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 arbazkhan971/godmode --skill incidentgit clone --depth 1 https://github.com/arbazkhan971/godmodeWrote 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/arbazkhan971/godmode/incident)<a href="https://agentmods.dev/skills/arbazkhan971/godmode/incident"><img src="https://agentmods.dev/badge/skills/arbazkhan971/godmode/incident.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.01939 |
| Opus 5 | $0.00030 | $0.00970 |
| Sonnet 5 | $0.00012 | $0.00388 |
| Haiku 4.5 | $0.00006 | $0.00194 |
Grade A, and why
incident 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 "http://localhost:9090/api/v1/query?\ How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident — Incident Response & Post-Mortem
Activate When
- User invokes
/godmode:incident - User reports production outage or degradation
- User says "production is down", "write a post-mortem"
- Monitoring alerts or PagerDuty notifications shared
Workflow
Step 1: Incident Classification
# Check recent deployments (common root cause)
git log --oneline --since="2 hours ago" | head -10
# Check error rates if monitoring accessible
curl -s "http://localhost:9090/api/v1/query?\
query=rate(http_requests_total{code=~'5..'}[5m])" \
2>/dev/null | head -5
INCIDENT CLASSIFICATION:
ID: INC-<YYYY-MM-DD>-<NNN>
Title: <concise impact description>
Severity: <SEV1 | SEV2 | SEV3 | SEV4>
Status: INVESTIGATING | IDENTIFIED | MONITORING | RESOLVED
SEVERITY MATRIX:
| Level | Impact | Response Time |
|-------|-----------------|---------------|
| SEV1 | Complete outage | < 15 min |
| SEV2 | Major degradation| < 30 min |
| SEV3 | Partial degradation| < 2 hours |
| SEV4 | Minimal impact | Next business day|
IF error rate > 50%: SEV1
IF error rate 10-50% or major feature broken: SEV2
IF error rate 1-10% or workaround exists: SEV3
IF cosmetic or < 1% impact: SEV4
Step 2: Timeline Construction
INCIDENT TIMELINE — INC-<ID>:
| Timestamp (UTC) | Event |
|-----------------|--------------------------|
| HH:MM:SS | First alert triggered |
| HH:MM:SS | On-call acknowledged |
| HH:MM:SS | Root cause identified |
| HH:MM:SS | Mitigation applied |
| HH:MM:SS | Service restored |
| HH:MM:SS | Incident resolved |
EVIDENCE per entry:
- Monitoring dashboards (screenshots/links)
- Log snippets with timestamps
- Deploy records (commit SHA, timestamp)
- Customer reports / support tickets
Step 3: Impact Assessment
IMPACT:
Duration: <start> to <end> (<total minutes>)
Users affected: <number or percentage>
Requests failed: <number or error rate %>
Revenue impact: <estimated $ or unknown>
SLA consumed: <budget used, remaining>
Data impact: <lost, corrupted, exposed, or NONE>
THRESHOLDS:
MTTD target: < 5 minutes (symptom to alert)
MTTA target: < 15 minutes (alert to response)
MTTR target: < 60 minutes (detection to resolution)
IF MTTR > 120 min for SEV1: escalate process review
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 First seen · 258 lines · 60 tokens per session scan A f200267fe451
incident is a skill published in the GitHub repository arbazkhan971/godmode (26 stars, last pushed 10d ago), licensed MIT. It adds 60 tokens to every session and 1,939 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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