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 vignesh2027/AI-AGENT-SKILLS --skill security-and-hardeninggit clone --depth 1 https://github.com/vignesh2027/AI-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/vignesh2027/ai-agent-skills/security-and-hardening)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/security-and-hardening"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/security-and-hardening/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/vignesh2027/ai-agent-skills/security-and-hardening"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/security-and-hardening.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 | $0.00019 | $0.00926 |
| Opus 5 | $0.00010 | $0.00463 |
| Sonnet 5 | $0.00004 | $0.00185 |
| Haiku 4.5 | $0.00002 | $0.00093 |
Grade A, and why
security-and-hardening 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 11d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Security is not a feature you add — it is a property you maintain. Most security failures are not sophisticated attacks; they are missing input validation, exposed secrets, misconfigured permissions, and unpatched dependencies. This skill systematically eliminates these before they reach production.
When to Use
- Before merging any PR that touches auth, data storage, user input, or external APIs
- When adding a new endpoint, form, or data type
- When setting up or modifying infrastructure
- As part of the
/reviewworkflow
Process
Step 1: Threat model the change
Ask: what can an attacker do with this change? What data does it touch? What systems does it connect to? Who has access? Threat model in 10 minutes with STRIDE:
- Spoofing — Can someone impersonate a legitimate user?
- Tampering — Can someone modify data they shouldn't?
- Repudiation — Can someone deny they took an action?
- Information Disclosure — Can someone read data they shouldn't?
- Denial of Service — Can someone block legitimate access?
- Elevation of Privilege — Can someone gain permissions they shouldn't have?
Step 2: Input validation
- Validate all inputs at the boundary (before processing or storage)
- Validate type, length, format, and range
- Reject invalid inputs — don't sanitize and continue
- Parameterize all database queries (no string concatenation)
- Encode all outputs for their context (HTML, SQL, shell, JSON)
Step 3: Authentication and authorization
- Verify authentication on every request (don't cache auth state across requests)
- Check authorization on every resource access (not just at the route level)
- Use principle of least privilege: request only the permissions you need
- Implement rate limiting on auth endpoints
- Ensure session tokens are invalidated on logout
Step 4: Secrets management
- No secrets in code, commits, or logs
- Use environment variables or a secrets manager
- Rotate secrets after any exposure (assume exposure if committed to git)
- Verify:
git log --all -p | grep -i "password\|secret\|token\|key"— if anything shows, rotate immediately
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.
- 11d ago First seen · 95 lines · 19 tokens per session scan A d111483d9df8
security-and-hardening is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 13d ago), licensed MIT. It adds 19 tokens to every session and 926 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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