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 dandye/ai-runbooks --skill triage-suspicious-logingit clone --depth 1 https://github.com/dandye/ai-runbooksWrote 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/dandye/ai-runbooks/triage-suspicious-login)<a href="https://agentmods.dev/skills/dandye/ai-runbooks/triage-suspicious-login"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/triage-suspicious-login/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/dandye/ai-runbooks/triage-suspicious-login"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/triage-suspicious-login.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 | $0.00050 | $0.01022 |
| Opus 5 | $0.00025 | $0.00511 |
| Sonnet 5 | $0.00010 | $0.00204 |
| Haiku 4.5 | $0.00005 | $0.00102 |
Grade A, and why
triage-suspicious-login 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 13d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suspicious Login Triage Skill
Guide initial triage of suspicious login alerts (impossible travel, untrusted location, multiple failed logins) for Tier 1 SOC Analysts.
Inputs
CASE_ID- SOAR case ID containing the alert(s)ALERT_GROUP_IDENTIFIERS- Alert group identifiers from the case- (Optional)
USER_ID- The user ID if known upfront - (Optional)
SOURCE_IP- The source IP if known upfront
Workflow
Step 1: Get Case Context
secops-soar.get_case_full_details(case_id=CASE_ID)
Step 2: Extract Key Entities
secops-soar.list_events_by_alert(case_id=CASE_ID, alert_id=ALERT_ID)
Parse events to extract:
USER_ID- The user accountSOURCE_IP- The login source IPHOSTNAME- The target/source hostname (if available)
Step 3: User Context (SIEM)
secops-mcp.lookup_entity(entity_value=USER_ID)
Record: Recent activity, first/last seen, related alerts.
Step 4: Source IP Enrichment
Use /enrich-ioc with IOC_TYPE="IP Address":
- GTI reputation and geolocation
- SIEM entity summary
- IOC match status
Step 5: Hostname Context (if available)
secops-mcp.lookup_entity(entity_value=HOSTNAME)
Step 6: Recent Login Activity
Search for login patterns over the last 96 hours:
secops-mcp.search_security_events(
text='metadata.event_type IN ("USER_LOGIN", "AUTH_ATTEMPT") AND principal.user.userid = "USER_ID"',
hours_back=96
)
Analyze for:
- Logins from unusual IPs
- Successful logins after failures
- Geographic anomalies (impossible travel)
- Concurrent sessions from different locations
Step 7: Check Related Cases
Use /find-relevant-case with search terms: [USER_ID, SOURCE_IP, HOSTNAME]
Step 8: (Optional) Identity Provider Check
If IDP tools available (e.g., Okta):
- Account status
- MFA enrollment
- Recent legitimate logins
- Password change history
Step 9: Synthesize & Document
Use /document-in-case with findings summary:
Suspicious Login Triage for USER_ID from SOURCE_IP:
- User SIEM Summary: [...]
- Source IP GTI: [reputation, geo]
- Login Pattern: [normal/anomalous]
- Related Cases: [...]
- Recommendation: [Close as FP | Escalate to Tier 2]
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.
- 13d ago First seen · 147 lines · 50 tokens per session scan A bd45371b70d1
triage-suspicious-login is a skill published in the GitHub repository dandye/ai-runbooks (126 stars, last pushed 29d ago), licensed Apache-2.0. It adds 50 tokens to every session and 1,022 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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