Skill UI

This skill guides the design and rigorous review of goal-oriented agent feedback loops. It helps ensure that complex autonomous multi-step agents are robust, preventing failures such as infinite looping, goal-gaming, or running to completion with incorrect assumptions. It covers defining machine-decidable goals, setting boundaries, and selecting the correct loop type (servo, regulator).

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

This tool is designed to validate the syntax, completeness, and correctness of network configuration files. It helps ensure that network devices are configured according to established standards and best practices, preventing deployment errors and ensuring reliable network operation. Ideal for network engineers and DevOps teams.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

Design and implement a unified operational workflow for managing notifications sourced from disparate tools like GitHub, Linear, local hooks, and chat platforms. This skill addresses notification fragmentation, alert noise, and redundancy by establishing clear severity levels, ownership, routing channels, and next operational actions into a single, cohesive pipeline.

This skill provides comprehensive strategies for implementing sustainable business growth loops. It covers Product-Led Growth (PLG) optimization, designing viral loops, optimizing referral programs, and improving user retention mechanics. Use this when planning product launches, scaling user bases, or building complex acquisition funnels.

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

Provides rigorous guidance and structure for statistical analysis in Field Crops Research (FCR). Focuses on implementing linear mixed models that respect complex experimental designs (e.g., split-plot, block, environment). Essential for proper G×E analysis, robust means separation, and accurate evaluation of crop models, ensuring results are methodologically sound and actionable.

Provides comprehensive guidelines for field crops researchers submitting manuscripts. The policy mandates reporting deep agronomic context (soil, weather, management, design) and requires authors to prepare a detailed data-availability statement. It ensures the reproducibility and structural completeness of research findings, while also covering guidelines for co-submission and AI use declaration.

This guide provides a framework for designing and auditing the experimental rigor of academic papers, particularly in systems and programming languages. It emphasizes the crucial link between a claim type (e.g., 'Faster') and the required evidence type (e.g., benchmarks, user studies, formal proofs). Learn how to establish robust baselines, define clear workload rules, size experiments for conference cycles, and ensure statistical compliance for maximum impact.