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.
git clone --depth 1 https://github.com/latestaiagents/agent-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/commands/latestaiagents/agent-skills/incident-response)<a href="https://agentmods.dev/commands/latestaiagents/agent-skills/incident-response"><img src="https://agentmods.dev/badge/commands/latestaiagents/agent-skills/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/commands/latestaiagents/agent-skills/incident-response"><img src="https://agentmods.dev/badge/commands/latestaiagents/agent-skills/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 | $0.00014 | $0.00698 |
| Opus 5 | $0.00007 | $0.00349 |
| Sonnet 5 | $0.00003 | $0.00140 |
| Haiku 4.5 | $0.00001 | $0.00070 |
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 9d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/incident-response
Initiate a structured incident response workflow with automated triage, stakeholder communication, and resolution tracking.
What I Need
Tell me about the incident:
- What's broken? (service name, error messages, symptoms)
- When did it start? (or when was it detected)
- What's the impact? (users affected, revenue impact, SLA breach risk)
- Severity level if known (SEV1-4)
Workflow
Step 1: Rapid Triage (2 minutes)
I'll analyze the incident and determine:
┌─────────────────────────────────────────────────────────────┐
│ INCIDENT TRIAGE │
├─────────────────────────────────────────────────────────────┤
│ Service: [affected service] │
│ Started: [timestamp] │
│ Severity: SEV-[1-4] │
│ Impact: [user/business impact] │
│ Blast Radius: [affected systems/regions] │
├─────────────────────────────────────────────────────────────┤
│ Initial Hypothesis: [likely cause based on symptoms] │
│ Recommended Actions: [immediate steps] │
└─────────────────────────────────────────────────────────────┘
Step 2: Incident Channel Setup
I'll help you:
- Create incident Slack channel (#inc-YYYYMMDD-service)
- Post initial incident summary
- Page on-call if not already done
- Set up incident document
Step 3: Investigation
Using connected observability tools, I'll:
- Query recent deployments (GitHub)
- Check error rates and latency (Datadog/Prometheus)
- Review recent alerts (PagerDuty)
- Examine logs for error patterns (CloudWatch)
- Check infrastructure changes (Terraform/K8s)
Step 4: Mitigation Options
Based on investigation, I'll suggest:
| Option | Risk | Time | Recommendation |
|---|---|---|---|
| Rollback | Low | 5min | If recent deploy |
| Scale up | Low | 2min | If capacity issue |
| Failover | Medium | 10min | If region issue |
| Hotfix | High | 30min+ | If code bug |
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.
- 9d ago First seen · 98 lines · 14 tokens per session scan A 3ba7c6b66345
incident-response is a command published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 698 once invoked, about $0.0001 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 commands, from other repositories
fdk-react-create
Create a new Platform 3.0 React Meta app (default UI stack). Uses fdk create react-starter-template or react-meta skeletons with DEW components, metaConfig in manifest.json, and React Router.
fdk-refactor
Reduce function complexity in a Freshworks app to meet cyclomatic complexity ≤ 7 per function. Extracts helper functions, simplifies conditionals, and preserves behavior while improving code quality.
fw-setup-uninstall
Uninstall FDK completely — keeps Node.js and nvm (/fw-setup uninstall).
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.