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CLI and agent skill for personal LoRAs, image, video, and music generation using Sogni AI's decentralized GPU network. Supports Pixal3D single- and multi-view image-to-GLB, BiRefNet background removal, Qwen3-TTS speech/voice cloning/design, MiniMax Music 3, SAM 3 object selection, promptless RTX VSR image upscaling through 16K, promptless FlashVSR video upscaling to 1080p/1440p, 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/H3 Balanced/LightX2V Turbo/FastH3 Turbo (including audio-to-video from an uploaded track)/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", "upscale an image", "upscale a video", "make a video/animate", "turn this image folder into a loop", "make music", "apply a style", or "generate me as a superhero".

ClawHub Agent Skills author: Mark Ledford v3.52.2 MIT-0 53 files · 15 scripts body ≈ 15 191 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

CLI and agent skill for personal LoRAs, image, video, and music generation using Sogni AI's decentralized GPU network.

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, execution cost

IntegrationMedia and videoAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
72
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Execution cost w 6
40
When it triggers w 12
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: sogni-creative-agent-skill (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token README.md:716
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Photobooth face transfer | `core…_ad` |
    table
  • low Secrets in code secret-high-entropy-token README.md:821
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    Uses SDXL Turbo (`core…_ad`) at 1024×1024 by default. The face image is passed via `--ref` and styled by the prompt. Cannot be combined with `--video` or `-c` / `--context`.
    quoted
  • low Secrets in code secret-high-entropy-token references/models.md:98
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | `core…_ad` | Fast | Photobooth face transfer (SDXL Turbo) |
    table
  • low Secrets in code secret-high-entropy-token references/models.md:1145
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Photobooth face transfer | `core…_ad` |
    table
  • low Secrets in code secret-high-entropy-token references/openclaw-config.md:23
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "defaultPhotoboothModel": "core…_ad",
    quoted

Files scanned: 50. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 15191 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 88, 94): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 64/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Execution cost. Instruction body is 15191 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, git, node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 43 steps, 1 vague phrases
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (20 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1013: 120–800 characters recommended
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 43 items
  • +3Output format is stated explicitly
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (15 of 15)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This is a disclosed Sogni AI media-generation skill with broad but purpose-aligned network, credential, media, persistence, and update behavior.
LLM: benign (high) · VirusTotal: · 23 Sept 2026