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 agentmods add skills/uptimerobot/ai/incident-responsenpx skills add uptimerobot/ai --skill incident-responsegit clone --depth 1 https://github.com/uptimerobot/aiWrote 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/uptimerobot/ai/incident-response)<a href="https://agentmods.dev/skills/uptimerobot/ai/incident-response"><img src="https://agentmods.dev/badge/skills/uptimerobot/ai/incident-response.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.01785 |
| Opus 5 | $0.00013 | $0.00892 |
| Sonnet 5 | $0.00005 | $0.00357 |
| Haiku 4.5 | $0.00003 | $0.00178 |
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 6d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident response
Preflight — read first. If you cannot see any
uptimerobot:*MCP tools in your tool list, invoke theuptimerobot:setupskill before doing anything else. Do not tell the user the MCP is misconfigured —setup's Step 0 detects the common case (server connected, tools loaded after session start) and resolves it without re-keying.
The full loop a user runs when something's on fire:
- Find what's down (
list-monitorswithDOWNfilter). - Pull incident details for the worst offenders (
get-incident-details). - Optionally silence flapping monitors while the underlying issue is being fixed (
update-monitor-status→PAUSED). - Log findings as you go with incident comments (requires the
incident-commentsplan feature). - After the fix, verify recovery with
get-monitor-statsand/orget-response-times.
Use this when the user says "what's down?", "is production still broken?", "triage the alerts", or "show me the outage".
Step 1 — Find what's down
{ "filter": ["DOWN"], "limit": 50 }
Paginate until hasMore: false. Group the results by tag or friendly-name prefix before reporting — the user typically cares "what system is down", not "which individual checks are failing".
Also surface NOT_STARTED monitors on request — they're not actively monitored but show up in downtime reports.
Active-only view
To exclude paused and unstarted:
{ "filter": ["DOWN"], "limit": 50 }
(DOWN by itself excludes PAUSED / NOT_STARTED automatically.)
Step 2 — Pull recent incidents
For each down monitor that the user wants to dig into:
{ "monitorId": 800123456, "timeRange": "24h", "limit": 5 }
Returns a list of incidents (active and resolved). Note incidentId is a string.
Lead with the active (unresolved) incident, then show recently-resolved ones as flap history.
Step 3 — Diagnose one incident
{ "incidentId": "inc_abcdef1234567890" }
Returns:
- Per-checker probe results (location, IP, HTTP status, error kind, response body snippet).
- Traceroute hops where available.
- Start time, duration, and root-cause category if assigned.
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.
- 6d ago First seen · 167 lines · 25 tokens per session scan A 3ffb691cee5d
incident-response is a skill published in the GitHub repository uptimerobot/ai (24 stars, last pushed 23d ago), licensed MIT. It adds 25 tokens to every session and 1,785 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 2026-08-30.
Other skills, from other repositories
sre_triage
SRE first-response triage for distributed training incidents. Automates the manual checks from PyTorch/NCCL debugging runbooks.
incident-response
Incident triage, cascade prevention, and postmortem methodology. Use when handling production incidents, designing resilience patterns, or conducting chaos engineering exercises.
APIMon
Monitor API endpoints and track response times to catch outages. Use when checking uptime, validating schemas, or generating status reports.
issue-triage
Issue triage: audit open issues, categorize, detect duplicates, cross-ref PRs, risk assessment, post comments. Args: "all" for deep analysis of all, issue numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
rtk-triage
Triage complet RTK : exécute issue-triage + pr-triage en parallèle, puis croise les données pour détecter doubles couvertures, trous sécurité, P0 sans PR, et conflits internes. Sauvegarde dans claudedocs/RTK-YYYY-MM-DD.md. Args: "en"/"fr" pour la langue (défaut: fr), "save" pour forcer la sauvegarde.
agent-qa-result-triage
Triage failed Agent QA runs with MCP evidence, artifacts, logs, fixed failure categories, confidence, and actionable next steps.