Browse skills

Show recent entries from the LLM Wiki log (wiki/log.md). Uses the standardized

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>"

Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.

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.

Search for social media accounts by username across 400+ platforms using Sherlock. Use when: OSINT investigations, finding online presence by username, digital footprint analysis, brand monitoring across social platforms.

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.

Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.

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.

Produce an implementation plan at .turbo/plans/<slug>.md. Use when the user asks to "draft a plan", "draft the plan", "write an implementation plan", "plan this change", "create an implementation plan", "fill in the shell", "expand the shell", or needs a first-draft plan file before refinement.