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 agentmods add rules/project-vic-international/agentic-ai-development-course/course-contextgit clone --depth 1 https://github.com/Project-VIC-International/Agentic-AI-Development-CourseWrote 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/rules/project-vic-international/agentic-ai-development-course/course-context)<a href="https://agentmods.dev/rules/project-vic-international/agentic-ai-development-course/course-context"><img src="https://agentmods.dev/badge/rules/project-vic-international/agentic-ai-development-course/course-context.svg" alt="Measured on agentmods" 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 | $0.00982 | $0.00982 |
| Opus 5 | $0.00491 | $0.00491 |
| Sonnet 5 | $0.00196 | $0.00196 |
| Haiku 4.5 | $0.00098 | $0.00098 |
Grade A, and why
course-context 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 5d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Course Context
This repository contains training materials for the Agentic AI Development Course — a two-part session teaching Crimes Against Children (CAC) investigators how to build interoperable mission tools using agentic AI and open standards.
Audience
- Law enforcement investigators, forensic examiners, crime intelligence analysts, and prosecutors
- Working Crimes Against Children (CAC) and Internet Crimes Against Children (ICAC) cases
- No prior programming experience assumed
- Explanations must be in plain language with law-enforcement-relevant examples
Key Standards
All tools and examples in this course MUST be compliant with:
- UCO (Unified Cyber Ontology) — foundational cyber domain vocabulary
- Namespace:
https://ontology.unifiedcyberontology.org/uco/ - GitHub: https://github.com/ucoProject
- Namespace:
- CASE (Cyber-investigation Analysis Standard Expression) — cyber-investigation extension
- Namespace:
https://ontology.caseontology.org/case/ - GitHub: https://github.com/casework
- Namespace:
- CAC Ontology (Crimes Against Children Ontology) — domain-specific extension
- Namespace:
https://cacontology.projectvic.org/ - GitHub: https://github.com/Project-VIC-International/CAC-Ontology
- Namespace:
Critical Rules
- No real PII, case data, victim information, or suspect information in any file — all examples use synthetic data
- Interoperability is mandatory — all tool output must be CASE/UCO/CAC compliant
- CJIS compliance is mandatory — all tools handling CJI must meet FBI CJIS Security Policy requirements
- Encryption at all times — data at rest (AES-256) and in transit (TLS 1.2+), no exceptions
- Independent testing and validation — any tool MUST be independently tested and validated before operational use in law enforcement or digital forensics
- NIST 800-53 controls — use NIST SP 800-53 Rev. 5 as the security control framework; reference NIST cybersecurity overlays for law enforcement
- Explanations must be accessible — write for investigators, not for software engineers
- Search before building — always check existing projects before creating something new
- Apache 2.0 license — consistent with the CDO ecosystem
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.
- 5d ago First seen · 83 lines · 982 tokens per session scan A 55b31b7ac163
course-context is a cursor rule published in the GitHub repository Project-VIC-International/Agentic-AI-Development-Course (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 982 tokens to every session, about $0.0049 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 2026-08-31.
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