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 obielin/responsible-ai-skills --skill ai-incident-responsegit clone --depth 1 https://github.com/obielin/responsible-ai-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/obielin/responsible-ai-skills/ai-incident-response)<a href="https://agentmods.dev/skills/obielin/responsible-ai-skills/ai-incident-response"><img src="https://agentmods.dev/badge/skills/obielin/responsible-ai-skills/ai-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/obielin/responsible-ai-skills/ai-incident-response"><img src="https://agentmods.dev/badge/skills/obielin/responsible-ai-skills/ai-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 | $0.00040 | $0.01871 |
| Opus 5 | $0.00020 | $0.00936 |
| Sonnet 5 | $0.00008 | $0.00374 |
| Haiku 4.5 | $0.00004 | $0.00187 |
Grade A, and why
ai-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 12d 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 -f https://your-ai-endpoint/health && echo "STILL RUNNING - escalate" || echo "System stopped" How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Incident Response
An AI incident is any event where an AI system's behaviour deviates from its intended, safe, and fair operation. Do not minimise, ignore, or quietly fix these. Follow this process every time.
Immediate Triage (First 15 Minutes)
1. Stop and Assess
Before doing anything else, answer these questions:
Is the system currently causing or about to cause active harm?
YES → Immediately escalate to P1. Skip to EMERGENCY SHUTDOWN.
NO → Continue triage.
Is the incident affecting real citizens / users right now?
YES → Treat as P1 or P2.
NO → Continue triage.
Is personal or sensitive data involved?
YES → ICO notification may be required within 72 hours.
NO → Continue triage.
2. Classify the Incident
| Type | Examples |
|---|---|
| Performance degradation | Accuracy drops significantly, response times increase |
| Fairness violation | Metrics show disproportionate impact on a group |
| Safety failure | Harmful, dangerous, or illegal output generated |
| Data incident | Unauthorised access, accidental disclosure, data poisoning |
| Manipulation | Prompt injection, adversarial attack, model inversion |
| Compliance failure | GDPR breach, ATRS not updated, override log missing |
3. Assign Severity
P1 — Critical: System actively causing harm. Shut down immediately.
P2 — High: Significant risk of harm. Pause within 4 hours.
P3 — Medium: Fairness or performance threshold exceeded. Fix within 24 hours.
P4 — Low: Anomaly detected. Investigate within 72 hours.
Emergency Shutdown (P1 Only)
Execute this in order. Do not skip steps.
# Step 1: Stop new requests immediately
# (Replace with your actual shutdown command)
systemctl stop ai-inference-service
# or: kubectl scale deployment ai-service --replicas=0
# or: disable the API gateway route
# Step 2: Verify the system is stopped
curl -f https://your-ai-endpoint/health && echo "STILL RUNNING - escalate" || echo "System stopped"
# Step 3: Preserve all logs — do NOT rotate or delete
cp -r /var/log/ai-service/ /var/log/ai-service-incident-backup-$(date +%Y%m%d%H%M%S)/
# Step 4: Activate manual fallback
# (Document your manual fallback procedure here)
echo "Activating manual fallback process — notify [team lead name]"
# Step 5: Notify emergency contacts immediately
# (Add your actual contacts)
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.
- 12d ago First seen · 286 lines · 40 tokens per session scan A c197daa2431e
ai-incident-response is a skill published in the GitHub repository obielin/responsible-ai-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,871 once invoked, about $0.0002 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 2026-08-31.
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