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 batu-sonmez/infraclaude --skill incident-responsegit clone --depth 1 https://github.com/batu-sonmez/infraclaudeWrote 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/batu-sonmez/infraclaude/incident-response)<a href="https://agentmods.dev/skills/batu-sonmez/infraclaude/incident-response"><img src="https://agentmods.dev/badge/skills/batu-sonmez/infraclaude/incident-response/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/batu-sonmez/infraclaude/incident-response"><img src="https://agentmods.dev/badge/skills/batu-sonmez/infraclaude/incident-response.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 | $0.00000 | $0.00593 |
| Opus 5 | $0.00000 | $0.00296 |
| Sonnet 5 | $0.00000 | $0.00119 |
| Haiku 4.5 | $0.00000 | $0.00059 |
Grade A, and why
incident-response 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SRE Incident Response Workflow
Systematic incident response workflow using InfraClaude tools. Follow these phases in order.
Phase 1: Detect & Assess
Goal: Understand the scope and severity of the incident.
- Check alerts:
prom_active_alerts— what's firing? - Check cluster health:
k8s_get_podsacross namespaces — any pods unhealthy? - Check events:
k8s_get_eventswith type=Warning — what recent warnings exist? - Assess severity:
- SEV1: Complete service outage or data loss risk
- SEV2: Degraded service affecting users
- SEV3: Minor degradation, workaround available
- SEV4: Non-user-facing issue
Phase 2: Triage
Goal: Identify what changed and what's affected.
- Check recent deployments:
k8s_get_deployments— any recent rollouts? - Check rollout status:
k8s_rollout_statusfor recently deployed services - Check resource usage:
k8s_top_podsandk8s_top_nodes - Check metrics:
prom_instant_queryfor error rates and latency - Identify the blast radius — which services and users are affected?
Phase 3: Mitigate
Goal: Restore service as quickly as possible. Fix it first, understand later.
Possible mitigations (in order of preference):
- Rollback: If a recent deployment caused the issue, use
k8s_rollback_deployment - Scale up: If capacity is the issue, use
k8s_scale_deployment - Restart: If a transient issue, delete the problematic pod with
k8s_delete_pod - Cordon node: If a node is unhealthy, use
k8s_cordon_node
Phase 4: Resolve
Goal: Implement a proper fix.
- Analyze logs:
k8s_get_pod_logs— look for root cause in error logs - Check for code issues, configuration errors, or infrastructure problems
- Deploy a fix if the root cause is identified
- Verify the fix:
k8s_rollout_statusandprom_instant_queryfor error rates
Phase 5: Post-Mortem
Goal: Learn from the incident and prevent recurrence.
Gather data for the post-mortem:
- Timeline of events from
k8s_get_events - Metrics from
prom_range_queryshowing the incident window - Deployment history from rollout status
- Document:
- What happened
- Why it happened
- How it was detected
- How it was fixed
- Action items to prevent recurrence
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 · 61 lines · 0 tokens per session scan A 1f0b6649559a
incident-response is a skill published in the GitHub repository batu-sonmez/infraclaude (0 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 593 tokens. 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.
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