Skill UI

This skill guides the creation of technical documentation (such as RFCs, READMEs, PR descriptions, and architecture guides) that minimizes the mental effort required for the reader. It emphasizes structural patterns like 'Lead with the answer,' 'Chunking,' and 'Progressive disclosure' to ensure content is scannable, actionable, and easy to review, significantly reducing cognitive load for maintainers and contributors.

A comprehensive guide and set of rules for writing effective, warm, and actionable comments across various development platforms. It helps users structure feedback for GitHub PRs, issue trackers, or Slack messages, ensuring the tone is consistently thoughtful, direct, and focused on clear next steps, rather than simply listing observations.

Standardizes branch creation and pull request submission for the Gentle AI repo, enforcing issue-first approval checks, branch naming patterns, type and policy labels, required CI checks, and a 400-line review budget before changes reach reviewers.

This skill analyzes excessively large Pull Requests (PRs) that exceed established line budgets (e.g., 400 lines). It automatically splits these massive changes into smaller, focused, and sequential PRs using advanced chaining or stacking strategies. This process is crucial for protecting reviewer focus, ensuring reviews can be completed within a manageable time budget (e.g., ≤ 60 minutes), and maintaining high code quality during complex feature integration.

This skill provides a comprehensive guide to advanced testing patterns in Go. It covers everything from basic unit tests (like table-driven testing) to complex state management in TUI applications (Bubbletea/teatest), and deterministic testing using golden files. Use this when writing, reviewing, or improving Go test coverage to ensure reliable and focused testing.

A skill that captures Guixingren's AI technology media creation mindset, featuring Silicon Valley first-line perspectives, China-US dual-line narratives, rapid news aggregation structure, and AI community productization operations. Suitable for AI industry news writing, Silicon Valley reporting, China-US AI comparison analysis, developer tool reviews, and AI community operations, including hackathons, podcasts, and offline events.

Skill for parent orchestrators to delegate independent workstreams to ephemeral context workers via delegate_task. Covers mapping needs, parallel unit writing, high-risk changes, adversarial review, and multi-step debugging. Enforces named-reason delegation, self-contained missions, and verification of worker output.

Framework for AI media writing inspired by Synced. Focuses on paper-first reporting, quantitative data, academic plus industry parallelism, layered analysis, and productized content. Applies to deep tech reviews, model benchmarks, industry trend reports, and editorial commentary.

A rigorous, standalone tool designed for explicit, blind, and adversarial code review. It executes the code target through two parallel, independent judges (Judge A and Judge B) with strict rules and limited fix rounds. This process ensures maximum scrutiny, providing a definitive, highly corroborated verdict (APPROVED or ESCALATED) that replaces standard review methods.

This document defines the critical architectural contract for standardizing advisory transport across diverse AI code review runtimes (e.g., Claude Code, OpenCode, Codex). It mandates that all integrations adhere to a shared Go contract, governing how review results, evidence, and validation are processed. This is essential for ensuring system integrity, managing architectural transitions, and preventing the creation of isolated, proprietary review systems.

This skill outlines a highly rigorous, receipt-driven workflow for managing defects and implementing code corrections. It guides public collaboration by enforcing strict controls over change authority, ensuring comprehensive lineage tracking, and mandating evidence-based validation. Key steps include CodeGraph impact mapping, black-box testing, and adherence to strict change budgets, ensuring every deployed change is fully traceable and robust.

Plan commits and PRs as reviewable work units instead of file-type chunks. Covers splitting rules, work-unit checklists, doc/test co-location, and chained PR strategies for healthy review load.