Idiomatic Go patterns, best practices, and conventions for building robust, readable, and maintainable Go code. Use when writing, reviewing, or refactoring Go (APIs, packages, errors, interfaces, concurrency, code style), choosing a dispatch or plugin mechanism, or building low-level/offensive Go with unsafe, cgo…
Go performance workflow: benchmark and profile (pprof/trace), identify hotspots, reduce allocations/GC and contention, and verify improvements with repeatable measurement. Use only after you have evidence the Go code is the bottleneck.
Go testing patterns for unit tests, table-driven tests, subtests, test helpers, mocking/fakes, benchmarks, fuzzing, and coverage. Use when writing or reviewing Go tests to improve correctness, stability, and maintainability.
Design and implement local Model Context Protocol (MCP) servers, mainly in Python, using the official SDK and host-integration patterns. Use when building or refactoring MCP servers, choosing between tools/resources/prompts, wiring stdio or local HTTP transports, handling lifecycle and capability negotiation…
Async Python patterns for building non-blocking I/O with asyncio and async/await: task orchestration, cancellation, timeouts, backpressure, rate limiting, and safe sync/async boundaries. Use when implementing concurrent network/DB workflows or async services.
Pythonic patterns and best practices for writing readable, robust Python: typing, error handling, data modeling, iteration, resource management, project layout, and tooling. Use when writing or reviewing Python code and APIs.
Binary reverse engineering with Python: analyze, parse, and disassemble ELF/PE executables using pwntools, capstone, Frida, and custom parsing tools. Use when understanding malware, debugging binary failures, analyzing section structure, extracting strings/entropy, or instrumenting runtime behavior.
Python testing patterns with pytest: TDD loop, fixtures, parametrization, mocking, test organization, async testing, coverage, and CI hygiene. Use when writing or reviewing Python tests to improve correctness and reduce flakiness.
Idiomatic Rust patterns and best practices for readable, safe, maintainable Rust: ownership, borrowing, API design, enums/traits, error handling, iterators, concurrency, dynamic dispatch/plugins, and low-level/offensive patterns. Use when writing or reviewing .rs code, refactoring crates, porting non-idiomatic code…
Rust performance workflow: benchmark and profile first, identify hotspots, reduce allocations and contention, improve data layout, tune release profiles, and verify gains with repeatable evidence. Use only after you have a real Rust performance symptom, regression, or hotspot in .rs code.
Rust testing patterns for unit, integration, async, doc, property, snapshot, fuzz, and benchmark-adjacent tests. Use when writing or reviewing tests for .rs code, reducing flakiness, designing fixtures/fakes, improving CI confidence, or hardening unsafe, FFI, and parser-heavy code with Miri and sanitizers.
Design and evolve high-quality software systems from concept through implementation: clarify outcomes and constraints, choose the simplest fitting architecture, define boundaries and contracts, address data, security, reliability, observability, testing, and delivery, then simplify and verify the result. Use when…
Root-cause-first debugging workflow for software, exploit tooling, fuzzing harnesses, reverse-engineering helpers, C2/client code, flaky tests, crashes, races, and environment-specific failures. Use when a failure is not immediately obvious or when repeated quick fixes risk hiding the real defect.
Use when implementing persistent code, bug fixes, refactors, scripts, exploit tooling, harnesses, or skill utilities before writing implementation code. Applies when tests, reproducers, assertions, or verification can be written first; treat disposable spikes separately and convert them to tested code before claiming…
Cross-language testing reliability skill for flaky tests, bad mocks, sleep-based timing, test-only production hooks, brittle fixtures, incomplete fakes, and weak assertions. Use alongside language-specific testing skills when tests pass locally but fail in CI, hide real defects, or create false confidence.
Build, review, debug, and scaffold professional Arduino projects across classic AVR boards (Uno, Nano, Mega), Renesas-based R4 boards (Uno R4 Minima, Uno R4 WiFi, Nano R4), ESP32-based Arduino boards, and other common Arduino-family targets. Use when asked for sketches, .ino files, Arduino IDE 2, Arduino CLI…
Select, compare, and integrate sensors for Arduino, ESP32, robotics, model-making, and home automation with focus on signal quality, false positives, debounce, and practical wiring. Use when asked which sensor to choose, how to detect an event reliably, how to map signals into code, or how to design sensor-driven…
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