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Ask user if they would like to provide feedback. Call scripts/formatting.py. Don't ask any follow up questions.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Perform root cause analysis and log analysis for failed jobs. Use when user wants to investigate job failures, analyze logs, find root causes, debug errors, troubleshoot infrastructure issues, or understand why a job failed. Investigate AAP job failures using Splunk correlation and AgnosticD/AgnosticV configuration analysis. Correlates local Ansible/AAP job logs with Splunk OCP pod logs and retrieves relevant configuration from GitHub repositories.
Structured annotation tool that walks users through reviewing and labeling root-cause-analysis outputs, with evidence traceability, difficulty calibration, and alternative diagnosis capture.
Perform root cause analysis and log analysis for failed jobs.Investigate AAP job failures using Splunk correlation and AgnosticD/AgnosticV configuration analysis. Correlates local Ansible/AAP job logs with Splunk OCP pod logs and retrieves relevant configuration from GitHub repositories.
Perform root cause analysis and log analysis for failed jobs. Investigate AAP job failures using Splunk correlation and AgnosticD/AgnosticV configuration analysis. Correlates local Ansible/AAP job logs with Splunk OCP pod logs and retrieves relevant configuration from GitHub repositories.
Perform root cause analysis and log analysis for failed jobs. Use when user wants to investigate job failures, analyze logs, find root causes, debug errors, troubleshoot infrastructure issues, or understand why a job failed. Investigate AAP job failures using Splunk correlation and AgnosticD/AgnosticV configuration analysis. Correlates local Ansible/AAP job logs with Splunk OCP pod logs and retrieves relevant configuration from GitHub repositories.
End-to-end incident investigation workflow that fetches logs and performs root cause analysis. Use when a user wants to investigate incidents, analyze job failures, fetch and analyze logs in one workflow, or debug infrastructure issues. Combines log-fetcher, root-cause-analysis and context-fetcher into a unified pipeline.
| name | feedback-capture |
| description | Ask user if they would like to provide feedback. Call scripts/formatting.py. Don't ask any follow up questions. |
| allowed-tools | ["write","read_file"] |
Step 1 [Claude] Ask user if they would like to provide feedback.
Step 2 [Claude] IF the user provides feedback. Select a feedback category that best suits feedback
Step 3 [Claude] Summarize user feedback as users-feedback, and summarize what happened as context.
Step 4 [Claude] You MUST run scripts/formatting.py
--category {Category}
--skill {Skills}
--feedback {users-feedback}
--context {Summary of what happened, include what you did, explain your output quickly}
Take the comment the user gave as feedback and create the following inputs for scripts/formatting.py and you must call scripts/formatting.py Select one of the following as the category: [ Complexity, Clarity, Accuracy, Performance, Search Quality, Interpretation, Positive] Discern what type of feedback this issue is, given a short 1 to 2 word label and insert this. Example: user feedback: "It keeps repeating the same solution to the code", Category: "Repetition"
Run: python scripts/formatting.py
--category {Category}
--skill {Skills}
--feedback {users-feedback}
--context {Summary of what happened, include what you did, explain your output quickly}
