SKILLEMALL.ai
GitHub ↗ RU Sign in

AC geo-multimodal-tagger

Generate AI-optimized Alt Text, file names, captions, and Schema markup for images, videos, and audio assets. Improves AI discoverability on Google Lens, ChatGPT Vision, and Perplexity. Use whenever the user mentions optimizing images for AI, writing Alt Text, generating video Schema, tagging assets for AI discoverability, or making images visible in ChatGPT Vision and Google Lens.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 859 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate AI-optimized Alt Text, file names, captions, and Schema markup for images, videos, and audio assets.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 3. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 859 tokens
    • 100Running it twice. No mutating operations
    • medium 1 test cases, all positive: not one "should refuse" or "should ask first"

    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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 384: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.