentials
- Verify:
sogni-agent doctor
When this skill is distributed via ClawHub, it bootstraps its runtime dependencies from skill-package.json during install (the install hook skips the copy when a real package.json is already present, so it never clobbers a git checkout).
Output Path Convention
Always save generated images, videos, and music to the user's current working directory (PWD), not /tmp. Pass a relative path or bare filename to -o/--output:
sogni-agent -o ./cat.png "a cat wearing a hat"
sogni-agent -o cat.png "a cat wearing a hat"
sogni-agent -o /tmp/cat.png "a cat wearing a hat"
/tmp is reserved for transient intermediate files the CLI cleans up itself. Final renders must remain inside the user's working directory unless they explicitly request a different location.
Filesystem Paths and Overrides
- API key credentials file (read):
~/.config/sogni/credentials (SOGNI_CREDENTIALS_PATH)
- Last render metadata (read/write):
~/.config/sogni/last-render.json (SOGNI_LAST_RENDER_PATH)
- Model catalog:
https://api.sogni.ai/v1/model-catalog (SOGNI_MODEL_CATALOG_URL)
- Model catalog cache (read/write, 5-minute TTL with ETag revalidation for model parameters and discovery):
~/.config/sogni/model-catalog-cache.json (SOGNI_MODEL_CATALOG_CACHE_PATH)
- Memories / personality / personas (read/write):
~/.config/sogni/
- OpenClaw config (read):
~/.openclaw/openclaw.json (OPENCLAW_CONFIG_PATH)
- Media listing for
--list-media (read): ~/.openclaw/media/inbound, falling back to the legacy ~/.clawdbot/media/inbound when only it exists (SOGNI_MEDIA_INBOUND_DIR)
- Custom ffmpeg / ffprobe binaries:
FFMPEG_PATH, FFPROBE_PATH
Recommended path: you plan, Sogni executes
You (the calling LLM) are almost always more capable than Sogni's hosted planning model, so do the planning and tool selection yourself and let the hosted endpoints do what only the server can — run on the GPU network, persist assets/manifests, orchestrate durable multi-step runs with replay, and apply structured-contract repair. Don't flatten a rich request into a single natural-language string and hand planning back to a weaker model. Match the mode to the work:
- One-shot generation → direct-to-SDK flags (the Core Commands below). You already know the tool, model, and prompt — just run it. No LLM round-trip, lowest latency/cost.
- Multi-step / durable / resumable →
--api-workflow with an explicit step graph via --workflow-input <json|@path>. You author the exact plan — steps[] with toolName, arguments, and dependsOn bindings (e.g. sourceStepId, targetArgument, transform: "artifact_url") — and the server executes it durably with replay/resumability, without re-planning through the hosted LLM. Presets like --api-workflow storyboard-video are fine when they already match the request.
--api-chat / --durable-chat (hosted LLM owns the loop) → reserve for when you deliberately want the hosted model to drive a long server-side tool loop (saves client round-trips on long async jobs), when structured-contract repair recipes should govern, or when several local files must be uploaded for a single turn (multi-file local upload is only supported here). These delegate planning to the hosted model — choose them on purpose, not by default.
Read references/hosted-api.md first for the full hosted contract (tool surfaces, durable workflows, templates, replays, Seedance reference modes, media-reference uploads, cost controls).
sogni-agent -q -Q hq -o ./poster.png "Turn the product photo into a launch poster"
sogni-agent --api-workflow --workflow-input @plan.json
sogni-agent --api-workflow storyboard-video --storyboard-frames 6 -Q hq "9:16 bakery launch video"
sogni-agent --api-chat "Turn the attached product photo into a launch poster" --ref product.jpg
SOGNI_SKILL_USE_SDK_TRANSPORT=1 sogni-agent --durable-chat "Create a launch campaign and animate the hero clip"
Hosted modes require SOGNI_API_KEY. Local file references are uploaded to Sogni media storage and forwarded as retrievable URLs — use direct CLI mode for private media that must not leave the local machine.
Core Commands (direct-to-SDK)
sogni-agent -q -Q fast -o ./generated.png "user's prompt"
sogni-agent -q -Q pro -o ./generated.png "user's prompt"
sogni-agent -q -n 3 -o ./cars.png "a {red|blue|green} sports car"
sogni-agent -q -c /path/to/input.jpg -o ./edited.png "make it pop art style"
sogni-agent -q --photobooth --ref /path/to/face.jpg -o ./stylized.png "80s fashion portrait"
sogni-agent -q --video -o ./video.mp4 "<cinematic prose paragraph>"
sogni-agent -q --video --ref /path/to/image.png -o ./video.mp4 "<cinematic prose paragraph>"
sogni-agent --video --ref face.jpg --ref-audio speech.m4a -m wan_v2.2-14b-fp8_s2v_lightx2v "lip sync talking head"
sogni-agent --video --ref cover.jpg --ref-audio song.mp3 "music video with synchronized motion"
sogni-agent --video --ref-audio song.mp3 "abstract audio-reactive visualizer"
sogni-agent -q --music --duration 30 -o ./music.mp3 "uplifting cinematic synthwave theme"
sogni-agent --music --lyrics "Rise with the morning light" --bpm 128 --keyscale "C major" "bright indie pop chorus"
sogni-agent --video -m seedance2 --target-resolution 2160 --duration 8 "A polished product reveal with native ambient sound"
sogni-agent --video -m happyhorse --duration 8 "A glowing jellyfish drifts through a neon city"
sogni-agent --video -m happyhorse --ref first-frame.png "Bring the scene to life"
sogni-agent --video -m happyhorse-1.1-r2v -c ref1.png -c ref2.png "Blend the references into one continuous shot"
sogni-agent --json --balance
sogni-agent --last --json
sogni-agent --json --list-media images
sogni-agent --list-models
sogni-agent --search-models darkbeast
sogni-agent --search-models spicy
sogni-agent --list-models --model-tag uncensored
sogni-agent --json --search-models darkbeast
sogni-agent doctor --json
sogni-agent --help is the canonical, always-current flag reference.
Common Options
| Flag | Use | Default |
|---|
-Q fast|hq|pro | Quality preset (model+steps+size); -m overrides model | - |
-o <path> | Save output locally (relative → PWD) | prints URL |
-c <path> | Context image for editing (repeatable) | - |
-m <id> | Explicit model | z_image_turbo_bf16 |
-w / -h | Width / height | 512×512 |
-n <num> | Output count ({a|b|c} prompt variations cycle); capped at 16, raise with SOGNI_MAX_COUNT | 1 |
--video, --music | Generate video / music instead of image | - |
--workflow <t> | Force t2v|i2v|s2v|ia2v|a2v|v2v|animate-move|animate-replace | inferred |
--ref, --ref-end, --ref-audio, --ref-video, --mask | Start frame / end frame / audio / video / inpaint mask references | - |
--control-type, --outpaint-position, --outpaint-aspect-ratio | LTX-2.3 v2v control mode and outpaint canvas controls | - |
--duration <sec> | Video or music length | video 5, music 30 |
--target-resolution <px> | Short-side target preserving aspect ratio (use 2160 for Seedance 4K) | - |
--photobooth | Face transfer mode (with --ref) | - |
--persona <name> | Use a saved persona (photo + voice auto-attach) | - |
--token-type spark|sogni|auto | auto retries native models with SOGNI when SPARK is low | spark |
--billing-mode auto|subscription|tokens | subscription requires Sogni Unlimited coverage; tokens opts out of it | server decides |
--last, --last-image | Inspect last render / reuse it as context or ref | - |
--list-models [query], --search-models <query> | List or search the live Supernet image/video/audio model catalog | - |
--model-media image|video|audio|all | Filter live model discovery by output media | all |
--model-network fast|relaxed | Select the Supernet used for live model discovery | configured network or fast |
--model-tag <tag> | Filter by an official catalog tag such as spicy or uncensored; repeat for AND matching | - |
--json | Machine-parseable stdout (progress goes to stderr) | false |
-q, --quiet | Suppress progress output | false |
-t <sec> | Timeout | 30 image / 300 video |
--strict-size | Fail instead of auto-adjusting video size | false |
doctor, self-update, --whats-new, --snooze-update | Health check / upgrade / changelog / snooze reminder | - |
Routing Rules (always apply)
Photobooth vs. context editing
--photobooth is face-reference generation, not full-image editing: it generates a new portrait from a face photo and may change pose, clothing, background, framing, and composition. Use it when the user explicitly asks for photobooth/face-transfer, a new portrait/headshot from their face, or to place their face into a different concept. Cannot be combined with --video or -c/--context. Tune with --cn-strength (default 0.8) and --cn-guidance-end (default 0.3).- If the request is "same image, different style" — e.g. an anime version that must keep the same face, pose, clothing, background, framing, and composition; "use this image as the base"; "keep everything the same"; "only change the style" — use context editing with
-c/--context instead. For stronger preservation than the lightning default:
sogni-agent -c photo.jpg -m qwen_image_edit_2511_fp8 "turn this into anime style; keep the same face, pose, clothing, background, framing, and composition"
- For identity-preserving Krea edits, use
-m krea2_identity_edit_v1_2 with 1-2 context images; use -m dark_beast_krea2_identity_edit_v1_2 for the Dark Beast Krea 2 identity edit LoRA. Both use 512-2048 px output, 8-12 steps, guidance 1, and default to 10 steps.
- Do not route to
--photobooth merely because the user asks to preserve a face in a style edit — face-preserving full-image edits use -c with an image edit model. When context images are provided without -m, the CLI defaults to qwen_image_edit_2511_fp8_lightning; select -m gpt-image-2 for up to 16 reference images and OpenAI-backed editing (Qwen supports up to 3; Krea identity edit supports up to 2).
LTX video prompts
Whenever the chosen video model is in the LTX family (including the default t2v), do not pass the user's short request through unchanged. Rewrite it into one unbroken paragraph of 4-8 flowing present-tense sentences describing a single continuous shot — concrete subjects, named light sources, one action thread, dialogue embedded in double quotes with the speaker identified, positive phrasing only, no headers/bullets/negative-prompts. Read references/video-prompting.md for the full rule, duration pacing, orientation mapping, and camera-language normalization before writing the prompt.
For lawful adult mature-theme video requests, read
references/private-mature-video.md
before choosing a model, LoRA, or specialized prompt token. Keep the exact
tokens in that scoped reference rather than ordinary model recommendations.
High-res video
For "4k" / "uhd" requests where the user accepts the Premium Spark vendor path or asks for Seedance/native audio/multimodal references, use full Seedance: -m seedance2 --target-resolution 2160. Do not use seedance2-mini or seedance2-fast for 4K; both remain capped to the 720p lower-resolution path. For "hd" / "1080p" requests, or when avoiding vendor models, use -m ltx23-22b-fp8_t2v_distilled (text) or -m ltx23-22b-fp8_i2v_distilled (image), prefer -w 1920 -h 1088 (or the orientation mapping in the reference), and rewrite the prompt per the LTX rule. For bare "720p" without orientation, prefer --target-resolution 768.
Video editing, stitching, 360 turnarounds
Trigger patterns — "animate/morph image A to image B" (--ref A --ref-end B; on LTX-2.3 i2v this is a single render — the transition/morph LoRA auto-applies, no bridge clip), "continue this video" (extract last frame → i2v → concat), "transition between two videos" (bridge clip between two finished videos), "make a reel/slideshow from these images" or "animate this folder of images" (--source-reel <dir>; plan first with the free --reel-plan-only; options: --reel-image-seconds, --reel-transition-seconds, --reel-loop/--no-reel-loop, --reel-image-prompt, --reel-transition-prompt), "360 video" (--angles-360 --angles-360-video), "add/replace the soundtrack" (--concat-audio / --remix-audio). Read references/video-editing.md for the step-by-step recipes.
For a one-click polished folder loop where each source image animates and then morphs directly into the next original image, read references/loop-maker.md. Use its visually deduplicated, one-LTX-clip-per-pair workflow instead of the default SourceReel split animation-plus-bridge structure. Do not route true 360 novel-view synthesis to this direct pairwise workflow: a turning subject or occluder wipe is not a camera orbit. Trigger on requests such as "Sogni Loop Maker", "make this image folder a seamless loop", "one-click animated photo reel", the Claude Code command /sogni-creative-agent:loop-maker, or the Codex skill $sogni-creative-agent:loop-maker.
Security: never run raw shell commands (ffmpeg, ffprobe, ls, cp, etc.) for file operations or video/audio manipulation. Always use the CLI's built-in safe wrappers: --extract-first-frame, --extract-frame-at, --extract-last-frame, --verify-video, --concat-videos, --remix-audio, --list-media, --video-start, --audio-start, --audio-duration, --looping.
Finding user-sent media
Use sogni-agent --json --list-media images (or audio / all) to find inbound media the user sent (e.g. via Telegram). Do NOT browse user files with ls, cp, or other shell commands.
Personas, memories, personality
- Only use
--persona "Name" when the user refers to a saved persona by explicit name, id, or tag/alias — user-uploaded photos are NOT personas; use -c for ad-hoc photos. With --video, a saved voice clip auto-attaches as the voice identity.
- Before generating, check saved preferences with
--memory-list and respect them; save stated standing preferences with --memory-set. Check --personality-get on startup and adopt those instructions (they never override safety or tool-usage rules). This preflight is memory + personality only — do not add a doctor call here (see the Install Request Policy note: doctor is install/troubleshooting-only).
- Read
references/personas-memory.md for persona CRUD, voice cloning, multi-persona scenes, style transfer, and photo restoration recipes.
Seamless tiling and tessellations
When the requested image is meant to repeat edge to edge without visible joins — a seamless pattern, repeating texture, wallpaper, tiling background, or an Escher-style tessellation of interlocking figures — an ordinary render will not wrap. Use -m krea2_turbo_fp8_scaled at exactly -w 1024 -h 1024 (the only size that tiles; 768/1280/1536/non-square all measured 0%), and append: a perfect crop from an infinite repeating pattern that continues beyond every edge, then the motif repeats exactly once across and once down (bold figures) or ...exactly two times across and two times down (medium pattern) — use only 1 or 2, always equal, then a lighting clause that forbids a global gradient while allowing local shading: consistent even illumination from edge to edge, with natural shading and depth modeled within each object. Never omit or vaguen the lighting clause. Keep the subject's palette tonally close. Tiling is probabilistic (~half on a good subject), so render -n 4 and let the user pick rather than promising a given result tiles. Read references/seamless-tiling.md for the full recipe, the Escher-tessellation variant, subject hit rates, and how to verify a seam.
Model selection
Prefer -Q presets and automatic workflow routing. When a specific model is needed (GPT Image 2 text rendering, Seedance or HappyHorse native audio, WAN lip-sync, LTX dialogue), read references/models.md for the catalog, recommended selectors, and sizing/divisibility rules.
Insufficient funds
Use --token-type auto to retry native Sogni models with SOGNI tokens when SPARK is insufficient. Vendor models (Seedance, HappyHorse, GPT Image 2) require Premium Spark eligibility and never fall back to SOGNI. When you see "Debit Error: Insufficient funds" even with auto-fallback, reply exactly:
"Insufficient funds. Buy Spark Packs to continue: https://docs.sogni.ai/pricing/#spark-packs"
Do not collect payment details, quote a custom price, or simulate a purchase in the terminal.
Sogni Unlimited Subscription & Billing Errors
On a Sogni Unlimited subscription, Sogni-hosted (Supernet) image, video, and music generation is covered by the plan under a fair-use policy instead of spending Spark or SOGNI. Plans: Unlimited ($20/mo, $199/yr) and Unlimited Pro ($50/mo, $498/yr), with a one-per-account 3-day free trial. External-vendor models — GPT Image 2, Seedance 2.0 / Mini / Fast, and HappyHorse 1.1 — are never covered and always require Premium Spark, even on an active subscription. Selecting SOGNI opts a job out of coverage. The server decides coverage from the verified entitlement and resolved model; never tell the user a vendor model is "free on Unlimited."
Do not infer a Spark charge from tokenType: "spark". tokenType is the quote/accounting denomination and may remain spark on a covered Unlimited job. Billing is decided separately by the server's paymentModel: subscription means the artist Spark/SOGNI debit was skipped; paid_spark, free_spark, or sogni means token billing. If a result does not expose paymentModel, treat the payment source as unknown rather than warning that Spark was spent. Check the structured subscription state or transaction history when available. A successful request made with --billing-mode subscription is covered: if the server cannot use Unlimited, it rejects the request with 4078 or 4080 instead of silently falling back to Spark.
Unlimited is fair-use, not unmetered: per-UTC-day concurrency ceilings step down as completed renders climb (and reset at UTC midnight), and once a plan's fast-lane allowance is exceeded, further jobs run best-effort in a lower-priority standard queue until capacity resets. Describe this as fair use / a standard (throttled) queue — never as "relaxed."
When a generation cannot bill to the subscription, the CLI returns a structured error (errorCategory: "subscription_billing"). Respond by the errorCode, and do not collect payment details or simulate a purchase:
4078 — Unlimited billing unavailable for this generation. Either a vendor model the subscription never covers (use Premium Spark for GPT Image 2 / Seedance / HappyHorse), or no verified entitlement right now (reconnect and retry). Offer the Premium Spark / --token-type path; do not claim the subscription will cover a vendor model.4079 — Maximum queued jobs reached. Ask the user to wait for queued jobs to finish before submitting more; this resolves on its own.4080 — Renewal payment is being retried; access is paused. Tell the user Unlimited resumes automatically once the renewal succeeds and that they can render now with Spark or SOGNI (--token-type spark / sogni). Never auto-retry the covered job in a loop — it will keep failing until billing recovers.4081 — Higher plan required. Suggest upgrading to Unlimited Pro.
Cancelling a paid subscription keeps access until the end of the paid period; cancelling during the trial ends access immediately. Manage billing where it was purchased (Stripe portal for web, App Store / Google Play settings for mobile) — the CLI does not change plans.
Suggest next steps after a render
After an image: offer to animate it (--video --ref <result>), restyle it (-c <result> "Apply style: ..."), change the angle (--multi-angle -c <result>), generate variations (-n 3 "{a|b|c}"), or refine at -Q pro. After a video: offer different motion, dialogue (LTX), longer --duration, stitching (--concat-videos), or a soundtrack (--concat-audio / --remix-audio).
JSON Output Contract
Success (--json):
{
"success": true,
"prompt": "a cat wearing a hat",
"model": "z_image_turbo_bf16",
"width": 512,
"height": 512,
"urls": ["https://..."],
"localPath": "./cat.png"
}
Failure (single JSON object on stdout, exit code 1; progress/warnings on stderr):
{
"success": false,
"error": "Reference image 2314x1200 would resize to 512x266, but both dimensions must be divisible by 16.",
"errorCode": "INVALID_VIDEO_SIZE",
"errorType": "PARAMETER_INVALID",
"errorCategory": "schema_validation",
"retryable": false,
"hint": "Try: --width 1296 --height 672 (or omit --strict-size)"
}
--json --balance → { "success": true, "type": "balance", "spark": 12.34, "sogni": 0.56, "username": "name", "subscription": { "active": true, "status": "active", "tier": "unlimited" } } (username/subscription are null when unavailable; an active subscription means eligible renders are covered by Sogni Unlimited even when spark is low). --last --json wraps the last render record in a { "success": true, ... } envelope and exits 1 with errorCode: "NO_LAST_RENDER" when nothing has been rendered. In --json mode stdout always carries exactly one JSON object — SSE workflow frames and progress lines go to stderr.
Cost
Eligible Sogni-hosted renders use Unlimited coverage when active; otherwise renders use the selected Spark or SOGNI token path. 512x512 images are most cost-efficient. -n is safety-capped at 16 outputs per call (SOGNI_MAX_COUNT raises it deliberately). Seedance, HappyHorse, and GPT Image 2 are vendor models requiring Premium Spark eligibility.
Troubleshooting
- Anything broken? Run
sogni-agent doctor first — it checks Node, credentials (and file permissions), config-dir writability, ffmpeg, live auth, and version freshness, with a fix in every failure detail.
- Auth errors: check
SOGNI_API_KEY or ~/.config/sogni/credentials (key from https://dashboard.sogni.ai, account menu).
- Video size errors: sizes are model-specific (WAN ÷16 min 480 max 1536; LTX ÷64, long side ≤2048). The CLI auto-adjusts for local refs;
--strict-size makes it fail with a suggested size instead. Details in references/models.md.
- Timeouts: try a faster model or raise
-t.
- No workers: check https://sogni.ai for network status.
Reference Index (read before acting)
| Read this | When the task involves |
|---|
references/video-prompting.md | Writing LTX video prompts; high-res/4K routing; orientation/aspect mapping; camera language |
references/private-mature-video.md | Mature-theme video model, LoRA, frame modes, and prompt tokens |
references/video-editing.md | Animate between images, continue/bridge videos, 360 turnarounds, concat, audio remix/layering, v2v ControlNet |
references/loop-maker.md | One-click image-folder loops with visual deduplication, direct LTX first/last-frame clips, music, and verification |
references/hosted-api.md | --api-chat, --durable-chat, --api-workflow, workflow templates, replays, Seedance reference modes, cost controls |
references/seamless-tiling.md | Seamless repeating patterns, wallpapers, tiling textures, Escher tessellations |
references/models.md | Choosing models, sizing/divisibility rules, image edit reference limits, music model options |
references/personas-memory.md | Persona CRUD/voice cloning, multi-persona scenes, memories, personality, style transfer, photo restoration |
references/openclaw-config.md | OpenClaw plugin config defaults and overrides |
skills/README.md | Hosted per-skill tool surface (for hosts that load focused capability subsets) |
