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bot-issue-qa

Validate claude_bot GitHub issues against Airflow logs and QA criteria.

Updated Aug 23, 2026
Search Tags:#automation#logs#github#qa#airflow#dag
One-click install
npx skills add https://github.com/mporenta/airflow --skill bot-issue-qa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bot-issue-qa
Source: https://github.com/mporenta/airflow/tree/main/.claude/skills/bot-issue-qa
Command: npx skills add https://github.com/mporenta/airflow --skill bot-issue-qa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the QA review and triage of claude_bot issues in Airflow by validating issues against the current logs, scoring accuracy and clarity, and posting a structured QA comment to drive resolution.

Core Features & Use Cases

  • Validate issue legitimacy by cross-checking issue content with DAG logs and system state.
  • Score and decide using six criteria: validity, error classification, root-cause accuracy, evidence quality, actionability, and clarity.
  • Post machine-parsable QA comments to automate triage and status updates.
  • Close invalid issues with an added label or enter plan mode to guide fixes for actionable items.

Quick Start

Provide a GitHub issue URL or number and run the QA workflow to produce a structured QA report.

Frequently Asked Questions about bot-issue-qa

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate QA review for Airflow claude_bot issues using GitHub URLs?▼

You can automate QA review for Airflow claude_bot issues by providing a GitHub issue URL or number. The workflow validates the issue against DAG logs and standard QA criteria, then posts a machine-parsable QA comment to guide resolution.

What criteria are used to score and triage Airflow claude_bot issues?▼

Airflow claude_bot issues are scored using six criteria: validity, error classification, root-cause accuracy, evidence quality, actionability, and clarity. This structured scoring automates triage and drives resolution decisions.

How does the automated QA workflow verify issue legitimacy in Airflow DAGs?▼

The automated QA workflow verifies issue legitimacy by cross-checking issue content with Airflow DAG logs and system state. It extracts markers from the issue body and reads evidence logs from S3 to validate accuracy.

Can I use this QA triage process to automatically close invalid GitHub issues?▼

Yes, the QA triage process can close invalid GitHub issues with an added label or enter plan mode to guide fixes for actionable items. It posts a structured QA comment to automate these status updates.

What is the best way to triage claude_bot issues in Airflow with S3 log evidence?▼

The best way to triage claude_bot issues in Airflow is to run an automated QA workflow that reads evidence logs from S3, scores each criterion, and posts a machine-parsable QA comment to automate resolution.

Why does the automated QA review extract markers from GitHub issue bodies?▼

Automated QA review extracts markers from GitHub issue bodies to identify and validate issue data against Airflow DAG logs and S3 evidence. This ensures accurate scoring of validity, root-cause, and actionability criteria.

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