Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/asgarovf/locusainpx agentmods add skills/asgarovf/locusai/security-auditWrote 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/asgarovf/locusai/security-audit)<a href="https://agentmods.dev/skills/asgarovf/locusai/security-audit"><img src="https://agentmods.dev/badge/skills/asgarovf/locusai/security-audit/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/asgarovf/locusai/security-audit"><img src="https://agentmods.dev/badge/skills/asgarovf/locusai/security-audit.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 | $0.00035 | $0.01683 |
| Opus 5.5 | $0.00014 | $0.00673 |
| Sonnet 5.5 | $0.00007 | $0.00337 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
security-audit scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
grep -rn "exec(\|spawn(\|execSync(\|child_process\|subprocess\|os\.system\|os\.popen" --include="*.ts" --include="*.py" --include="*.js" . How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Audit
When to use this skill
- Auditing a codebase for security vulnerabilities
- Reviewing authentication and authorization
- Checking for exposed secrets or credentials
- Assessing dependency security
- Hardening an application before deployment
- Responding to security alerts
Step 1: Secrets and credentials scan
# Search for hardcoded secrets
grep -rn "password\s*=\|api_key\s*=\|secret\s*=\|token\s*=" --include="*.ts" --include="*.py" --include="*.js" --include="*.env" .
grep -rn "AKIA[0-9A-Z]\{16\}" . # AWS access keys
grep -rn "sk-[a-zA-Z0-9]\{20,\}" . # API keys (OpenAI, Stripe, etc.)
grep -rn "ghp_[a-zA-Z0-9]\{36\}" . # GitHub personal access tokens
# Check for .env files in git
git ls-files | grep -i "\.env"
# Check .gitignore covers sensitive files
cat .gitignore | grep -i "env\|secret\|key\|credential"
Findings to flag:
- Any credential in source code (even in comments)
.envfiles tracked by git- Missing
.gitignoreentries for sensitive files
Step 2: Input validation (Injection attacks)
SQL Injection
# Find raw SQL queries
grep -rn "query(\`\|query(f\"\|execute(f\"\|\.raw(" --include="*.ts" --include="*.py" --include="*.js" .
grep -rn "\$\{.*\}" --include="*.sql" .
// VULNERABLE
db.query(`SELECT * FROM users WHERE id = ${userId}`);
// SAFE: Parameterized query
db.query('SELECT * FROM users WHERE id = $1', [userId]);
XSS (Cross-Site Scripting)
# Find dangerous HTML injection
grep -rn "innerHTML\|dangerouslySetInnerHTML\|v-html\|\|html(" --include="*.tsx" --include="*.jsx" --include="*.vue" --include="*.html" .
// VULNERABLE
element.innerHTML = userInput;
<div dangerouslySetInnerHTML={{ __html: userContent }} />
// SAFE
element.textContent = userInput;
// Or sanitize: DOMPurify.sanitize(userContent)
Command Injection
# Find shell command execution
grep -rn "exec(\|spawn(\|execSync(\|child_process\|subprocess\|os\.system\|os\.popen" --include="*.ts" --include="*.py" --include="*.js" .
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.
- 1mo ago First seen · 210 lines · 35 tokens per session scan A e2d8a42345cf
security-audit is a skill published in the GitHub repository asgarovf/locusai (23 stars, last pushed 7mo ago), licensed MIT. It adds 35 tokens to every session and 1,683 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
security-review
Security audit focused on OWASP Top 10 — injection, auth flaws, secrets exposure, IDOR, and more. Use when reviewing code for vulnerabilities, before deploying to production, or auditing a new codebase.
ship-safe
Run a full security audit on this project — 16 agents scan for secrets, injections, auth bypass, SSRF, supply chain, Supabase RLS, MCP security, agentic AI, RAG poisoning, PII compliance, and more. Use when the user wants a security audit, vulnerability scan, or asks if their code is safe to ship.
ship-safe-red-team
Run a multi-agent red team scan — 29 specialized security agents scan for 80+ attack classes including injection, auth bypass, SSRF, supply chain, Supabase RLS, MCP security, agentic AI, RAG poisoning, PII compliance, and more. Use when the user wants a deep security analysis beyond just secrets.
ship-safe-baseline
Manage your security baseline — accept current findings as known debt, then only report new regressions on future scans. Use when the user wants to adopt security scanning incrementally or suppress existing findings.
ship-safe-ci
Run Ship Safe in CI mode — compact output, exit codes, SARIF generation. Use when the user wants to set up CI/CD security gates or test their pipeline configuration.
ship-safe-deep
Run a deep security audit with LLM-powered taint analysis — regex scan nominates findings, then an LLM verifies taint reachability and exploitability. Use when the user wants thorough, high-confidence results with fewer false positives.