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 adriannoes/awesome-agentic-ai --skill investigating-insider-threat-indicatorsgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-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/adriannoes/awesome-agentic-ai/investigating-insider-threat-indicators)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/investigating-insider-threat-indicators"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/investigating-insider-threat-indicators/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/adriannoes/awesome-agentic-ai/investigating-insider-threat-indicators"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/investigating-insider-threat-indicators.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 | $0.00078 | $0.02954 |
| Opus 5.5 | $0.00031 | $0.01182 |
| Sonnet 5.5 | $0.00016 | $0.00591 |
| Haiku 4.5 | $0.00008 | $0.00295 |
Grade A, and why
investigating-insider-threat-indicators 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 1mo 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 — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigating Insider Threat Indicators
When to Use
Use this skill when:
- HR refers a departing employee for monitoring during their notice period
- DLP alerts indicate bulk data downloads or transfers to personal storage
- UEBA detects anomalous access patterns deviating significantly from peer baselines
- Management reports concerns about an employee accessing sensitive data outside their role
Do not use without proper legal authorization — insider threat investigations must be coordinated with HR, Legal, and Privacy teams before monitoring begins.
Prerequisites
- Legal authorization and HR referral documenting investigation justification
- SIEM with DLP, endpoint, email, proxy, and authentication log sources
- Data Loss Prevention (DLP) system (Microsoft Purview, Symantec, Forcepoint) with policy alerts
- Endpoint monitoring capability (EDR with USB/removable media logging)
- HR data feed providing employment status, notice dates, and access entitlements
- Chain of custody procedures for evidence preservation
Workflow
Step 1: Establish Investigation Scope and Legal Authorization
Before any monitoring, ensure proper authorization:
INSIDER THREAT INVESTIGATION AUTHORIZATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Case ID: IT-2024-0089
Subject: [Employee Name] — [Department]
Authorized By: [CISO / General Counsel]
Referral Source: HR — Employee submitted resignation, 2-week notice
Justification: Employee has access to trade secrets and customer PII
Scope: Email, file access, USB, cloud storage, printing
Duration: 2024-03-15 to 2024-03-29 (notice period)
Privacy Review: Completed — compliant with acceptable use policy
Step 2: Build Activity Timeline from SIEM
Query comprehensive activity for the subject:
index=* (user="jsmith" OR src_user="jsmith" OR sender="[email protected]"
OR SubjectUserName="jsmith")
earliest="2024-03-01" latest=now
| eval event_category = case(
sourcetype LIKE "%dlp%", "DLP",
sourcetype LIKE "%proxy%", "Web Access",
sourcetype LIKE "%email%", "Email",
sourcetype LIKE "%WinEventLog%", "Endpoint",
sourcetype LIKE "%o365%", "Cloud",
sourcetype LIKE "%vpn%", "VPN",
sourcetype LIKE "%badge%", "Physical Access",
1=1, sourcetype
)
| stats count by event_category, sourcetype, _time
| timechart span=1d count by event_category
What ships with it
3 files 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.
- 1mo ago First seen · 313 lines · 78 tokens per session scan A ed11964e8661
investigating-insider-threat-indicators is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (87 stars, last pushed 4d ago), licensed MIT. It adds 78 tokens to every session and 2,954 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
AI/ML Model Testing
Testing machine learning models including accuracy validation, bias detection, drift monitoring, A/B testing, and model regression testing.
parallel-investigation
Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist…
hypothesis-testing
Applies the scientific method to debugging by helping users form specific, testable hypotheses, design targeted experiments, and systematically confirm or reject theories to find root causes. Use when a user says their code isn't working, they're getting an error, something broke, they want to troubleshoot a bug, or…
root-cause-analysis
Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not…
bug-diagnosis
Use this skill to systematically diagnose the root cause of a bug before attempting a fix. Activates when a bug is reported, a test is failing, or unexpected behavior is observed. Prevents guess-and-check debugging that introduces additional bugs while chasing symptoms.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.