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 agenisea/ai-design-engineering-cc-plugins --skill argusgit 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/skills/agenisea/ai-design-engineering-cc-plugins/argus)<a href="https://agentmods.dev/skills/agenisea/ai-design-engineering-cc-plugins/argus"><img src="https://agentmods.dev/badge/skills/agenisea/ai-design-engineering-cc-plugins/argus.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 5.1 | $0.00043 | $0.00574 |
| Opus 5 | $0.00022 | $0.00287 |
| Sonnet 5 | $0.00009 | $0.00115 |
| Haiku 4.5 | $0.00004 | $0.00057 |
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 7d 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 — 63 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.
Your job: Take an agentic application description and produce a comprehensive security architecture with layered defenses, real-time auditing, and resilient safeguards that cannot be bypassed.
Research First
Before generating the security blueprint, research using available tools:
- Preferred: Built-in
WebSearchtool if available
Research the following:
- OWASP guidelines - AI/ML system security standards
- Agent security patterns - Production implementations
- Authentication best practices - JWT, API keys, zero-trust
- Audit logging standards - Compliance and forensics
- Threat modeling - Attack vectors for agentic systems
Your Outputs
- 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
Defense Layers
- Perimeter: API gateway, rate limiting, input validation
- Identity: Agent authentication, JWT validation, credential rotation
- Authorization: Role-based access, capability tokens, least privilege
- Data: Encryption at rest/transit, PII handling, data isolation
- Audit: Comprehensive logging, tamper-proof trails, real-time alerts
- Recovery: State snapshots, rollback procedures, incident response
Security Principles
- No single point of failure
- Defense in depth
- Least privilege
- Zero trust
- Idempotent by default
- Audit everything
Common Vulnerabilities
- Prompt injection - Malicious input manipulating agent behavior
- Privilege escalation - Agents exceeding authorized capabilities
- State corruption - Race conditions, inconsistent data
- Credential leakage - Secrets exposed in logs or responses
- Denial of service - Resource exhaustion, infinite loops
- Data exfiltration - Unauthorized access to sensitive information
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.
- 7d ago First seen · 63 lines · 43 tokens per session scan A 341b401a5fc5
argus is a skill published in the GitHub repository agenisea/ai-design-engineering-cc-plugins (26 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 574 once invoked, about $0.0002 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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