Browse skills

Discover, list, create, edit, toggle, copy, move, and delete AI agent skills across 11 tools (Cursor, Claude, Agents, Windsurf, Copilot, Codex, Cline, Aider, Continue, Roo Code, Augment)

Bootstrap a fresh LLM Wiki vault with the three-layer structure, schema files, and starter templates. Usage /wiki-init <path> --topic "<topic>" [--tool all|claude-code|codex|cursor|antigravity]

Run a health check on the LLM Wiki vault — mechanical checks (orphans, broken links, stale pages, missing frontmatter, log gap, duplicates) plus semantic checks (contradictions, cross-reference gaps, concepts missing their own page). Outputs a markdown report with suggested actions. Usage /wiki-lint [--stale-days N] [--log-gap-days N]

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.

Dispatched sub-agent that answers queries against an LLM Wiki vault. Reads index.md first, drills into 3-10 relevant pages across categories, synthesizes an answer with inline [[wikilink]] citations, and offers to file the answer back into the wiki as a new comparison or synthesis page. Spawn when the user asks a substantive question the wiki might answer, says "what does the wiki say about X", "compare A and B across my sources", or wants to explore a topic.

Dispatched sub-agent that runs a periodic health check on an LLM Wiki vault. Runs mechanical checks via scripts (orphans, broken links, stale pages, missing frontmatter, duplicate titles, log gaps), does semantic checks (contradictions, stale claims, cross-reference gaps, concepts missing their own page), and produces a markdown report with suggested actions. Spawn weekly, after batch ingests, or when the user says "check the wiki" / "lint my wiki" / "audit the vault".

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>"

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.