Skill UI

This skill helps structure the foundational commercial policies governing discounting off list price. It builds three deterministic tools: a 4D discount matrix builder (considering ARR, term length, payment terms), an exception flow router with compensating commitments, and a policy linter to detect governance gaps. It is designed for Heads of Commercial and RevOps to establish defensible rules of engagement, ensuring margin adherence and preventing discount drift, rather than for approving individual deals.

This skill is designed for Heads of Procurement or BizOps leaders conducting annual category reviews. It analyzes complex enterprise spending patterns by categorizing costs using UNSPSC standards, identifying the top cost drivers (Pareto analysis), and diagnosing bottlenecks in the purchasing cycle. It generates a defensible, risk-balanced plan for supplier consolidation, ensuring no critical tier-1 category is collapsed to a single source without documented contingency plans.

A powerful tool for designing and writing deterministic, multi-agent workflow scripts in JavaScript. It allows users to orchestrate complex, repeatable, multi-step tasks using advanced patterns like fan-out, pipelines, loops, and judge-panels. Ideal for automating sophisticated business processes that require multiple specialized sub-agents and robust context management.

A meta-skill designed to generate a comprehensive daily operating brief. It intelligently combines structured data from pasted context (calendars, tasks, emails), user memory (priorities, open loops), and external factors like weather, providing a holistic and actionable plan for the day. Ideal for users needing a single, high-level summary of their commitments and priorities.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

Falco is a powerful CNCF project for real-time runtime security monitoring. This guide teaches authors how to write and deploy custom detection rules using Falco's robust YAML rule engine and modern eBPF driver. It is designed to detect critical threats such as container escapes, namespace manipulation, privileged mounts, and anomalous syscalls in Kubernetes and Docker environments, significantly hardening the overall container security posture.

This library provides a comprehensive framework for managing bounded AI workflows (loops). Users can analyze existing codebases or work histories to discover opportunities for repeating, automated agent processes. It guides the user through finding published templates, auditing and repairing weak agents, adapting parameters, or crafting entirely new, robust automation designs, ensuring clear triggers, verification, and mandatory stopping conditions.

A comprehensive presentation template designed for educational workshops and training sessions. It features a warm paper aesthetic, persistent learning objectives on the left sidebar, core content slides, interactive Multiple Choice Question (MCQ) self-assessment pages, and module wrap-ups, ensuring a structured and engaging learning experience.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

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.