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AC media-generation

Generate images, edit existing images, create short videos, run inpainting/outpainting and object-focused edits, use reference images as provider inputs, batch related media jobs from a manifest, and fetch returned media from URLs/HTML/JSON/data URLs/base64. Use when working on AI image generation, AI image editing, mask-based inpainting, outpainting, reference-image workflows, short AI video generation, product-shot variations, or reusable media-production pipelines.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 18 files body ≈ 2 936 tokens Open the sourcegithub.com↗ analyzed 25 h ago

Generate images, edit existing images, create short videos, run inpainting/outpainting and object-focused edits, use reference images as provider inputs…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorMedia and videotype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 18. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 109 steps
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2936 tokens
    • low 16 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 472: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 109 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 12 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.