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 itallstartedwithaidea/agent-skills --skill agent-security-scanninggit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote 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/itallstartedwithaidea/agent-skills/agent-security-scanning)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/agent-security-scanning"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/agent-security-scanning.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 | $0.00020 | $0.01716 |
| Opus 5 | $0.00010 | $0.00858 |
| Sonnet 5 | $0.00004 | $0.00343 |
| Haiku 4.5 | $0.00002 | $0.00172 |
Grade C, and why
agent-security-scanning scanned grade C with 3 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.
Downloads and executes remote codemediumSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
for dangerous in ["rm -rf", "curl | sh", "wget | bash", "> /dev/"] Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Recursive force deletemediumDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
for dangerous in ["rm -rf", "curl | sh", "wget | bash", "> /dev/"] Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
for dangerous in ["rm -rf", "curl | sh", "wget | bash", "> /dev/"] How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Security Scanning
Part of Agent Skills™ by googleadsagent.ai™
Description
Agent Security Scanning detects vulnerabilities in AI-generated code before it reaches production. The agent applies OWASP Top 10 for LLM Applications, scans for known CVEs in dependencies, identifies prompt injection vectors, and flags insecure patterns specific to agent-generated code—such as unsanitized dynamic SQL, eval() usage, and unvalidated deserialization.
AI code generators produce code that "works" but frequently contains security vulnerabilities invisible to functional testing. Studies show that AI-generated code contains exploitable vulnerabilities at higher rates than human-written code, particularly in input validation, authentication, and cryptographic operations. This skill applies security analysis specifically calibrated for the patterns that AI agents produce.
The scanning pipeline covers three layers: static analysis of generated code (injection, XSS, SSRF), dependency vulnerability scanning (CVE database matching), and agent-specific threat modeling (prompt injection, tool misuse, data exfiltration through tool calls). Each finding includes a severity rating, CWE classification, and a concrete remediation with code example.
Use When
- Reviewing AI-generated code before committing or deploying
- Scanning dependencies for known CVEs after
npm installorpip install - Auditing agent tool call patterns for potential misuse
- Implementing security gates in CI/CD pipelines
- The user requests security review, vulnerability scan, or penetration testing
- Building applications that handle user input, authentication, or payments
How It Works
graph TD
A[AI-Generated Code] --> B[Layer 1: Static Analysis]
B --> C[Injection: SQL, XSS, SSRF, Command]
B --> D[Auth: Hardcoded Secrets, Weak Crypto]
B --> E[Data: PII Exposure, Logging Secrets]
A --> F[Layer 2: Dependency Scan]
F --> G[CVE Database Match]
F --> H[License Compliance]
A --> I[Layer 3: Agent Threat Model]
I --> J[Prompt Injection Vectors]
I --> K[Tool Call Audit]
I --> L[Data Exfiltration Paths]
C --> M[Severity Classification + CWE]
D --> M
E --> M
G --> M
J --> M
K --> M
L --> M
M --> N[Remediation Report]
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 · 182 lines · 20 tokens per session scan C 8e704c681b7a
agent-security-scanning is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (37 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 1,716 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 3 findings (downloads and executes remote code, recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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