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 iradoweck/antigravity-awesome-skills --skill ai-sre-incident-responsegit clone --depth 1 https://github.com/iradoweck/antigravity-awesome-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/iradoweck/antigravity-awesome-skills/ai-sre-incident-response)<a href="https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/ai-sre-incident-response"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/ai-sre-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/iradoweck/antigravity-awesome-skills/ai-sre-incident-response"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/ai-sre-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 | $0.00032 | $0.03140 |
| Opus 5.5 | $0.00013 | $0.01256 |
| Sonnet 5.5 | $0.00006 | $0.00628 |
| Haiku 4.5 | $0.00003 | $0.00314 |
Grade A, and why
ai-sre-incident-response scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -o /dev/null -w "%{http_code}" https://api.provider.com/health This is a copy
100% identical to ai-sre-incident-response — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SRE Incident Response
Apply SRE rigor to AI systems where incidents include quality regressions, unsafe outputs, and budget explosions.
When to Use This Skill
- An LLM endpoint begins returning degraded or hallucinated answers
- Token spend spikes beyond budget thresholds
- A model provider goes down and traffic must fail over
- Safety guardrails fire at abnormal rates
- A new model deployment causes latency or accuracy regression
Prerequisites
- Prometheus and Alertmanager deployed with scrape targets for AI services
- Grafana dashboards for golden signals (latency, error rate, cost, quality)
- On-call rotation configured in PagerDuty, Opsgenie, or equivalent
- Runbook repository accessible to responders
- Rollback mechanism for model and prompt versions (GitOps or feature flags)
AI Incident Classes
- Availability incident: model/provider unavailable, timeout storm.
- Quality incident: answer accuracy or tool success drops below SLO.
- Safety incident: harmful or policy-violating outputs increase.
- Cost incident: unexpected token or provider spend spike.
Severity Framework
| Severity | Criteria | Response Time | Notification |
|---|---|---|---|
| SEV1 | User-facing outage, compliance risk, data leak | 5 min | Page on-call + incident commander |
| SEV2 | Major degradation in key flows | 15 min | Page on-call |
| SEV3 | Limited impact or internal-only issue | 1 hour | Slack alert |
| SEV4 | Cosmetic or low-priority regression | Next business day | Ticket |
Golden Signals for AI Services
- Request success rate
- Latency (queue + generation + tool execution)
- Hallucination/groundedness proxy metrics
- Cost per minute and per tenant
- Guardrail violation rate
Prometheus Alert Rules
The thresholds below are illustrative. Calibrate them to the declared SLO, metric units, tenant budgets and workload; a groundedness proxy is not a verified quality measurement. Counter rates are per second, so the cost-per-minute alert explicitly multiplies by 60.
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.
- 4d ago Changed · +1 lines 88764883b8bb
- 28d ago First seen · 337 lines · 32 tokens per session scan A b9654a5db9e0
ai-sre-incident-response is a skill published in the GitHub repository iradoweck/antigravity-awesome-skills (30 stars, last pushed 5d ago), licensed MIT. It adds 32 tokens to every session and 3,140 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to ai-sre-incident-response, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG…
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…