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 caipe-io/ai-platform-engineering --skill incident-investigationgit clone --depth 1 https://github.com/caipe-io/ai-platform-engineeringWrote 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/caipe-io/ai-platform-engineering/incident-investigation)<a href="https://agentmods.dev/skills/caipe-io/ai-platform-engineering/incident-investigation"><img src="https://agentmods.dev/badge/skills/caipe-io/ai-platform-engineering/incident-investigation/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/caipe-io/ai-platform-engineering/incident-investigation"><img src="https://agentmods.dev/badge/skills/caipe-io/ai-platform-engineering/incident-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.00920 |
| Opus 5 | $0.00028 | $0.00460 |
| Sonnet 5 | $0.00011 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
incident-investigation 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Investigation
Perform multi-agent investigation by correlating PagerDuty incidents, Jira tickets, and ArgoCD deployment history to identify root cause and impacted systems.
Instructions
Phase 1: Gather Incident Data (PagerDuty Agent)
- Fetch active incidents - list all triggered and acknowledged incidents
- For each incident, collect:
- Incident ID, title, urgency, and status
- Service affected and escalation policy
- Triggered timestamp and duration
- Assigned responders and acknowledgment status
- Alert details and monitoring source
Phase 2: Correlate with Tickets (Jira Agent)
- Search for related Jira tickets using:
- Incident ID or service name in ticket descriptions
- Recent tickets with labels like `incident`, `outage`, `p0`, `p1`
- Tickets linked to the affected service or component
- For each related ticket, collect:
- Ticket key, summary, status, assignee
- Priority and labels
- Comments with recent updates
Phase 3: Check Recent Deployments (ArgoCD Agent)
- Search for recent deployments in the last 24 hours:
- Applications related to the affected service
- Any applications with recent sync operations
- Failed syncs or rollbacks
- For each deployment, collect:
- Application name and sync status
- Deployment timestamp
- Revision/commit that was deployed
- Sync result (success, failed, pruned resources)
Phase 4: Build Incident Timeline
- Merge all events into a chronological timeline:
- Deployments -> Alerts triggered -> Incident created -> Responses
- Identify correlations:
- Did a deployment happen shortly before the incident?
- Are multiple services affected (blast radius)?
- Is there a pattern (recurring incident)?
- Assess impact:
- Which services/teams are impacted?
- Customer-facing or internal only?
- Estimated time to resolution
Output Format
```markdown
Incident Investigation Report
Active Incidents
| Incident | Service | Urgency | Duration | Status |
|---|---|---|---|---|
| INC-1234 | payment-api | High | 45m | Acknowledged |
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 · 96 lines · 56 tokens per session scan A f72daebdf3fd
incident-investigation is a skill published in the GitHub repository caipe-io/ai-platform-engineering (408 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 920 once invoked, about $0.0003 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.
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