Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Creates new skills, modifies and improves existing skills, and measures skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Meta-skill for creating new Claude Code skills with configurable execution modes. Supports sequential (fixed order) and autonomous (stateless) phase patterns. Use for skill scaffolding, skill creation, or building new workflows. Triggers on "create skill", "new skill", "skill generator".
Work decomposition, dependency ordering, and status tracking for software tasks. Provides hierarchical decomposition (features into stories into leaf tasks), Slice-to-Task decomposition (walking skeleton first, then acceptance tests before implementation), GWT acceptance criteria on every task, explicit dependency declarations, estimated scope, and a strict lifecycle (Open/Active/Closed with one-at-a-time active task rule). Use when breaking down features, managing work items, tracking dependencies, creating stories or epics, slicing work for incremental delivery, or deciding what to work on next. Triggers on: "decompose this feature", "create tasks", "what should I work on next", "break this into smaller pieces", "walking skeleton", "slice into tasks", "task dependencies". Works with any task tool.
Inject UX principle context into your AI coding session before generating components. Maps component types (forms, tables, dashboards, navigation, checkout, etc.) to relevant UX principles, fetches their vibeCodingPrompts, and returns an assembled context block ready to paste into Cursor, Claude Code, Windsurf, or any AI coding tool. API key optional—enriched prompts with research citations require uxuiprinciples.com API Access.
One-shot installer for the akillness/oh-my-gods agent skill bundle (80+ god-skills) — wraps the official install.sh with pinned URL and env knobs (PLATFORM, WITH_LANGCHAIN, INSTALL_MODE, SKIP_BACKUP) to copy the catalog into Claude Code, Codex CLI, Antigravity/Gemini, and OpenCode in one step.
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
Write, edit, and adapt PostHog Signals scouts: scheduled agents that scan a project and report findings. Use to change a canonical scout's scope, thresholds, schedule, or dry-run settings; add a scout for an uncovered surface; record structured output from scheduled LLM scoring; or give feedback through notes. Covers SKILL.md structure, report requirements, follow-up report checks, structured output, duplicate detection, scratchpad memory, scout notes, per-team skills-store and canonical repository edits, and private prompt trials with saved rubrics. Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>", "score/judge/measure X with a scout", "test/compare scout prompts", "generate scout rubrics", "structured output from a scout", "scout output to Slack", "leave a note for / give feedback to a scout".
Introduces the user to the ai-bro skill set, skill map, and typical workflow. Use only when explicitly invoked with /bro-on-board for a first introduction or to repeat onboarding. Do not use for general questions about the project or the work of other tools
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing context usage, or implementing conversation history management.
How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and write reports into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the reports it wrote or edited, reading the follow-up checks still measuring a report, and assessing a scout's health and performance over time (cadence, success rate, report rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/report nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".
JavaScript/TypeScript SDK for inference.sh - run AI apps, build agents, integrate with all models. Package: @inferencesh/sdk (npm install). Full TypeScript support, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, human approval. Use for: JavaScript integration, TypeScript, Node.js, React, Next.js, frontend apps. Triggers: javascript sdk, typescript sdk, npm install, node.js api, js client, react ai, next.js ai, frontend sdk, @inferencesh/sdk, typescript agent, browser sdk, js integration
Use when product or feature work starts, or a PDR-worthy decision (scope, persona, monetization) emerges — injects the PDR index (docs/adlc/memory/pdr/, legacy .adlc/memory/pdr/ fallback) as session context and suggests the decision for capture via /product-specify (never writes drafts itself); invoked from team-boot's Class Boots catalog
Declarative UI widgets from JSON for React/Next.js from ui.inference.sh. Render rich interactive UIs from structured agent responses. Capabilities: forms, buttons, cards, layouts, inputs, selects, checkboxes. Use for: agent-generated UIs, dynamic forms, data display, interactive cards. Triggers: widgets, declarative ui, json ui, widget renderer, agent widgets, dynamic ui, form widgets, card widgets, shadcn widgets, structured output ui
Use when architecture work starts or a tech-stack or pattern decision emerges — injects the project ADR index (docs/adlc/memory/adr/, legacy .adlc/memory/adr/ fallback) as session context and suggests the decision for capture via /architect-specify (never writes drafts itself); invoked from team-boot's Class Boots catalog
Use the ClawdHub CLI to search, install, update, and publish agent skills from clawdhub.com. Use when you need to fetch new skills on the fly, sync installed skills to latest or a specific version, or publish new/updated skill folders with the npm-installed clawdhub CLI.
Continues interrupted Claude Code work after local history is read and reconciled — reconstructs the original outcome, unfulfilled requests, and the next action. Use when given a Claude Code Session ID, asked to pick up prior Claude work, told a run was interrupted or compacted, or asked to take over without `claude --resume`. Not for Codex sessions (use continue-codex-work).
Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces. Use when someone wants to understand what's going wrong with an AI feature, find and categorize failure modes, triage errors, or investigate quality issues (wrong answers, ignored instructions, hallucinations, tool misuse) — "what's failing in my agent", "surface error patterns", "why are the responses bad", "find the common failure modes", "what should I fix next". Covers scoping to one use case, finding failing traces by whichever signal fits the context (code errors, metric outliers, trace-type slices, manual review, existing-eval spikes, clustering), and reading them into a ranked failure taxonomy.
Structured decision modifiers (/think, /verify, /adversarial, /edge, /confidence, /assumptions, etc.) to stress-test conclusions, evidence, assumptions, alternatives, and edge cases. Use when validating an important design, architecture decision, or ambiguous plan before committing.
Meta-prompting standard library for generating, optimizing, and composing prompts programmatically via Standards, Handlebars Templates, and Tools; output is always a prompt to use elsewhere, not final content. USE WHEN meta-prompting, template generation, prompt optimization, prompt engineering, write a prompt, create system prompt, Handlebars template, eval prompt, judge prompt. NOT FOR generating final content (use the appropriate domain skill).
Drive iOS Simulator, Android emulator/device, and physical iPhone/iPad screens for AI agents. Use when asked to automate a simulator or emulator, drive a real iOS device, tap/swipe/type on a device, describe UI, take a screenshot, or interact with a mobile app.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.