kubernetes
Kubernetes operations playbook for deploying services: core objects, probes, resource sizing, safe rollouts, and fast kubectl debugging
Works with
--- name: kubernetes description: Kubernetes operations playbook for deploying services: core objects, probes, resource sizing, safe rollouts, and fast kubectl debugging license: MIT --- # Kubernetes ## Quick Start (kubectl) ```bash kubectl describe pod/<pod> -n <ns> kubectl get events -n <ns> --sort-by=.lastTimestamp | tail -n 30 kubectl logs pod/<pod> -n <ns> --previous --tail=200 ``` ## Production Minimums - Health: `readinessProbe` and `startupProbe` for safe rollouts - Resources: set `requests`/`limits` to prevent noisy-neighbor failures - Security: run as non-root and grant least privilege ## Load Next (References) - `references/core-objects.md` — choose the right workload/controller and service type - `references/rollouts-and-probes.md` — probes, rollouts, graceful shutdown, rollback - `references/debugging-runbook.md` — common failure modes and a fast triage flow - `references/security-hardening.md` — pod security, RBAC, network policy, supply chain
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diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
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Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
