Code Review Enviornment for the Modern Era

Name: Code Review Enviornment for the Modern Era
Availability: InStock
Author: ramoz

by ramoz·Jul 6, 2026·3 points·0 comments

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AI Analysis

●●●BangerSlickSolve My ProblemBig Brain

6.8k stars prove devs want local-first AI code review with annotation feedback loops.

Strengths

•Annotations export as markdown anchored to exact files and lines for agent consumption
•Git status view shows committed, uncommitted, and untracked changes in one screen
•Optional AI layer means manual review works without any model in the loop

Weaknesses

•Perforce support mentioned but unclear how well it works compared to git
•Review agents feature requires writing your own skills, adds complexity

Post Description

Plannotator opens a local diff viewer (git, jj, p4). You annotate lines, tokens, or comment on files, and when you're done they feed straight into your agent session; creating an automated feedback loop.

It works on local diffs, commits, branches, worktrees, and GitHub/GitLab PRs. The PR view lets you annotate the descriptions and comments while also being able to filter bot comments.

The core tool enables focused manual review, the optional AI layer:

- Ask AI: Ask questions directly inline - Guided Reviews: Have an agent reorganize a large diff into semantic overview sorted by importance (inspired by linear) - Review Agents: Run review agents whose findings land as inline annotations. Review jobs can be your own skills that plannotator links to locally.

There's also a local API, your agents can POST findings or other markers/insights/notes onto the diff. Every annotation you make is saved to ~/.plannotator. Point an agent at that history and repeated feedback distills into a custom review skill (compound engineering).

Plannotator works with most agents and the AI features utilize core model subscription providers.

Free, MIT/Apache 2.0, runs entirely on your machine.

https://github.com/backnotprop/plannotator