---
name: address-github-comments
description: "Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI."
risk: unknown
source: community
date_added: "2026-02-27"
---

# Address GitHub Comments

## Overview

Efficiently address PR review comments or issue feedback using the GitHub CLI (`gh`). This skill ensures all feedback is addressed systematically.

## Prerequisites

Ensure `gh` is authenticated.

```bash
gh auth status
```

If not logged in, run `gh auth login`.

## Workflow

### 1. Inspect Comments

Fetch the comments for the current branch's PR.

```bash
gh pr view --comments
```

Or use a custom script if available to list threads.

### 2. Categorize and Plan

- List the comments and review threads.
- Propose a fix for each.
- **Wait for user confirmation** on which comments to address first if there are many.

### 3. Apply Fixes

Apply the code changes for the selected comments.

### 4. Respond to Comments

Once fixed, respond to the threads as resolved.

```bash
gh pr comment <PR_NUMBER> --body "Addressed in latest commit."
```

## Common Mistakes

- **Applying fixes without understanding context**: Always read the surrounding code of a comment.
- **Not verifying auth**: Check `gh auth status` before starting.

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

```bash
# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Address Github Comments"
```

### Storing Results

After completing work, store workflow/automation decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags address-github-comments workflow
```

### Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>
```

### Playbook Engine

Combine this skill with others using the Playbook Engine (`execution/workflow_engine.py`) for guided multi-step automation with progress tracking.

<!-- AGI-INTEGRATION-END -->
