bitlysis-security-audit
Bitlysis — Security Audit & Best Practices
When to Use
- Reviewing authentication or authorization implementations
- Auditing API endpoints for security vulnerabilities
- Reviewing smart contracts before deployment
- Checking for OWASP Top 10 vulnerabilities
- Pre-production security review
- Dependency vulnerability scanning
Instructions
Step 1: Authentication & Authorization
// ✅ Use getUser() not getSession() for server-side auth (prevents CSRF)
import { createClient } from "@/lib/supabase/server";
More from bernieweb3/bitlysis-skills
bitlysis-testing
Write comprehensive tests for Bitlysis using Vitest (TypeScript), pytest (Python), and Playwright (E2E). Use when adding unit tests, integration tests, E2E tests, or when improving test coverage for any part of the codebase.
1bitlysis-ai-agents-llm
Apply best practices for LLM applications, AI agents, RAG pipelines, and evaluations in Bitlysis. Use when building agentic workflows, integrating LLM APIs, designing RAG systems, writing eval test cases, or working in agent/llm/rag/eval/mcp directories.
1bitlysis-infrastructure
Apply Bitlysis DevOps and infrastructure best practices — Docker multi-stage builds, GitHub Actions CI/CD, environment management, Render and Vercel deployment, security headers, and observability. Use when working on Dockerfiles, CI workflows, deployment configs, or infrastructure code.
1bitlysis-fullstack
Apply Bitlysis fullstack development standards for Next.js 15 (App Router), React 19, FastAPI, Drizzle ORM, and TanStack Query. Use when building frontend components, API endpoints, database schemas, or full-stack features.
1bitlysis-project-core
Apply Bitlysis core project standards — tech stack validation, naming conventions, quality gates, and specialist agent routing. Use this skill before starting any feature, refactor, or debugging session in the Bitlysis codebase.
1bitlysis-data-science
Apply Bitlysis data science best practices for R, Python, Quarto, and reproducible analysis pipelines. Use when working on statistical analysis, data pipelines, notebooks, R packages, or any file with .R, .Rmd, .qmd, or .ipynb extensions.
1