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Review Content Quality

Organization
openvetta
review-content-quality

Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair. Use when the user asks whether an output is good, requests critique or selection among variants, reports weak or broken media, or before expanding an approved direction into more assets.

Overview

Publisheropenvetta
Repositoryopen-vetta
Skill namereview-content-quality
Stars
299
Forks
51
Bundled files
5
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openvetta on GitHub. Read the source before you install it.

Installation

Install the Review Content Quality AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openvetta/open-vetta/tree/main/packages/plugins/presets/content-creation/agent/skills/review-content-quality
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openvetta/open-vetta.git /tmp/open-vetta
mkdir -p .claude/skills
cp -r /tmp/open-vetta/packages/plugins/presets/content-creation/agent/skills/review-content-quality .claude/skills/review-content-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Content Quality in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Content Quality on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Content Quality is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Review content quality

Judge the rendered artifact, not the prompt's intention. Use $operate-content-workflow to inspect objective, node purpose, references, capabilities, runtime, and diagnostics. If the actual pixels or frames are unavailable, state that visual quality cannot be verified and limit conclusions to workflow/runtime evidence.

Review sequence

  1. Restate the artifact's job, surface, hard constraints, and continuity anchors.
  2. Apply must-pass gates before scoring creative polish.
  3. Inspect the actual image at delivery size or sample the video across beginning, middle, end, and critical transitions.
  4. Separate observed evidence from inferred cause.
  5. Return a verdict: approve, approve with minor repair, regenerate with targeted change, or blocked by capability/input.
  6. Propose one primary repair and preserve what already works.

Read references/image-rubric.md for images, references/video-rubric.md for video, and references/repair-policy.md before proposing another generation. Read references/scenario-gates.md for logos and brand systems, ecommerce/product sets, identity or try-on work, spatial/UI designs, social assets, UGC, product films, action/tutorial sequences, or clipped highlights.

Output contract

Report: verdict, must-pass failures, strongest qualities, evidence by rubric dimension, primary cause hypothesis, next change, and invariants to preserve. For variants, rank them against the same criteria and explain the tradeoff; do not average away a hard failure.

This method is an original Vetta adaptation informed by visual-skills by Serge Shima (CC BY 4.0) and ViMax (MIT).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Review Content Quality AI skill do?

Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair. Use when the user asks whether an output is good, requests critique or selection among variants, reports weak or broken media, or before expanding an approved direction into more assets.

Why use Review Content Quality on TypingMind?

Because you install it once and use it with any model. Review Content Quality is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Content Quality in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openvetta/open-vetta/tree/main/packages/plugins/presets/content-creation/agent/skills/review-content-quality. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Content Quality?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Content Quality?

As many as you like. As long as a model supports skills, you can use Review Content Quality with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Content Quality AI skill free?

Yes. It is published on GitHub by openvetta under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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