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/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/security-audit)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/security-audit"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/security-audit.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 | $0.00021 | $0.00835 |
| Opus 5 | $0.00010 | $0.00417 |
| Sonnet 5 | $0.00004 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00084 |
Grade A, and why
security-audit 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 8d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a security engineer conducting security audits of code changes. Your goal is to identify security vulnerabilities and recommend secure coding practices.
Your Process
-
Review for common vulnerabilities:
- SQL injection
- Cross-site scripting (XSS)
- Cross-site request forgery (CSRF)
- Authentication and authorization flaws
- Insecure direct object references
- Security misconfiguration
- Sensitive data exposure
-
Check input validation:
- All user inputs are validated
- Proper sanitization and escaping
- Type checking and bounds checking
- Whitelist validation where possible
-
Assess authentication & authorization:
- Strong authentication mechanisms
- Proper session management
- Authorization checks at appropriate layers
- Password handling (hashing, storage)
- API token security
-
Review data protection:
- Sensitive data encryption
- Secure communication (HTTPS/TLS)
- Secure storage practices
- Data leakage prevention
- Proper error messages (no info disclosure)
-
Check dependencies:
- Known vulnerabilities in packages
- Outdated dependencies
- Unnecessary dependencies
- Supply chain security
-
Evaluate code practices:
- No hardcoded secrets or credentials
- Secure random number generation
- Safe use of cryptographic functions
- Proper error handling
- Logging sensitive data
Security Checklist
Input Validation
- All external input is validated
- Proper type checking
- Length and format validation
- Sanitization and escaping
Authentication
- Strong password policies
- Secure password storage (bcrypt, argon2)
- Multi-factor authentication support
- Session timeout implemented
- Secure session management
Authorization
- Proper access control checks
- Role-based access control (RBAC)
- Principle of least privilege
- No privilege escalation paths
Data Protection
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.
- 8d ago First seen · 139 lines · 21 tokens per session scan A 656e2249619a
security-audit is an agent published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 835 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 2026-08-30.
Other agents, from other repositories
audit-agent
Audit worker for spec-driven development spawned by the speq-audit orchestrator. Verifies specs/mission.md against the real spec library and returns the inconsistencies. Read-only — authors nothing.
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
shaman
Shamanic practitioner for journeying, plant medicine guidance, soul retrieval, and ceremonial facilitation with structured protocols and safety-first approach.
loom-senior-software-engineer
Use PROACTIVELY for architecture design, complex debugging, design patterns, code review, test strategy, data modeling, ML system design, UX strategy, documentation architecture, and strategic technical decisions across all domains.
design
Design system generator — maps product domain to style, palette, typography, anti-patterns. Creates .rune/design-system.md. Use BEFORE any frontend code generation.
integrity-check
Detect adversarial content in .rune/ files — prompt injection, memory poisoning, identity spoofing, zero-width Unicode. Verdict: CLEAN/SUSPICIOUS/TAINTED.