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Found 131 Skills
Run a structured, adversarial multi-agent bug review pipeline on a codebase. Use this skill whenever the user wants to find bugs, audit code quality, review a codebase for issues, or run any kind of bug-finding or code analysis workflow. Also trigger when the user asks to 'review my code for bugs', 'find all issues in this repo', 'audit this codebase', or any similar request. The pipeline uses three sequential phases: a Bug Finder that maximizes issue discovery, a Bug Adversary that challenges false positives, and an Arbiter that issues final verdicts — producing a clean, high-confidence bug report.
System Audit - Proactively identify bug risks, security vulnerabilities, performance issues, maintainability debt, and architecture drift from code, and generate a batch list of findings. Triggers: Users say "review the system", "audit code", "scan for issues", "find bugs", "what can be optimized".
Generate and audit Microsoft Clarity browser instrumentation from the terminal. Trigger phrases: `generate a Clarity snippet`, `audit Clarity instrumentation`, `add Microsoft Clarity identify call`.
Use when code has been written and needs validation before committing, or when the user asks for a code review or security check.
Detect common Python vulnerabilities such as SQL injection, unsafe deserialization, and hardcoded secrets. Use as part of a secure SDLC for Python projects.
Audit repos for architectural drift, dead code, and abstraction bloat.
L3 Worker. Goal-based open-source replacement auditor: discovers custom modules (>100 LOC), analyzes PURPOSE via code reading, searches OSS alternatives via MCP Research (WebSearch, Context7, Ref), evaluates quality (stars, maintenance, license, CVE, API compatibility), generates migration plan.
Write and audit Python code comments using antirez's 9-type taxonomy. Two modes - write (add/improve comments in code) and audit (classify and assess existing comments with structured report). Use when users request comment improvements, docstring additions, comment quality reviews, or documentation audits. Applies systematic comment classification with Python-specific mapping (docstrings, inline comments, type hints).
Execute a micro-level NestJS code quality audit. Validates code against live GitHub standards for testing, architecture, DTO validation, error handling, and code implementation. Produces a detailed violations report with prioritized action plan. Use when the user asks to check NestJS code quality, validate best practices, or review backend code standards. Triggers on: 'nestjs best practices', 'backend code quality', 'code review', 'nestjs standards', 'dto validation', 'error handling review'.
Scan any codebase for 14 critical safety issues across security vulnerabilities, server stability (500 errors), and payment misconfigurations. Use when auditing code before deployment, reviewing AI-generated code for production readiness, or...
Detects entropy signals in a codebase: stale TODOs, disabled tests, lint suppressions, commented-out code, dead imports, empty catch blocks, and deprecated API usage. Designed for daily runs to catch quality erosion early. Do NOT use for feature work, refactoring planning, or security audits.