engineering▌

23 production-ready engineering skills organized into core engineering, AI/ML/Data, and specialized tools.

Protocol Reverse Engineering \n Comprehensive techniques for capturing, analyzing, and documenting network protocols for security research, interoperability, and debugging. \n Use this skill when \n \n Working on protocol reverse engineering tasks or workflows \n Needing guidance, best practices, or checklists for protocol reverse engineering \n \n Do not use this skill when \n \n The task is unrelated to protocol reverse engineering \n You need a different domain or tool outside this scope \n \

Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Bridge design and engineering teams by clarifying roles, organizational placement, and hiring strategy. \n \n Defines design engineering as merging UX design with engineering to accelerate prototyping and production shipping, with scope varying from validation prototypes to production-ready code \n Emphasizes combining design sensibility with shipping ability rather than seeking unicorn-level expertise in both disciplines \n Guides organizational placement decisions, handoff friction diagnosis,

Comprehensive PostgreSQL database engineering covering indexing, query optimization, replication, and production management. \n \n Covers 6 index types (B-tree, Hash, GiST, GIN, BRIN, SP-GiST) with decision matrices for choosing the right index for specific query patterns \n Includes streaming and logical replication setup, failover procedures, and high-availability configuration with synchronous/asynchronous modes \n Provides partitioning strategies (range, list, hash) with automation examples

Diagnose whether your AI workflows are context stuffing or context engineering, then implement structured practices to improve output quality. \n \n Distinguishes between context stuffing (volume-based) and context engineering (structure-based), with five diagnostic questions to identify Context Hoarding Disorder \n Guides two-layer memory architecture: short-term conversational memory plus long-term persistent memory via vector databases for semantic retrieval \n Implements the Research→Plan→Re

Engineering operating model for teams shipping code with AI-assisted generation. \n \n Emphasizes planning quality, evaluation coverage, and behavior-focused code review over traditional syntax checking and typing speed \n Requires agent-friendly architectures with explicit boundaries, stable contracts, typed interfaces, and deterministic tests to minimize implicit behavior \n Shifts review priorities to behavior regressions, security assumptions, data integrity, failure handling, and rollout sa

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.

A diagnostic skill that identifies code, architecture, and technical decisions built to impress rather than to ship. Vanity engineering is entropy disguised as craftsmanship — it increases complexity without proportional capability gain, and it compounds maintenance cost while delivering zero additional user value.