productivity▌

$20

Analyzes codebases to generate comprehensive architectural documentation with diagrams and implementation patterns. \n \n Auto-detects technology stacks (.NET, Java, React, Angular, Python, Node.js, Flutter) and architectural patterns (Clean Architecture, Microservices, Layered, MVVM, Hexagonal, Event-Driven, Serverless, Monolithic) \n Generates C4, UML, Flow, or Component diagrams at multiple abstraction levels showing subsystems, dependencies, and data flow \n Documents core components, layers

Comprehensive security auditing workflow for web applications, APIs, and infrastructure. This bundle orchestrates skills for penetration testing, vulnerability assessment, security scanning, and remediation.

High-quality neural text-to-speech audio generation with Microsoft Edge voices. \n \n Supports 30+ voices across English, French, and Chinese with adjustable speed, pitch, and volume \n Generates MP3 audio files and optional WebVTT subtitles from plain text input \n Triggered by \"tts\" keyword or when audio output is needed for accessibility, multitasking, or specific voice requirements \n Command-line interface via uvx edge-tts with voice selection, rate/pitch/volume modulation, and subtitle o

Spawn autonomous subagents to offload context-heavy work and preserve parent token budget. \n \n Subagents burn their own tokens and return only final results, ideal for deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, and long-running operations \n Choose between mini model (gpt-5.1-codex-mini) for pure search tasks or inherit parent model for multi-step analysis, refactoring, and generation work \n Supports up to 5 parallel subagents via background shell exec

Systematic codebase search and pattern discovery for locating implementations, understanding architecture, and answering location-based code questions. \n \n Supports three thoroughness levels (quick, medium, very thorough) ranging from 30 seconds to 10 minutes, scaling from broad file structure overview to exhaustive dependency tracing \n Uses multiple search strategies including grep/rg for text patterns, ast-grep for code structure, LSP tools for symbol references, and git history for context

Iterative evaluation and refinement patterns for improving AI agent outputs through self-critique loops. \n \n Provides three core patterns: basic reflection (self-critique loops), evaluator-optimizer (separated generation and evaluation), and code-specific test-driven refinement \n Supports multiple evaluation strategies including outcome-based assessment, LLM-as-judge comparison, and rubric-based scoring with weighted dimensions \n Includes practical Python implementations with structured JSON

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.

Production-grade code standards emphasizing correctness, crash-safety, and Rust idioms over convenience. \n \n Prioritizes data integrity: crash on invalid state rather than corrupt data; assert invariants aggressively; handle all errors explicitly \n Rust-specific patterns: make illegal states unrepresentable, use exhaustive pattern matching and enums over strings, minimize allocations \n Comments document why , not what ; avoid temporal markers, AI conversation references, and code-repeating e

Execute comprehensive network traffic analysis using Wireshark to capture, filter, and examine network packets for security investigations, performance optimization, and troubleshooting. This skill enables systematic analysis of network protocols, detection of anomalies, and reconstruction of network conversations from PCAP files.