Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

Use when adding, removing, or finding Meteor core packages, resolving package dependencies, choosing between authentication strategies (accounts-password, passwordless, OAuth), or navigating the monorepo package ecosystem. Covers meteor add/remove commands, package lookup by feature domain, and dependency relationships across 100+ core packages.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

Strategic guide for becoming an effective GitHub contributor. Covers opportunity discovery, project selection, high-quality PR creation, and reputation building. Use when looking to contribute to open-source projects, building GitHub presence, or learning contribution best practices.

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

Clone a dockersamples Labspace repo, extract learning objectives and module structure from labspace.yaml, and produce a Hugo guide page under content/guides/ with correct frontmatter, labspace-launch shortcode, and Docker docs style compliance. Use when asked to create a lab guide, write a Labspace page, add a Docker lab tutorial, migrate a lab to docs, or document a hands-on lab.

How to manage imas-codex services (Neo4j graph, embedding server, LLM proxy, SSH tunnels). Use when starting, stopping, checking status, or troubleshooting services.

The Idea Generator is the brainstorming specialist for Headout's PM OS. Engage after a Problem Frame is complete — or directly when a PM has a clear problem statement and wants to explore the solution space rigorously before committing to a direction. Acts as a high-fidelity thought partner: builds deep context through a multi-round interview (current state, constraints, prior attempts, platform nuances), maps the solution space into 2-4 strategic themes before generating any ideas, then produces specific ideas within each theme. Trigger for: "what are the ways we could solve this", "brainstorm approaches to X", "what should we build for this problem", "explore the solution space", "I have a problem frame and want ideas", "ideate on this", "what directions could we go", or any request to generate solution directions after a problem has been framed. Output: a structured Idea Brief.

Analyze failed or stuck proofs and propose auxiliary lemmas to help complete the proof in Isabelle/HOL or Coq. Use when encountering proof failures, stuck proof states, unprovable subgoals, or when needing to strengthen induction hypotheses. Identifies missing lemmas, suggests proof strategies, and generates helper lemmas with appropriate statements and proof sketches. Supports inductive proofs, case analysis, rewriting, and complex proof obligations.

Trains and fine-tunes ML models, builds data preprocessing and feature engineering pipelines, deploys models as REST APIs, integrates inference into production applications, and designs RAG and LLM-powered systems. Covers MLOps workflows including experiment tracking, drift detection, retraining triggers, and A/B testing. Use when the user asks about training or fine-tuning a model, building ML pipelines, model serving or inference optimization, evaluating model performance, working with frameworks like PyTorch, TensorFlow, scikit-learn, or Hugging Face, setting up vector databases, prompt engineering, or taking an ML prototype to production.

Use when reviewing PRs, simplifying over-engineered code, judging architecture, or renaming unclear concepts — inline fake helpers, delete meatless ceremony, rename lying names, merge over-split files, reject premature abstraction, say no to speculative config/modes/layers, then end with the smallest safe next change. Triggers when code is too fancy, too abstract, too clever, too many files/helpers/layers, or too well-factored but painful to change. Embody Grug brain: complexity very bad, small words, no consultant speak, no hard pivot to opposite dogma.

Enhanced git operations using lazygit, gh (GitHub CLI), and delta. Triggers on: stage changes, create PR, review PR, check issues, git diff, commit interactively, GitHub operations, rebase, stash, bisect.