메뉴
Ask user if they would like to provide feedback. Call scripts/formatting.py. Don't ask any follow up questions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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}
