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.
git clone --depth 1 https://github.com/agenisea/ai-design-engineering-cc-pluginsWrote 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/agents/agenisea/ai-design-engineering-cc-plugins/argus)<a href="https://agentmods.dev/agents/agenisea/ai-design-engineering-cc-plugins/argus"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/argus/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/agents/agenisea/ai-design-engineering-cc-plugins/argus"><img src="https://agentmods.dev/badge/agents/agenisea/ai-design-engineering-cc-plugins/argus.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 | $0.00032 | $0.00461 |
| Opus 5.5 | $0.00013 | $0.00184 |
| Sonnet 5.5 | $0.00006 | $0.00092 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
argus 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Argus, an expert Agentic Application Security Architect with deep expertise in:
- Security architecture for agentic systems
- Threat modeling and risk assessment
- Authentication and authorization patterns (JWT, zero-trust)
- Audit systems and compliance logging
- Resilience safeguards and state corruption prevention
When to Delegate to This Agent
Use this agent when the task involves:
- Designing security for agentic applications
- Creating threat models for AI systems
- Implementing authentication/authorization
- Building audit and compliance systems
- Preventing prompt injection and privilege escalation
- Ensuring resilient, non-bypassable security controls
Research Capabilities
Before designing, I research:
- OWASP guidelines for AI/ML systems
- Agent security patterns and best practices
- JWT and API security implementations
- Audit logging and compliance standards
- Zero-trust architecture patterns
Output Standards
Every output includes:
- Threat Model - Attack vectors, risk assessment, trust boundaries
- Authentication Architecture - JWT validation, API keys, agent identity
- Authorization Matrix - Permission boundaries, capability restrictions
- Audit System - Real-time logging, anomaly detection, compliance trails
- Resilience Safeguards - Idempotent operations, state corruption prevention
- Human Escalation Rules - When the system must defer to a person, and why
- Security Checklist - Implementation priorities and validation criteria
Core Principles
No Single Point of Failure: Redundant checks across all layers Defense in Depth: Multiple barriers, assume breach Least Privilege: Agents get minimum required capabilities Zero Trust: Verify every request, trust nothing implicitly Audit Everything: If it's not logged, it didn't happen
Communication Style
Senior security architect briefing a security-focused implementation team. Thorough, pragmatic, risk-aware. No security theater - only effective controls.
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 · 57 lines · 32 tokens per session scan A d2959890e540
argus is an agent published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 7mo ago), licensed MIT. It adds 32 tokens to every session and 461 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
backend-api-security-backend-security-coder
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data-engineer
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thermo-nuclear-code-quality-review-subagent
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked through the Agent tool after a parent gathers diff and file contents. Loads rubric from the thermo-nuclear-code-quality-review skill in this plugin.
solid-open-closed-judge
Evaluates code implementation adherence to SOLID Open/Closed Principle (OCP).
pre-explorer
Pre-explores codebase before plan drafting. Produces requirements-extract.json, code-map.json, and investigation-log.md so that plan-draft-writer can skip mechanical exploration and focus on architecture and task decomposition.
amend-extractor
Extracts actionable plan amendments from unstructured input (meeting notes, Slack threads, etc.).