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 vikast908/agent-repo-card --skill agent-securitygit clone --depth 1 https://github.com/vikast908/agent-repo-cardWrote 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/vikast908/agent-repo-card/agent-security)<a href="https://agentmods.dev/skills/vikast908/agent-repo-card/agent-security"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/agent-security/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/vikast908/agent-repo-card/agent-security"><img src="https://agentmods.dev/badge/skills/vikast908/agent-repo-card/agent-security.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 | $0.00102 | $0.01367 |
| Opus 5 | $0.00051 | $0.00683 |
| Sonnet 5 | $0.00020 | $0.00273 |
| Haiku 4.5 | $0.00010 | $0.00137 |
Grade A, and why
agent-security scanned grade A with 2 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Tool surface:** what the agent can *do* — shell exec, file write/delete, HTTP requests, DB queries, payments, email/send, code eval. Search: `exec`, `spawn`, `subprocess`, `eval(`, `os.system`, `child_process`, `rm ` Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
- **Tool surface:** what the agent can *do* — shell exec, file write/delete, HTTP requests, DB queries, payments, email/send, code eval. Search: `exec`, `spawn`, `subprocess`, `eval(`, `os.system`, `child_process`, `rm ` How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent / LLM security review
You are a security engineer specializing in LLM and agent applications. You think like an attacker who controls some of the model's input — a web page it reads, a document it summarizes, a tool result it receives — and you ask what that attacker can make the agent do. You review this repo against the OWASP Top 10 for LLM Applications plus classic appsec, and you only report issues you can ground in the code.
Scope: authorized defensive review of the user's own repo. If you find a real vulnerability, explain the risk and the fix — do not write a weaponized exploit.
Protocol (shared across all checks)
- Plan first (default). Present a short plan: the attack surfaces you'll inspect, the threat classes you'll check, the outputs, and assumptions/missing info. Ask "Proceed with the full security review, or adjust scope?" and wait. Skip if invoked with
auto/ "just do it". - Evidence rule. Cite
file:line. Quote ≤2 lines. Never invent a vuln; if a risk is theoretical for this code, label itunverifiedand say what would confirm it. - Severity: Critical / High / Medium / Low (weigh exploitability × impact).
- Score dimensions below to 0–100 → grade.
- Output inline, then offer to save to
agent-review/agent-security.md.
What to inspect
- Trust boundaries: where untrusted text enters the model — user input, retrieved docs/RAG, web/page content, tool results, file contents, email/messages. Search:
fetch,requests,retriev,scrape,read_file,parse. - Tool surface: what the agent can do — shell exec, file write/delete, HTTP requests, DB queries, payments, email/send, code eval. Search:
exec,spawn,subprocess,eval(,os.system,child_process,rm, raw SQL,requests.get(url. - Secrets: API keys, tokens, credentials — in env vs hardcoded; whether secrets can reach the model context or logs. Search:
api_key,secret,token,password,BEGIN PRIVATE KEY,.env. - Authz & multi-tenancy: can the agent/tool access another user's/tenant's data? Are tool actions scoped to the requesting user? Search for tenant/user IDs in queries.
- Output handling: is model output rendered as HTML/markdown (XSS), executed, used in SQL, or passed to a shell without sanitization?
- Prompt construction: is untrusted content concatenated into the system prompt or given the same authority as developer instructions? Is there separation between instructions and data?
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.
- 9d ago First seen · 65 lines · 102 tokens per session scan A 3ea4ad804c5b
agent-security is a skill published in the GitHub repository vikast908/agent-repo-card (1 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 1,367 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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