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Agent skill

Review Content Quality

by openvetta in openvetta/open-vetta

Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair.

Apache-2.0Auto-check passedEducation

Install Review Content Quality

skills CLI
$ npx skills add openvetta/open-vetta --skill review-content-quality -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openvetta/open-vetta review-content-quality --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openvetta/open-vetta.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/plugins/presets/content-creation/agent/skills/review-content-quality .claude/skills/review-content-quality && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-content-quality
GitHub stars
290
Token cost
~545 tokens
SKILL.md length
215 words
Files
6 (incl. references)
Skills in repo
21
Repo updated
First seen
Licence
Apache-2.0

At a glance

Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair.

  • Works in 6 steps: Restate the artifact's job, surface,… → Apply must-pass gates before scoring… → Inspect the actual image at delivery… → …
  • The user asks whether an output is good
  • SKILL.md covers Review sequence and Output contract
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Content Quality is an agent skill from openvetta/open-vetta. 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.

Its SKILL.md is about 550 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `agents/openai.yaml`, `references/image-rubric.md` and `references/repair-policy.md`).

It sits in Education. The repository describes itself as: Open-source, local-first AI agent for coding and real work. BYOK models, MCP, skills, plugins, workflows, and private knowledge bases. The licence is Apache-2.0.

When your agent uses it

  • The user asks whether an output is good
  • Requests critique
  • Selection among variants
  • Before expanding an approved direction into more assets

Example prompts

  • “/review-content-quality”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit b983179. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Content Quality loads about 545 tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 97 tokens; SKILL.md has 215 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~545
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openvetta/open-vetta at commit b983179, republished under its Apache-2.0 licence (© openvetta). 215 words, ~545 tokens.

Download SKILL.mdSave it as .claude/skills/review-content-quality/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
review-content-quality
description
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.

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).

© openvetta, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in packages/plugins/presets/content-creation/agent/skills/review-content-quality of openvetta/open-vetta.

  • SKILL.md
  • agents/openai.yaml
  • references/image-rubric.md
  • references/repair-policy.md
  • references/scenario-gates.md
  • references/video-rubric.md

Open the folder on GitHubat commit b983179

Compare with similar skills

Review Content Quality next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Content Quality compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Content Quality this skillopenvetta/open-vetta290—~545Automated safety check: PassApache-2.0
DeepTutor CLIHKUDS/DeepTutor41k—~2.8kAutomated safety check: PassApache-2.0
AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch67k—~2kAutomated safety check: PassMIT
Deep Reading Analystginobefun/deep-reading-analyst-skill3544 repos~3.6kAutomated safety check: PassMIT
OpenMAIC Setup and ExtensionTHU-MAIC/OpenMAIC40k—~1.7kAutomated safety check: NotesMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone

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Categories

Questions about Review Content Quality

What does Review Content Quality do?

Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair. Review Content Quality is an agent skill from openvetta/open-vetta. Evaluate generated images and videos against their creative brief, delivery requirements, continuity rules, and technical quality, then propose the smallest evidence-based repair.

When should I use Review Content Quality?

Review Content Quality fits situations like: the user asks whether an output is good; requests critique; selection among variants; before expanding an approved direction into more assets.

How do I install Review Content Quality in Claude Code?

Run `npx skills add openvetta/open-vetta --skill review-content-quality -a claude-code`. Or copy the skill folder (packages/plugins/presets/content-creation/agent/skills/review-content-quality in openvetta/open-vetta) into .claude/skills/review-content-quality in your project. Claude Code loads it when a task matches its description.

How do I install Review Content Quality in Codex?

Run `npx skills add openvetta/open-vetta --skill review-content-quality -a codex`. Or copy the skill folder (packages/plugins/presets/content-creation/agent/skills/review-content-quality in openvetta/open-vetta) into .agents/skills/review-content-quality in your project. Codex loads it when a task matches its description.

Can I use Review Content Quality in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openvetta/open-vetta --skill review-content-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-content-quality, .gemini/skills/review-content-quality, .github/skills/review-content-quality and .opencode/skills/review-content-quality in your project.

What does Review Content Quality need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Content Quality is instructions for the agent only.

Does Review Content Quality access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Content Quality safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Content Quality use?

Review Content Quality is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Content Quality use?

About 545 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Review Content Quality?

Skills that share tags, products or a category with Review Content Quality: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Deep Reading Analyst (ginobefun/deep-reading-analyst-skill, 354 stars) and OpenMAIC Setup and Extension (THU-MAIC/OpenMAIC, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Content Quality?

openvetta (a GitHub organization) maintains it in openvetta/open-vetta, which has 290 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 9, 2026.

Source: openvetta/open-vetta on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.