Browse skills

Query the LLM Wiki — reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back as a new comparison or synthesis page. Usage /wiki-query "<question>"

Simplifies code for clarity without changing behavior. Use when refactoring for readability, when code works but is harder to maintain than it should be, or when reviewing code that has accumulated unnecessary complexity. Covers the five principles, simplification process, and language-specific guidance.

Optimizes agent context setup for maximum output quality. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when configuring rules files and context for AI-assisted development. Covers the context hierarchy, packing strategies, and confusion management.

Records architectural decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase. Covers ADRs, inline docs, API docs, READMEs, and changelogs.

Use Block's Goose — an open-source, extensible AI agent that goes beyond code suggestions to install software, execute commands, edit files, run tests, and manage infrastructure. Use when: setting up Goose, building custom extensions, adding MCP tool support, configuring multi-LLM backends, creating task-specific agents (DevOps, data pipelines, incident response), or comparing Goose to other AI coding agents.

Refines raw ideas into sharp, actionable concepts through structured divergent and convergent thinking. Use when you have a vague idea that needs sharpening, want to stress-test a plan, or need to explore variations before committing to a direction. Produces a concrete markdown one-pager with problem statement, assumptions, MVP scope, and trade-offs.

Delivers changes incrementally in thin vertical slices. Use when implementing any feature or change that touches more than one file, when you're about to write a large amount of code at once, or when a task feels too big to land in one step. Each increment leaves the system in a working, testable state.

Build multi-agent AI systems with Microsoft's Agent Framework (formerly Semantic Kernel Agents). Use when: defining AI agents, orchestrating multi-agent workflows (sequential, parallel, conditional), creating custom agents in Python or .NET, using built-in agents (ChatCompletion, OpenAI Assistant), deploying agent systems, or comparing agent orchestration patterns.

Run Vision Language Models locally on Apple Silicon Macs using MLX. Use when: installing mlx-vlm, running VLM inference (image + text → response), fine-tuning vision models on custom datasets, batch processing images with local AI, comparing local VLM to cloud APIs (GPT-4V, Claude Vision), or working with LLaVA, Phi-3-Vision, Qwen2-VL, Pixtral, Llama-3.2-Vision on Mac.

Deploy and configure Onyx, a self-hosted AI chat platform with RAG and 25+ data connectors. Use when: setting up private ChatGPT alternative, connecting AI to company documents, building enterprise AI assistant with Confluence/Slack/Drive access.

Browse, search, and self-host a community prompt library with 1000+ curated prompts. Use when: finding proven prompts for specific tasks, building a team prompt library, learning prompt patterns from community-tested examples.

Forecast time series data using Google's TimesFM foundation model with zero-shot prediction. Use when: forecasting sales or demand, predicting server metrics, financial time series analysis, anomaly detection without training custom models.

Meta-skill for discovering and invoking the right agent skill for any task. Use when starting a session, when unsure which skill applies, or when you need to understand the development lifecycle and how skills chain together. Covers skill discovery, core operating behaviors, and failure modes to avoid.

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

Behavioral protocol engineered for extreme AI agent token optimization, eliminating I/O noise via context-aware truncation and strict conciseness.

Create professional Marp presentation slides with 7 beautiful themes (default, minimal, colorful, dark, gradient, tech, business). Use when users request slide creation, presentations, or Marp documents. Supports custom themes, image layouts, and "make it look good" requests with automatic quality improvements.

Decompose a specification file into context-sized shell plans. Each shell captures the wiring invariants (Produces, Consumes, Covers) and high-level Implementation Steps without committing to file paths. Use when the user asks to "draft a prompt plan", "create a prompt plan", "break spec into prompts", "decompose spec into sessions", "plan prompts for spec", "generate prompts from spec", or "make prompts from spec".

Comprehensive guide for writing systems papers targeting OSDI, SOSP, ASPLOS, NSDI, and EuroSys. Provides paragraph-level structural blueprints, writing patterns, venue-specific checklists, reviewer guidelines, LaTeX templates, and conference deadlines. Use this skill for all systems conference paper writing.

Execute an implementation plan file produced by /draft-plan or /turboplan. Runs pre-implementation prep, loads task-specific skills by matching plan content against available skill triggers, executes the plan steps, and runs /finalize. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the plan", "implement plans/<slug>.md", "start implementing the plan", or starts a fresh session to implement a previously drafted plan.

Recall the reasoning behind a past change by locating the Claude Code transcript that produced it. Use when the user asks to "recall reasoning", "find reasoning", "look up reasoning", "recall implementation reasoning", "find the rationale", "why did I do X", "recall from transcripts", or "find the transcript for this commit".