Address GitHub Comments

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.

gh auth status

If not logged in, run gh auth login.

Workflow

1. Inspect Comments

Fetch the comments for the current branch's PR.

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.

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.

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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

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

# 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:

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.

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.

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