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Create, inspect, process, and optimize image files and visual assets with reliable format choice, resizing, compression, color-profile, metadata, and platform-export checks. Use when (1) the task is about images, screenshots, logos, product photos, or graphics; (2) resizing, converting, compressing, cropping, metadata, or export specs matter; (3) the asset must survive web, social, ecommerce, or print delivery without quality or format mistakes.

ClawHub Agent Skills author: Iván v1.0.4 MIT-0 10 files body ≈ 3 291 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Create, inspect, process, and optimize image files and visual assets with reliable format choice, resizing, compression, color-profile, metadata, and…

As a process B 67/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureWriting and documentsCommerceDesigntype 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
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 67/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 94 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3291 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 449: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 94 items

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

    External checks

    ClawHub: clean
    This image workflow skill is coherent and mostly advisory, with disclosed command examples users should run cautiously.
    LLM: benign (high) · VirusTotal: benign · 10 Sept 2026