Skill UI

Automates the creation of deprecation notices for technical documentation, guiding users through patterns, best practices, and validation steps when writing API docs, user guides, or architecture notes.

This comprehensive guide teaches the best practices for writing effective, blameless postmortem reports following technical incidents. It provides a structured approach to documenting timelines, conducting root cause analysis (RCA), identifying systemic failures, and creating actionable follow-up items. Mastering this skill is crucial for fostering organizational learning and preventing future service disruptions.

Defines a Markdown/Mermaid-first documentation skill for scientific and technical writing, providing diagram standards, 24 reference types, and templates so every report, README, or analysis stays text-first, versionable, and visualization-ready.

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

A comprehensive guide and skill for generating and auditing high-quality technical documentation for Go projects. It covers writing accurate godoc comments, READMEs, CONTRIBUTING, and CHANGELOGs for both libraries and CLIs, adhering to professional best practices. Supports multi-agent orchestration for large-scale codebases.

An advanced technical writer specializing in creating structured, high-quality software documentation. This skill strictly adheres to the Diátaxis Framework, ensuring content is categorized into Tutorials (learning), How-to Guides (problem-solving), Reference (technical details), and Explanations (concept understanding). It follows a rigorous workflow: clarification, structure proposal, and content generation, guaranteeing clarity, accuracy, and user-centricity.

A professional methodology for writing penetration test and red team reports, covering executive summaries, technical findings, CVSS scoring, evidence handling, remediation tracking, retest notes, and delivery formats for clients and internal teams.

A comprehensive template designed to structure complex technical specifications, ensuring a complete record of engineering decisions. It guides users to document problem statements, proposed solutions, system architecture, data models, API designs, security considerations, and rollout plans, bridging product requirements and technical implementation.

This skill guides users through Phases 2 (Pseudocode Design) and 3 (Architecture Design) of the SPARC methodology. It translates high-level specifications into detailed technical blueprints, covering algorithm pseudocode, module boundaries, API contracts (using DDD patterns), and infrastructure planning, ensuring a robust plan before writing production code.

A comprehensive guide for authors writing the Related Work section for high-impact journals (e.g., Acta Electronica Sinica). It teaches how to structure the review critically, moving beyond mere chronology to organize by technical path, defining scope, articulating clear technical deficiencies, and precisely positioning the paper's novelty against state-of-the-art methods.

This guide provides comprehensive rules for authors revising technical papers for top AI/Statistics conferences like AISTATS. It emphasizes achieving statistical rigor, explicitly stating assumptions, pairing every claim with proof or evidence, ensuring conciseness (e.g., adhering to an 8-page limit), and maintaining double-blind academic style. Perfect for PhD students or researchers preparing manuscripts for highly technical review.

This guide provides comprehensive strategies for drafting and revising highly technical academic papers for top-tier conferences like ASPLOS. It focuses on structuring the core argument around cross-layer insights, ensuring claims are quantifiable and bounded, and optimizing the entire manuscript to survive rapid peer review and strict page limitations.