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Review Image

Community
mizchi
review-image

Review screenshots or other images with OpenRouter vision models via bundled Deno scripts. Use for quick VRT sanity checks, invalid-image screening, or CI gates. `scripts/review-image.ts` returns freeform feedback; `scripts/review-image-ci.ts` returns strict `pass|fail` JSON and exits non-zero on fail.

Overview

Publishermizchi
Repositoryskills
Skill namereview-image
Stars
333
Forks
4
Bundled files
5
Links
  • 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 mizchi on GitHub. Read the source before you install it.

Installation

Install the Review Image 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.
  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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/review-image .claude/skills/review-image
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Image 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 Image 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 Image 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-image

Use this skill when you want a cheap vision-model opinion on an image before doing heavier work.

When to use

  • Quick VRT sanity checks
  • Detect obviously broken, blank, malformed, or suspicious inputs
  • Ask a model whether a screenshot or generated image matches a prompt
  • Add a simple image-review gate to CI

Requirements

  • Deno
  • OPENROUTER_API_KEY or OPENROUTER_KEY

Invocation

Both scripts are executable Deno files with shebang #!/usr/bin/env -S deno run --allow-env --allow-net --allow-read. Run them directly — no deno run wrapper needed.

When the skill is APM-installed, the scripts live under ~/.claude/skills/review-image/scripts/. Examples below use the ~/... form, which is shell-expanded. In non-shell exec contexts (e.g. child_process.spawn without shell: true) both ~ and $HOME stay literal — substitute the resolved absolute path (/Users/<you>/.claude/skills/review-image/scripts/...).

Flags can appear before, between, or after the positional args. --model accepts both --model X and --model=X. Both scripts write program output to stdout and logs/errors to stderr — but only the JSON-emitting modes (CI gate always; freeform with --json) are safe to pipe straight into jq / JSON.parse. Freeform default mode is prose, not JSON.

Scripts

Freeform review — review-image.ts

sh
~/.claude/skills/review-image/scripts/review-image.ts <image-path-or-url> "<prompt>" [--model <id>] [--json]

# example with flag interleaved with positionals:
~/.claude/skills/review-image/scripts/review-image.ts ./shot.png "VRT diff broken?" --model google/gemini-2.5-flash

Default output: plain prose on stdout (the model's reply, single block).

With --json: stdout becomes a single JSON object, safe to pipe into jq / JSON.parse:

json
{
  "id": "gen-...",
  "model": "<resolved>",
  "text": "<freeform answer>",
  "usage": { "prompt_tokens": 123, "completion_tokens": 45, "total_tokens": 168, "cost": 0.0001 },
  "raw": { /* full upstream OpenRouter response, opaque */ }
}

Read .text for the answer; .usage.cost (USD) and .usage.total_tokens for cost telemetry.

Exit codes: 0 on success, 1 on any error (missing API key, network/HTTP failure, malformed response).

CI gate — review-image-ci.ts

sh
~/.claude/skills/review-image/scripts/review-image-ci.ts <image-path-or-url> "<prompt>" [--model <id>]

Always prints a single JSON object on stdout (the --json flag is silently ignored without warning — output is always JSON):

json
{
  "decision": "pass",
  "summary": "<short reason>",
  "issues": [],
  "model": "<resolved>",
  "usage": { "prompt_tokens": 123, "completion_tokens": 45, "total_tokens": 168, "cost": 0.0001 },
  "rawText": "<unparsed model reply>"
}

issues is always an array of strings (never null / never omitted) — empty on pass, populated with one or more reasons on fail. usage keys match the freeform script and may include additional OpenRouter-passthrough fields (e.g. is_byok, *_details); treat the four listed keys (prompt_tokens, completion_tokens, total_tokens, cost) as the stable contract.

The CI gate wraps your prompt internally with strict JSON-schema instructions for the model. Write <prompt> as plain English describing the gate criteria — do not include schema, formatting directives, or "respond in JSON" yourself; those are supplied automatically and your additions can conflict with them.

Exit codes:

  • 0decision: pass
  • 2decision: fail
  • 1 — script error (missing API key, network/HTTP failure, malformed model response). Treat as neither pass nor fail in CI; do not gate on $? -ne 0 alone.

Writing prompts

The <prompt> argument is plain English describing what to check. Use these as starting templates:

CI gate (gate-criteria style) — describe what should be there and what counts as failure. Avoid vague "is this broken?"; pixel art and intentionally low-res content fail naively-worded gates:

"This image is {expected: a Playwright screenshot of /dashboard / a generated cactus illustration / ...}. Pass if it shows recognizable intended content (any style, including pixel art, low-res, or stylized). Fail only if blank, all-black/all-white, corrupted, an error page, or visibly malformed."

Freeform (diagnostic style) — ask for specifics so the prose answer is actionable:

"This image is supposed to be {expected subject}. Describe what you see and call out any visual artifacts, broken layout, color anomalies, or signs of file corruption."

Default model

google/gemini-2.5-flash-lite

Override per call with --model or globally with OPENROUTER_MODEL.

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 Image AI skill do?

Review screenshots or other images with OpenRouter vision models via bundled Deno scripts. Use for quick VRT sanity checks, invalid-image screening, or CI gates. `scripts/review-image.ts` returns freeform feedback; `scripts/review-image-ci.ts` returns strict `pass|fail` JSON and exits non-zero on fail.

Why use Review Image on TypingMind?

Because you install it once and use it with any model. Review Image 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 Image in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/review-image. 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 Image?

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 Image?

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

Is the Review Image AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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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