Skill UI

Provides deep expertise in analyzing DWARF v3-v5 debug files, verifying their integrity through llvm-dwarfdump, and helping parse or write code that interacts with DWARF data for more reliable debugging toolchains.

MCPB is a specialized local server bundle that packages the entire runtime environment (Node.js or Python) alongside the server code. It allows developers to distribute and run complex MCP services directly on a user's machine without requiring the user to pre-install specific runtimes or toolchains. Use this when the server must interact with the local filesystem, desktop applications, or OS-level APIs.

Converts markdown documents to HTML via marked.js-style workflows, pandoc/gomarkdown knowledge, and templating systems like Jekyll or Hugo for both single-file and batch pipelines, enabling static site previews, documentation, and CLI/Node toolchains.

This skill generates standardized, production-ready `mise.toml` configurations. It helps developers standardize runtime versions across mixed-language projects (e.g., Node.js, Python, Go, etc.) and CI/CD pipelines, eliminating the need for multiple legacy version managers like asdf or nvm. Ideal for ensuring reproducible, consistent development environments.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

A comprehensive guideline for establishing and documenting the reproducibility of complex sensor-systems artifacts. It covers necessary components such as firmware sources, detailed hardware Bills of Materials (BOMs) and board files, raw sensor traces, ground truth data, and calibrated measurements. Use this guide to ensure that scientific claims can be verified by a competent reader, detailing the exact dependencies on physical equipment and software toolchains.

A comprehensive guide for microarchitecture researchers on achieving rigorous scientific reproducibility. It mandates tracking every critical artifact—including simulator commits, workload traces, power/area models, and RTL toolchains—and compiling them into a single run manifest, ensuring any reported result can be verified by a third party.

A comprehensive guide for researchers to ensure network security findings are reproducible and verifiable. It covers techniques for freezing volatile environments (targets, networks, toolchains), anonymizing sensitive data traces, maintaining a detailed claims ledger, and ethically stating limitations.

This guide addresses the stringent requirements for empirical evaluation in compiler and runtime systems, aligning with SIGPLAN's guidelines. It covers critical aspects such as proper warmup discipline, reporting variance and confidence intervals, selecting principled benchmarks, pinning toolchains, and ensuring cross-platform validity. It provides a protocol for generating verifiable measurement logs for academic publications.

This guide details the rigorous process for packaging and evaluating mechanized proofs (e.g., in Coq, Isabelle, Lean, Agda) submitted for academic conferences. It outlines criteria for achieving Functional, Reusable, and Available badges, focusing heavily on completing the formal development, auditing axioms, and ensuring perfect reproducibility by pinning toolchains and mapping theorems to source files.

This guide outlines essential strategies for ensuring that complex physical or scientific measurements are fully reproducible by external parties, long after the original testbed is decommissioned. It mandates capturing detailed provenance data, including energy measurement methods, hardware specifications, firmware toolchains, environmental conditions, and ground-truth protocols, thus transforming raw data into verifiable, sharable scientific artifacts.