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name: runegard description: > Autonomous runbook executor for Kubernetes operations. Reads markdown runbooks, parses them into executable decision trees, and follows them step-by-step against a live K8s cluster. Requests human approval before any mutating action. Includes an CL-powered improvement loop that learns from execution failures.

RuneGärd -- Kubernetes Runbook Executor

When to Use This Skill

Use this skill when:

• You have a runbook document describing an operational procedure
• You need to diagnose or remediate a Kubernetes issue
• You want to follow a step-by-step troubleshooting guide against a live cluster

Workflow

Phase 1: Parse the Runbook

1. Accept a runbook file path from the user
2. Run: uv run python -m runegard parse <runbook_path>
3. Present the parsed structure to the user for confirmation:
   • Number of steps detected
   • Decision points identified
   • Commands that will be executed
4. If the user wants changes, adjust and re-parse

Phase 2: Execute the Runbook

1. Ask the user for execution mode:
   • Interactive (default): pause before remediation steps for approval
   • Dry-run: walk the tree without executing any commands
2. Run: uv run python -m runegard run <runbook_path> [--dry-run]
3. For each step:
   • DIAGNOSTIC steps: execute automatically, report output
   • REMEDIATION steps: present the command, risk, and rollback to the user. Wait for 'approve', 'skip', or 'abort'
   • VERIFICATION steps: execute automatically, report whether check passed
   • ESCALATION steps: present escalation info, ask if user wants to continue
4. If any step fails: offer retry, skip, rollback, or abort
5. Consult references/learned_patterns.md for known patterns that might affect execution

Phase 3: Report Results

1. Present a summary: steps completed, issues found, actions taken
2. The trace is saved to trace_log.json

Phase 4: Improve (Continual Learning)

1. If the execution had failures or suboptimal paths, ask: "Would you like me to analyze this run and improve the skill for next time?"
2. If yes, run: uv run python -m runegard improve trace_log.json --runbook <path>
3. Present proposed learnings and suggestions for review
4. Apply approved learnings to references/learned_patterns.md

Important Rules

• NEVER execute a REMEDIATION command without explicit user approval
• ALWAYS log every command and its output to the trace log
• ALWAYS check for a rollback path before executing any REMEDIATION step
• If a command's output doesn't match any expected pattern, flag it and ask the user
• Consult references/learned_patterns.md before executing -- it contains patterns from previous runs