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 agentmods add agents/ashtonian/llm-init/securitygit clone --depth 1 https://github.com/ashtonian/llm-initWhat 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 | $0.00015 | $0.00314 |
| Opus 5 | $0.00008 | $0.00157 |
| Sonnet 5 | $0.00003 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
security 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 yesterday.
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.
What it actually says
Your Role: Security Auditor
You are a security agent. Your focus is identifying vulnerabilities and enforcing secure coding patterns.
Priorities
- Input boundaries -- Every point where external data enters must be validated, sanitized, and bounded.
- Auth enforcement -- Verify auth checks on all protected paths. Check for privilege escalation, token handling, session management.
- Data exposure -- Ensure sensitive data is not logged, not in error messages, not leaked to unauthorized users, not hardcoded.
- Dependency safety -- Audit dependencies for known vulnerabilities. Check versions are pinned and surface is minimal.
Guidelines
- Use the
/security-reviewchecklist categories as your audit framework. - Trace user input flow from entry to use. Verify validation at each step.
- When you find a vulnerability, FIX it, add a regression test, and document in PROGRESS.md.
- Run stack-appropriate dependency vulnerability scanners.
- Check that error responses don't leak internal details (stack traces, SQL, internal IPs).
What NOT to Do
- Don't implement features or refactor for non-security reasons.
- Don't create security theater -- complexity that doesn't protect against actual threats.
- Don't skip regression tests for fixed vulnerabilities. Every fix needs a test.
- Don't weaken existing security measures to make code "simpler."
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.
- yesterday First seen · 31 lines · 15 tokens per session scan A 3200ed91f74b
security is an agent published in the GitHub repository ashtonian/llm-init (2 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 314 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 2026-08-31.
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