Skill UI

This skill is designed to mitigate false-positive safety triggers from strict AI classifiers (e.g., cyber, bio/chem, reasoning). It operates by making minimal, surgical edits to sensitive phrases, abstracting away specific domains or actions while preserving the original benign intent and structure. It ensures the prompt remains policy-compliant without losing its core purpose, making it ideal for academic or defensive inquiries.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

This skill guides the safe, systematic, and at-scale refactoring of existing Go codebases. It ensures that structural changes maintain observable behavior by following a rigorous process: mapping blast radius, strengthening safety nets (tests), applying small tool-driven transforms, and committing changes atomically via staged PRs. Ideal for cleaning up large functions, resolving code smells, renaming packages, or tackling complex architectural improvements in Go.

This skill guides the creation of comprehensive, structured strategic documents (SSOT, life vision, principles) using a strict, iterative interview-style methodology. Instead of generating content, the AI asks one focused question at a time. The user provides the answer, and the AI patches the document incrementally, ensuring the user remains the primary author. It is ideal for deep reflection, strategic planning, and building authoritative knowledge bases.

Drafts high-engagement, long-form LinkedIn posts using 16 reverse-engineered hook formulas. Instead of writing randomly, the skill focuses on the desired outcome (comments, reposts, likes, or saves). It integrates the user's voice, adheres to modern formatting rules, and includes an AI-tell scrub pass to ensure the content reads authentically and humanly.

This skill provides reliable methods for opening and viewing Markdown files within cmux panes. It specifically addresses a known bug where attempting to move an existing Markdown viewer surface using `move-surface` results in a blank, unreadable output. Use this when you need a stable, correctly rendered Markdown view in a designated cmux pane, avoiding the rendering failure bug.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

This skill automates the commit and push process for local skill changes. It should be used after a skill has been fully validated and updated, ensuring the canonical copy of the skill is published to the configured remote GitHub repository. This is essential for sharing or finalizing skill updates.

This skill ensures a comprehensive understanding of a project's technical history by reading every Architecture Decision Record (ADR) in the designated folder. It is crucial to use this when summarizing architectural context, making major changes, or evaluating project decisions, guaranteeing nothing is missed.

This skill converts ambiguous or general research needs into a single, highly precise, self-contained prompt. It guides the user to define the project context, the core question, and specific sub-questions, ensuring the resulting prompt is rigorous enough for deep, multi-source AI research, minimizing ambiguity and guesswork.

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This skill guides users through complex, multi-step processes such as installation, configuration, or initial setup. It breaks down the process into small, atomic actions, presenting only the current required step and a limited, manageable checklist of remaining tasks. Ideal for onboarding new users or executing lengthy technical setups.