Open Plannotator's browser-based code review UI and address the returned feedback.
| Publisher | backnotprop |
| Repository | plannotator |
| Skill name | plannotator-review |
| Stars | 8.8K |
| Forks | 658 |
| Bundled files | Instructions only |
| License | Apache-2.0 |
| 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 backnotprop on GitHub. Read the source before you install it.
Install the Plannotator Review 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/backnotprop/plannotator.git /tmp/plannotator
mkdir -p .claude/skills
cp -r /tmp/plannotator/apps/kiro-cli/skills/plannotator-review .claude/skills/plannotator-reviewEnable Plannotator Review 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 Plannotator Review 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.
Run:
bashPLANNOTATOR_ORIGIN=kiro-cli plannotator review
You may append an optional PR URL:
bashPLANNOTATOR_ORIGIN=kiro-cli plannotator review <pr-url>
Or open the session against a specific base / diff mode (session-only, git-only; for a stacked branch, --base <the branch below yours> reviews just that layer):
bashPLANNOTATOR_ORIGIN=kiro-cli plannotator review --base <ref> [--diff-type <type>]
Open Plannotator's browser-based code review UI and address the returned feedback.
Because you install it once and use it with any model. Plannotator Review 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/backnotprop/plannotator/tree/main/apps/kiro-cli/skills/plannotator-review. 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 Plannotator Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
Yes. It is published on GitHub by backnotprop under the Apache-2.0 license. 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
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
Using Git Worktrees
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback
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