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 LgrappaG/Workflows-Agents --skill ai-threat-assessmentgit clone --depth 1 https://github.com/LgrappaG/Workflows-AgentsWrote 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/lgrappag/workflows-agents/ai-threat-assessment)<a href="https://agentmods.dev/skills/lgrappag/workflows-agents/ai-threat-assessment"><img src="https://agentmods.dev/badge/skills/lgrappag/workflows-agents/ai-threat-assessment/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/lgrappag/workflows-agents/ai-threat-assessment"><img src="https://agentmods.dev/badge/skills/lgrappag/workflows-agents/ai-threat-assessment.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 | $0.00013 | $0.00258 |
| Opus 5 | $0.00006 | $0.00129 |
| Sonnet 5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
ai-threat-assessment 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 10d 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.
What it actually says
Ai Threat Assessment
Implement threat evaluation system for dynamic AI responses
Risk Level
MEDIUM
Core Rules
- Tune threat evaluation
- balance difficulty
- Test thoroughly before deploying
Response Pattern
When Using This Skill
- Configure threat system
- Validate the implementation
- Test edge cases and error conditions
- Ensure performance meets requirements
Usage Contexts
- AI decision making
- threat prioritization
What NOT to Do
- Inappropriate threat response
- player frustration
- Deploy without testing
Key Requirements
- Understand the use cases before application
- Follow the documented response pattern
- Validate results in the target environment
- Monitor for performance impact
Further Learning
Review related skills and documentation for deeper understanding of related systems and best practices.
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.
- 10d ago First seen · 49 lines · 13 tokens per session scan A de2e6139cd6b
ai-threat-assessment is a skill published in the GitHub repository LgrappaG/Workflows-Agents (2 stars, last pushed 4mo ago), licensed MIT. It adds 13 tokens to every session and 258 once invoked, about $0.0001 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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