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Found 2,005 Skills
Review secret detection patterns and scanning workflows. Use for identifying high-signal secrets like AWS keys, GitHub tokens, and DB passwords. Use proactively during all security audits to scan code and history. Examples: - user: "Scan for secrets in this repo" → run high-signal rg patterns and gitleaks - user: "Check for AWS keys" → scan for AKIA patterns and server-side exposure - user: "Audit my .env files" → ensure secrets are gitignored and not committed - user: "Verify secret redaction" → check that reported secrets follow 4+4 format - user: "Scan build artifacts for keys" → search dist/ and build/ for secret patterns
Review, audit, and harden AI skills for security risks including prompt injection, hidden instructions, tool misuse, data exfiltration, and malicious payloads; use when analyzing SKILL.md, scripts, references, or assets for vulnerabilities and when producing remediation guidance.
Security audit enforcement for AI agents. Automated security scans and health verification.
Analyze system, application, and security logs for forensic investigation. Use when investigating security incidents, insider threats, system compromises, or any scenario requiring analysis of log data. Supports Windows Event Logs, Syslog, web server logs, and application-specific log formats.
Provides comprehensive KeyCloak administration guidance including realm management, user/group administration, client configuration, authentication flows, identity brokering, authorization policies, security hardening, and troubleshooting. Covers SSO configuration, SAML/OIDC setup, role-based access control (RBAC), user federation (LDAP/AD), social login integration, multi-factor authentication (MFA), and high availability deployments. Use when configuring KeyCloak, setting up SSO, managing realms and clients, troubleshooting authentication issues, implementing RBAC, or when users mention "KeyCloak", "SSO", "OIDC", "SAML", "identity provider", "IAM", "authentication flow", "user federation", "realm configuration", or "access management".
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
Security best practices and vulnerability prevention for Golang. Covers injection (SQL, command, XSS), cryptography, filesystem safety, network security, cookies, secrets management, memory safety, and logging. Apply when writing, reviewing, or auditing Go code for security, or when working on any risky code involving crypto, I/O, secrets management, user input handling, or authentication. Includes configuration of security tools.
Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
Parse, analyze, and process SARIF (Static Analysis Results Interchange Format) files. Use when reading security scan results, aggregating findings from multiple tools, deduplicating alerts, extracting specific vulnerabilities, or integrating SARIF data into CI/CD pipelines.
Verifies that git commits address security audit findings without introducing bugs. This skill should be used when the user asks to "verify these commits fix the audit findings", "check if TOB-XXX was addressed", "review the fix branch", "validate remediation commits", "did these changes address the security report", "post-audit remediation review", "compare fix commits to audit report", or when reviewing commits against security audit reports.
Perform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Covers security, performance, testing, and design review.
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.