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Found 27 Skills
Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and records evidence in AGENT-SKILLS-LEARNING.md.
Analysis of Lanhu design drafts and Axure prototypes. Directly read prototype pages, design drafts, and slice resources of Lanhu projects via lanhu MCP Server. Trigger scenarios: - Need to obtain Axure prototype pages from Lanhu for requirement analysis - Need to view Lanhu UI design drafts and design parameters - Need to extract slice resources from Lanhu design drafts - Need to collaborate via Lanhu team message board - Need to parse Lanhu invitation links Trigger words: Lanhu, lanhu, design draft, prototype, Lanhu link, design image, slice
Draws static SVG figures for lessons, READMEs and manuals in the AI Engineering from Scratch look: paper panel, blueprint accent, nine figure kinds, fixed hue meanings, eight arrow styles, and checks for legibility and markup. Trigger phrases: "draw a figure", "blueprint figure", "sequence figure", "state machine figure", "figure for this lesson", "add a diagram to the manual".
Builds an interactive isometric machine page. Each station of one invented machine stands for one step of a process. A pure, tested state model drives the motion, the readouts and the copy. The page has controls and a verification loop. Trigger phrases: "isometric explainer", "machine explainer", "explain this as a machine", "interactive isometric figure", "explorable explainer", "show how it works inside".
The shared visual and writing reference for AI Engineering from Scratch pages, figures, and manuals. Use it for light and dark colour tokens, font stacks, FIG_ label formats, and card layout. Use it for the plain technical English rules and the accessibility and reduced motion defaults. Trigger phrases: "course style", "blueprint palette", "course tokens", "FIG label", "plain technical English", "check my prose", "course fonts", "dark mode tokens"
Builds figures that move or respond to the reader, in three tiers: SMIL animations and slider widgets inside lessons, interactive mechanism figures that show the named parts of a real system with a computed verdict, and ordered dither bands in the course palette. Triggers: "animated figure", "add a slider widget", "interactive figure", "show how this system works", "make this diagram move", "dither masthead", "mechanism figure", "figure fence".
Writes a long technical manual that explains one subject at one pinned version, from its purpose to each request and response. Every listing comes from an offline capture, every claim traces to a ranked source, and an audit must report TOTAL 0. Use it for "write a manual", "write a 101 manual for <subject>", "manual.json", "add a manual section", "build a capture kit", "audit the manual", or "pin a spec version and document it".
Focused interactive tutor for the Reverse Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to read a compiled program: file formats, disassembly, control flow, lifting, evidence, proof, calling conventions, compiler idioms, linking, types, debugging, bytecode, diffing, symbolic execution, and AI-assisted analysis. Teaches one lesson per invocation and records evidence in REVERSE-ENGINEERING-LEARNING.md.
Opens a lesson screen in the browser next to the terminal while a course tutor teaches. It shows the lesson's registered figures, code traces with real values, and clickable predictions. It also builds a figure for the lesson on the fly with the course figure skills and any figure skill the host has installed: a blueprint, a mechanism, an isometric machine, or a 3D model. Clicks return to the tutor as events. Trigger phrases: "open the lesson screen", "show me this visually", "show this in 3D", "draw the architecture", "visualize this lesson", "build a figure for this lesson", "lesson screen".
GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper.
Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical content in domains like RAG, embeddings, fine-tuning, prompt engineering, LLM deployment.
Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.