Agent Skills [Beta]
A collection of skills for AI coding agents. Skills are packaged instructions and scripts that extend agent capabilities. This should be considered beta-level software; it's primarily generated by transforming open-source Semgrep rules into skill format.
Skills follow the Agent Skills format.
Installation
npx skills add semgrep/skills
Available Skills
code-security
Comprehensive code security guidelines from Semgrep Engineering covering OWASP Top 10, infrastructure security, and secure coding best practices across 15+ languages.
Use when:
- Writing new code
- Reviewing code for security vulnerabilities
- Asking about secure coding practices
- Configuring cloud infrastructure (Terraform, Kubernetes, Docker)
Categories covered:
| Impact | Category | Description |
|---|---|---|
| Critical | SQL Injection | Parameterized queries, ORM safety |
| Critical | Command Injection | Shell command safety, input validation |
| Critical | Cross-Site Scripting (XSS) | Output encoding, DOM safety |
| Critical | XML External Entity (XXE) | XML parser configuration |
| Critical | Path Traversal | File path validation |
| Critical | Insecure Deserialization | Safe deserialization patterns |
| Critical | Code Injection | Eval safety, template injection |
| Critical | Hardcoded Secrets | Environment variables, secret management |
| Critical | Memory Safety | Buffer overflows, use-after-free (C/C++) |
| High | Insecure Cryptography | Strong hashing (SHA-256+), encryption (AES) |
| High | Insecure Transport | HTTPS, certificate validation, TLS |
| High | Server-Side Request Forgery | URL validation, allowlists |
| High | JWT Authentication | Signature verification, algorithm safety |
| High | Cross-Site Request Forgery | CSRF tokens, SameSite cookies |
| High | Prototype Pollution | Object key validation (JavaScript) |
| High | Unsafe Functions | Dangerous function alternatives |
| High | Terraform AWS | S3, IAM, EC2, RDS security |
| High | Terraform Azure | Storage, App Service, Key Vault |
| High | Terraform GCP | GCS, GCE, GKE, IAM |
| High | Kubernetes | Pod security, RBAC, secrets |
| High | Docker | Non-root containers, image pinning |
| High | GitHub Actions | Script injection, action pinning |
| Medium | Regex DoS | Catastrophic backtracking prevention |
| Medium | Race Conditions | TOCTOU, secure temp files |
| Medium | Code Correctness | Common bugs, type errors |
| Low | Best Practices | Code quality patterns |
| Low | Performance | Efficiency anti-patterns |
| Low | Maintainability | Code organization |
Languages: Python, JavaScript/TypeScript, Java, Go, Ruby, PHP, C/C++, C#, Scala, Kotlin, Rust, HCL (Terraform), YAML (Kubernetes)
llm-security
Security guidelines for LLM applications based on the OWASP Top 10 for Large Language Model Applications 2025.
Use when:
- Building LLM-powered applications
- Implementing RAG systems
- Securing AI/ML pipelines
- Reviewing code that interacts with language models
Categories covered:
| Impact | Category | Description |
|---|---|---|
| Critical | Prompt Injection | Input validation, content segregation, output filtering |
| Critical | Sensitive Information Disclosure | PII detection, permission-aware RAG |
| Critical | Supply Chain | Model verification, safetensors, ML-BOM |
| Critical | Data and Model Poisoning | Training data validation, anomaly detection |
| Critical | Improper Output Handling | Context-aware encoding, parameterized queries |
| High | Excessive Agency | Least privilege, human-in-the-loop |
| High | System Prompt Leakage | External guardrails, no secrets in prompts |
| High | Vector and Embedding Weaknesses | Permission-aware retrieval, tenant isolation |
| High | Misinformation | RAG, fact verification, confidence scoring |
| High | Unbounded Consumption | Rate limiting, budget controls |
Frameworks: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF
semgrep
Run Semgrep static analysis scans and create custom detection rules for security vulnerabilities and bug patterns.
Use when:
- Running security scans with Semgrep
- Creating custom Semgrep rules
- Detecting specific vulnerability patterns
- Setting up Semgrep in CI/CD pipelines
Capabilities:
| Feature | Description |
|---|---|
| Quick Scans | Run semgrep --config auto or use curated rulesets |
| Rulesets | security-audit, owasp-top-ten, cwe-top-25, trailofbits |
| Custom Rules | Pattern matching and taint mode for data flow analysis |
| Test-Driven | Write test cases first with ruleid: and ok: annotations |
| CI/CD | GitHub Actions integration with diff-aware scanning |
Rule Creation Workflow:
- Analyze the vulnerability pattern
- Create test cases first (test-driven development)
- Analyze AST structure with
semgrep --dump-ast - Write the rule (taint mode for injection, pattern matching for syntax)
- Iterate until 100% tests pass
- Optimize patterns
When to use taint mode: SQL injection, command injection, XSS, path traversal, SSRF - any vulnerability where untrusted data flows to a dangerous sink.
Usage
Skills are automatically available once installed. The agent will use them when relevant tasks are detected.
Examples:
Review this React component for security issues
Help me implement input validation for my LLM chat endpoint
Create a Semgrep rule to detect hardcoded API keys in Python
Development
Building Skills
make install # Install dependencies
make validate # Validate all skills
make build # Build AGENTS.md for all skills
make zip # Create distribution packages
make # All of the above
Single Skill Operations
make validate-skill SKILL=code-security
make build-skill SKILL=llm-security
Skill Structure
Each skill contains:
SKILL.md- Instructions for the agentrules/- Individual rule files (for skills with rules)scripts/- Helper scripts for automation (optional)references/- Supporting documentation (optional)
Acknowledgments
Originally created by @DrewDennison at Semgrep. This work was heavily inspired by Vercel's React Best Practices.