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.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 | $0.00035 | $0.01683 |
| Opus 5 | $0.00017 | $0.00842 |
| Sonnet 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 6d 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.
- 6d 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 5mo ago), licensed MIT. It adds 35 tokens to every session and 1,683 once invoked, about $0.0002 per session on Opus 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.
testing-api-authentication-weaknesses
Tests API authentication mechanisms for weaknesses including broken token validation, missing authentication on endpoints, weak password policies, credential stuffing susceptibility, token leakage in URLs or logs, and session management flaws. The tester evaluates JWT implementation, API key handling, OAuth flows, and…
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-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.