Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jtprogru/bear-skills --skill sre-k8s-triagegit clone --depth 1 https://github.com/jtprogru/bear-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jtprogru/bear-skills/sre-k8s-triage)<a href="https://agentmods.dev/skills/jtprogru/bear-skills/sre-k8s-triage"><img src="https://agentmods.dev/badge/skills/jtprogru/bear-skills/sre-k8s-triage/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jtprogru/bear-skills/sre-k8s-triage"><img src="https://agentmods.dev/badge/skills/jtprogru/bear-skills/sre-k8s-triage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.01882 |
| Opus 5 | $0.00061 | $0.00941 |
| Sonnet 5 | $0.00024 | $0.00376 |
| Haiku 4.5 | $0.00012 | $0.00188 |
Grade A, and why
sre-k8s-triage scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sre-k8s-triage — Диагностика k8s от симптома к причине
Цель: дать структурированный путь от симптома к гипотезе, без бесцельного kubectl-серфинга.
Перед началом — если у пользователя есть домашний runbook на этот сценарий, посмотри его (~/.claude/rules/sre-runbook-template.md — структура общая).
Шаг 0. Зафиксируй симптом
Спроси:
- ресурс: Pod / Deployment / Service / Ingress
- namespace, имя
- статус:
kubectl -n <ns> get <res> <name>— что показывает - с какого времени, что изменилось
Если симптом размыт («приложение не работает») — попроси конкретику: ошибка пользователя, HTTP-статус, логи.
Шаг 1. Выбери ветку по симптому
| Симптом / Status | Идти в раздел |
|---|---|
Pod: Pending |
A. Шедулинг |
Pod: CrashLoopBackOff |
B. Падает на старте |
Pod: ImagePullBackOff / ErrImagePull |
C. Образ |
Pod: OOMKilled (в kubectl describe Last State) |
D. Память |
Pod: Running, но не отвечает |
E. Readiness / трафик |
| Service: нет endpoints | F. Селектор |
| Ingress: 502/503/504 | G. Маршрутизация |
| Deployment: rollout stuck | H. Деплой |
A. Pod Pending — шедулинг
kubectl -n <ns> describe pod <pod> | tail -20
Смотри секцию Events. Частые причины:
Insufficient cpu/memory→ нет нод с нужными ресурсами. Решение: понизитьrequests, скейл нод, проверитьkubectl top nodes.0/N nodes are available: ... untolerated taint→ под не толерирует taint. Решение: добавитьtolerationsили unaint ноду.pod has unbound immediate PersistentVolumeClaims→ PVC не provisioned. Проверить StorageClass, провижионер.node(s) didn't match Pod's node affinity/selector→ проверитьnodeSelector/affinity.
B. CrashLoopBackOff — падает на старте
kubectl -n <ns> logs <pod> --previous --tail=100
kubectl -n <ns> describe pod <pod> | grep -A5 'Last State'
Частые причины:
- Exit code 1 + ошибка в логах → ошибка приложения (конфиг, миссинг env-vars, БД недоступна).
- Exit code 137 → OOM, см. раздел D.
- Exit code 139 (SIGSEGV) → segfault, скорее всего баг в коде или libc-несовместимость.
- Liveness probe failure → проверь
livenessProbe. Часто стартап дольше, чемinitialDelaySeconds.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 162 lines · 122 tokens per session scan A 66591e7732b5
sre-k8s-triage is a skill published in the GitHub repository jtprogru/bear-skills (1 stars, last pushed 24d ago), licensed MIT. It adds 122 tokens to every session and 1,882 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
debug
Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing…
systematic-debugging
A step-by-step method for investigating and fixing software bugs: reproduce the problem, find its root cause, assess what it may affect, fix it, and test again.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - reproduce and isolate before changing anything.
debugging-and-recovery
Use when something is broken, a test is failing, behavior is wrong, when investigating a production incident, when a user reports a bug, when a feature works locally but not in another environment, or when the system behaves differently than the contract specifies.
performance-audit
Audit a Godot 4.x project for common performance pitfalls — repeated node lookups, process abuse, missing pooling, untyped collections, missing occlusion, heavy fragment shaders. Reports findings with severity and fix.
Pair Programming
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with…