This skill should be used when CodeRabbit code review feedback needs to be processed and fixed systematically. Use after running `coderabbit --plain` to automatically save feedback, analyze issues using MCP tools, and implement minimal code fixes with proper planning.
Install with Tessl CLI
npx tessl i github:alchemiststudiosDOTai/coderabbit-fix-flow-plugin --skill coderabbit-fix-flow66
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
This skill automates the workflow of processing CodeRabbit code review feedback by saving the review output to a timestamped document, then using MCP tools (sequential thinking and Exa context) to analyze and implement fixes with minimal code changes.
Use this skill immediately after running coderabbit --plain or when you have CodeRabbit feedback that needs systematic processing. The skill handles type safety issues, code style violations, and other CodeRabbit-identified problems.
Run the CodeRabbit review command in plain text mode:
coderabbit --plainSave the CodeRabbit output to a timestamped QA document:
memory-bank/qa/coderabbit/cr-qa-{timestamp}.md---
title: "CodeRabbit QA Review - {timestamp}"
link: "cr-qa-{timestamp}"
type: "qa"
tags:
- code-review
- coderabbit
- type-safety
created_at: "{timestamp}"
updated_at: "{timestamp}"
uuid: "{generate-uuid}"
---Use the sequential thinking MCP tool to analyze all identified issues:
Use the Exa code context MCP tool to research current best practices for each issue type:
Execute the fixes following the sequential thinking plan:
Run validation as appropriate:
npm run build or tsc --noEmitmypy or ruff checknpm run lint or biomeDocument the fixes in the QA document with:
// Before (unsafe)
const data = response as ResearchDataShape;
// After (safe)
const data = isResearchDataShape(response) ? response : {} as ResearchDataShape;# Before (incorrect)
timestamp: float = None
metadata: dict[str, Any] = None
# After (correct)
timestamp: float | None = None
metadata: dict[str, Any] | None = NoneUse for:
Use for:
This skill doesn't require bundled resources as it relies on MCP tools for context and the existing codebase for implementation.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.