Skill UI

This skill analyzes your opencode prompt history to identify repeating multi-step workflows, recurring unmet needs, and candidates for new reusable skills. By scoring patterns based on frequency, complexity, and tool usage, it helps users save time and avoid repeating complex manual tasks.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

Max is a specialized agent designed for safe, non-behavioral code improvement. It systematically refactors existing, working code by optimizing time complexity (Big-O), abstracting duplicated logic, removing dead code, and enhancing readability. It operates under a strict constraint: no change may alter the original functionality, requiring all refactored code to pass existing test suites flawlessly.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.

A comprehensive guide on designing, building, and optimizing interactive developer playgrounds. Learn principles covering instant gratification, progressive complexity, and zero friction to maximize developer engagement, demonstrate core value, and convert curious visitors into active users.

Use this skill to structure, outline, and write comprehensive, step-by-step technical tutorials, quickstarts, and code walkthroughs. It guides the creation process from setting prerequisites and defining audience context to ensuring progressive complexity and robust troubleshooting sections, resulting in high-quality, actionable developer content.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Pattern for controlling LLM API costs while preserving quality. Combines complexity-based model routing, immutable budget tracking, retry logic limited to transient failures, and prompt caching. Use when building LLM API apps, processing batches with varying difficulty, or enforcing budget guardrails in production.

This shared engine defines a comprehensive, gated pipeline for software development (Research-Plan-TDD-Review-Commit). It acts as the central coordinator, delegating tasks to specialized agents (planner, code-reviewer) and managing the entire lifecycle of feature development, bug fixing, or refactoring. It includes a size classifier to adapt the workflow complexity and enforces mandatory human gates at planning and committing stages, ensuring structured and high-quality development.

This skill intercepts the AI response flow to allow users to explicitly control the desired depth, length, and token usage of the generated answer before the model begins writing. By analyzing the prompt's complexity and the user's intent (e.g., 'short version', 'detailed answer'), it estimates the appropriate token range and prompts the user to select a granularity level (25%, 50%, 75%, or 100%).

A comprehensive strategy guide for authors preparing manuscripts for the ACM Symposium on Theory of Computing (STOC). It helps researchers determine the ideal fit, required evidence bar, and narrative framing for major theoretical contributions in algorithms, complexity, cryptography, and theoretical computer science.

An essential orchestrator for authors submitting manuscripts to Agricultural Systems. This tool guides users through the entire academic writing lifecycle, helping them determine the appropriate article type (e.g., Review, Perspective) and ensuring the core research question addresses true systems complexity, interactions, and trade-offs, rather than single-factor trials. It maps the manuscript stage to the necessary sub-skills.