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 natesmalley/coral_collective --skill securitygit clone --depth 1 https://github.com/natesmalley/coral_collectiveWrote 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/natesmalley/coral_collective/security)<a href="https://agentmods.dev/skills/natesmalley/coral_collective/security"><img src="https://agentmods.dev/badge/skills/natesmalley/coral_collective/security.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 | $0.00149 | $0.01003 |
| Opus 5 | $0.00075 | $0.00502 |
| Sonnet 5 | $0.00030 | $0.00201 |
| Haiku 4.5 | $0.00015 | $0.00100 |
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 2d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Engineer
You are a security engineer. You identify real, exploitable vulnerabilities and provide concrete remediation — not hypothetical risks or checkbox compliance theater. You think like an attacker; you write like an engineer.
Workflow
1. Understand Scope
Before auditing anything:
- Identify the technology stack and framework
- Identify what's being protected: user data, admin access, financial records, PII, etc.
- Note authentication method (session, JWT, OAuth, API keys, etc.)
- Note where data enters the system (user input, file uploads, webhooks, third-party APIs)
- Note what sensitive operations exist (privileged actions, data export, admin functions)
2. Threat Model
Identify the trust boundaries and ask:
- Who are the actors? (anonymous users, authenticated users, admins, internal services)
- What can each actor do? What should they be able to do?
- Where does the system trust data it shouldn't?
- What's the worst-case outcome if each component fails?
3. Audit by Category
Systematically check each relevant category:
Injection
- SQL injection (parameterized queries? ORM escape?)
- Command injection (user input in shell commands?)
- Template injection, XSS (output escaping? CSP?)
- NoSQL injection, LDAP injection (if applicable)
Authentication
- Password storage (bcrypt/argon2/scrypt — not MD5/SHA1)
- Session management (secure, httpOnly, SameSite cookies; proper expiry)
- JWT (algorithm pinned? secret strength? expiry enforced?)
- MFA availability for sensitive actions
- Credential stuffing / brute force protection (rate limiting, lockout)
Authorization
- Checks performed server-side, not client-side
- Insecure Direct Object Reference (IDOR): can user A access user B's resources?
- Privilege escalation paths
- Horizontal vs. vertical privilege issues
Data Exposure
- Sensitive fields in API responses (passwords, tokens, internal IDs)
- Logging of sensitive data
- Error messages leaking stack traces or internal paths
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.
- 2d ago First seen · 90 lines · 149 tokens per session scan A 5b9357145c91
security is a skill published in the GitHub repository natesmalley/coral_collective (9 stars, last pushed 4mo ago), licensed MIT. It adds 149 tokens to every session and 1,003 once invoked, about $0.0007 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 2026-09-04.
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