内容工厂专用复核 Skill，检查事实依据、平台适配、AI 味、风险和人工确认建议。
| Publisher | limecloud |
| Repository | lime |
| Skill name | content-reviewer |
| Stars | 1.5K |
| Forks | 207 |
| Bundled files | Instructions only |
| Links |
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Everything the model needs lives in the instructions — no extra files to sync.
Published by limecloud on GitHub. Read the source before you install it.
Install the Content Reviewer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.
TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.
Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.
git clone --depth 1 https://github.com/limecloud/lime.git /tmp/lime
mkdir -p .claude/skills
cp -r /tmp/lime/lime-rs/resources/default-skills/content-reviewer .claude/skills/content-reviewerEnable Content Reviewer 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.
AI skills are plain Markdown instructions rather than provider-specific code, so Content Reviewer 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.
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
你是内容工厂的质量复核 Skill。你的目标是让 App 内的内容资产可以被人工审核、继续补齐或交付，而不是只给一段主观评价。
最终必须输出 JSON，顶层只使用 workspacePatch。patch 中至少包含：
kind: 固定为 workspace_patchprojectId: 当前项目 IDquality_check 或 reviewReport: 检查结论、风险、证据和待确认项assetPack.summary: 可确认、待修改、阻塞的资产数量skillEvidence: 至少记录 content-reviewer 的执行摘要如果只是补齐已有内容，不要重写全部资产；只标记缺口和建议动作。
内容工厂专用复核 Skill，检查事实依据、平台适配、AI 味、风险和人工确认建议。
Because you install it once and use it with any model. Content Reviewer 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.
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/limecloud/lime/tree/main/lime-rs/resources/default-skills/content-reviewer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.
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.
As many as you like. As long as a model supports skills, you can use Content Reviewer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
It is published on GitHub by limecloud. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.
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.
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.
Requesting Code Review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Subagent Driven Development
Use when executing implementation plans with independent tasks in the current session
Systematic Debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Test Driven Development
Use when implementing any feature or bugfix, before writing implementation code
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