Clawhub 3.26.1

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, MiniMax H3/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".

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:

bash

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:

bash

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.

Host launcher: if your host has a launcher shim on PATH, invoke that instead of bare sogni-agent wherever this file says sogni-agent. Each shim behaves identically to sogni-agent and only attributes the request to the host that ran it:

Host	Command
Hermes	sogni-agent-hermes
Codex CLI	sogni-agent-codex
Claude Code	sogni-agent-claude-code
OpenClaw	sogni-agent (detected automatically from OPENCLAW_PLUGIN_CONFIG)
Anything else	sogni-agent

Pick the one matching the host you are running in, and fall back to sogni-agent if that command is not found. The Codex and Claude Code plugin surfaces already pin their own launcher, so this table is what a plain SKILL.md install (Hermes and other runtimes) should follow.

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 CLI package is available and offer to run sogni-agent self-update (follow with sogni-agent --whats-new to summarize what changed). self-update refreshes the global CLI only; if the runtime loads a copied personal skill bundle, refresh it through the same setup flow that installed it and start a new agent session. If the user declines the CLI update, 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

1. Get your Sogni API key by logging into https://dashboard.sogni.ai and opening the account menu.
2. Create the credentials file (or just export SOGNI_API_KEY):

bash

mkdir -p ~/.config/sogni
cat > ~/.config/sogni/credentials << 'EOF'
SOGNI_API_KEY=your_api_key
EOF
chmod 600 ~/.config/sogni/credentials

3. 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:

bash

sogni-agent -o ./cat.png "a cat wearing a hat"       # ✓ lands in PWD
sogni-agent -o cat.png "a cat wearing a hat"         # ✓ lands in PWD
sogni-agent -o /tmp/cat.png "a cat wearing a hat"    # ✗ avoid — user can't easily find it

/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)
• Reusable app-ID slot pool (read/write): ~/.config/sogni/app-ids/slot-<n> plus .lease files; new IDs use sogni-agent-<uuid> (SOGNI_APP_ID_POOL_DIR/SOGNI_APP_ID_POOL_MAX). Concurrent agent processes lease distinct slots automatically; the legacy single ~/.config/sogni/app-id file migrates into slot-0 on first use. Set a stable SOGNI_APP_ID for ephemeral/container homes or long-lived daemons, or SOGNI_APP_ID_PATH for legacy single-file mode.
• 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).

bash

# One-shot: you pick the tool, the server just executes (see Core Commands below)
sogni-agent -q -Q hq -o ./poster.png "Turn the product photo into a launch poster"

# Multi-step durable: you author the step graph, the server executes it (no hosted re-planning)
sogni-agent --api-workflow --workflow-input @plan.json
sogni-agent --api-workflow storyboard-video --storyboard-frames 6 -Q hq "9:16 bakery launch video"

# Deliberately hand the whole loop to the hosted model (long async job, or multi local-file upload)
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)

bash

# Image (quality presets pick model/steps/size: fast | hq | pro)
sogni-agent -q -Q fast -o ./generated.png "user's prompt"
sogni-agent -q -Q pro -o ./generated.png "user's prompt"

# Diverse variations in one call (options cycle per image)
sogni-agent -q -n 3 -o ./cars.png "a {red|blue|green} sports car"

# Edit an existing image (source-preserving)
sogni-agent -q -c /path/to/input.jpg -o ./edited.png "make it pop art style"

# Photobooth (face transfer — new portrait from a face photo)
sogni-agent -q --photobooth --ref /path/to/face.jpg -o ./stylized.png "80s fashion portrait"

# Text-to-video / image-to-video (write the prompt per references/video-prompting.md)
# Single-image i2v defaults to wan_v2.2-14b-fp8_i2v_lightx2v; adding --ref-end
# defaults to ltx23-22b-fp8_i2v_distilled (transition/morph LoRA auto-applies).
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 -q --video --ref ./first.png --ref-end ./last.png -o ./morph.mp4 "<LTX transition paragraph>"

# LTX-2.3 10Eros v1.4 (explicit uncensored I2V; 30GB+ workers only)
sogni-agent -q --video --workflow i2v --ref /path/to/image.png -m ltx23-eros --no-filter -o ./video.mp4 "<LTX-rewritten paragraph>"

# Sound-to-video (lip-sync), image+audio, audio-only (workflow auto-inferred)
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"

# Music (direct audio generation; mp3 by default)
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"

# Seedance 2.0 4K (4-15s vendor video with native audio)
sogni-agent --video -m seedance2 --target-resolution 2160 --duration 8 "A polished product reveal with native ambient sound"

# MiniMax H3 (fixed 24fps, native stereo audio + dialogue; natural prose prompt
# with a timed shot list and audio direction — see references/video-prompting.md).
# t2v default; --ref = i2v; --ref + --ref-end = flf2v; r2v is explicit.
sogni-agent --video -m minimax-h3 --duration 10 -w 1344 -h 768 "<H3 prose prompt>"
sogni-agent --video -m minimax-h3-i2v --ref first.png --duration 8 "<H3 prose prompt>"
sogni-agent --video -m minimax-h3-flf2v --ref first.png --ref-end last.png --duration 8 "<H3 prose prompt>"
sogni-agent --video -m minimax-h3-r2v --ref identity.png -c wardrobe.png --ref-video motion.mp4 --ref-audio voice.m4a "<H3 prompt using <Picture 1>/<Picture 2>/<Video 1>/<Audio 1>>"

# HappyHorse 1.1 (3-15s vendor video, fixed 24fps, native audio). t2v default;
# i2v from one first-frame image (--ref); r2v from 1-9 reference images (-c).
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"

# Balances / last render / inbound media / health (no prompt required)
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|r2v|s2v|ia2v|a2v|v2v|animate-move|animate-replace	inferred
--ref, -c, --ref-end, --ref-audio, --ref-video, --mask	Frame / loose image / audio / video / mask references; audio/video repeat for H3 r2v	-
--generate-audio, --no-generate-audio	Keep or strip MiniMax H3's jointly generated audio track	keep
--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:

bash

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 an edit of a referenced person or character that must preserve likeness or character identity while changing clothing, hair or makeup, pose or position, face/head/body, background, lighting, or visual style, default to -m krea2_identity_edit_v1_2 with -c. Infer that semantic intent in any language; never route from keyword or regex matching. Also use it for a single-character sheet unless Pro/detail-critical layout requirements favor GPT Image 2. An explicitly requested model always wins; use -m dark_beast_krea2_identity_edit_v1_2 only when the creator explicitly requests that community/uncensored variant. Both accept 1-2 references at 512-2048 px. For two references, pass the base scene first and the person/detail/outfit/pose/style reference second. Write a concise 1-4 sentence delta instruction, do not send a negative prompt, and leave steps, guidance, sampler, and scheduler unset so the current model tier and worker choose their optimized defaults.
• For Krea 2 style/control LoRAs, use the base text-to-image model -m krea2_turbo_fp8_scaled with repeatable ordered --lora <id> --lora-strength <n> arguments (or comma-separated --loras / --lora-strengths). Up to 8 LoRAs may be stacked. Order matters, strengths are positional, omitted strengths default to 1, and many Krea 2 LoRAs are bipolar sliders whose negative values apply the inverse effect. Do not clamp them to 0-2. The first use of an uncached LoRA may pause while the worker downloads it.
• 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 Krea 2 Identity Edit by the semantic rule above. The bare CLI still defaults context edits without -m to qwen_image_edit_2511_fp8_lightning, so agents must pass the Krea model explicitly for identity-sensitive edits. 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.

Whenever the creator explicitly requests 10Eros, and for lawful adult mature-theme video requests generally, 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

MiniMax H3 is an explicit model choice, not a universal default. Use -m minimax-h3 for text-to-video, -m minimax-h3-i2v --ref A for first-frame animation, -m minimax-h3-flf2v --ref A --ref-end B for a first/last-frame transition, or explicitly select -m minimax-h3-r2v for a loose reference set. H3 r2v accepts up to 9 images (--ref then repeatable -c), 3 videos (repeat --ref-video), and 3 audio clips (repeat --ref-audio), with 12 files total and at least one image; it is never inferred. Address them as <Picture 1>, <Video 1>, and <Audio 1> in per-type submission order, give every reference one job, and never use --ref-end for r2v. H3 generates picture and native 32 kHz stereo audio jointly at fixed 24 fps, so dialogue, foley, and score must be described in the prompt. --no-generate-audio strips that generated track from the delivered file; it does not skip audio generation. Frames snap to the 124 + n×17 grid (5.17-15.08 s); use -w 1344 -h 768 or -w 768 -h 1344; send no steps, guidance, sampler, scheduler, or negative prompt. It is the 768p-class open-weights release — do not claim 2K. The initial Sogni release is routed to 32 GB-class workers.

H3 takes natural cinematic prose, not a required structured document. Expand the user's one-liner into a directed scene: a timed shot list with bracketed timecodes ([0-2 seconds] …) for anything longer than one beat, deliberate audio direction (ambience, specific SFX, music by instrumentation and timing; say so when you want no music), dialogue as ordinary quoted prose, and negative direction stated in the prompt text since there is no negative-prompt field. MiniMax's <d>[Language] …</d> + (S1) markup and three-field IR document are optional input forms; never invent or strip that markup when a user supplied it. Read references/video-prompting.md § MiniMax H3 Prompting before writing an H3 prompt.

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" or any first-frame/last-frame request (--ref A --ref-end B — defaults to ltx23-22b-fp8_i2v_distilled, a single render whose transition/morph LoRA auto-applies, no bridge clip; single-image i2v defaults to wan_v2.2-14b-fp8_i2v_lightx2v), "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 / HappyHorse / MiniMax H3 native audio and dialogue, WAN lip-sync, LTX dialogue), read references/models.md for the catalog, recommended selectors, and sizing/divisibility rules.

ltx23-eros is an explicit-only uncensored LTX-2.3 image-to-video selector. Never choose it merely because a prompt appears sexual or another model rejects a request. Use it only when the user explicitly asks for 10Eros/the uncensored model and explicitly permits disabling the content filter. It requires an input image, --no-filter, and a 30GB+ worker; the CLI pins its required 9 steps, guidance 1, euler_ancestral sampler, and manual_sigmas scheduler.

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):

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):

json

{
  "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.
• A newly listed live model fails immediately with PROJECT_ERROR: if doctor reports a newer CLI, update before retrying. The live catalog can expose a model that needs model-specific client rules added after the installed CLI version; repeated submissions with generic defaults will fail the same way.
• Auth errors: check SOGNI_API_KEY or ~/.config/sogni/credentials (key from https://dashboard.sogni.ai, account menu).
• Error 4061 / too many app IDs: the CLI leases stable IDs from the persistent pool in ~/.config/sogni/app-ids/. Do not delete that directory between runs. For ephemeral/container homes, set the same SOGNI_APP_ID on every session. App IDs already registered today remain counted until 00:00 UTC, so an existing block may require waiting for that reset once after upgrading.
• Kicked mid-render / SWITCH_CONNECTION 4015: two processes shared one app ID. The slot pool prevents this for concurrent CLI runs; if a long-lived daemon also uses this account, give it its own pinned SOGNI_APP_ID.
• 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; writing MiniMax H3 prose prompts (timed shot lists, audio direction, quoted dialogue, optional IR markup); 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)