diff --git a/examples/83_TxtAI_got_skills.ipynb b/examples/83_TxtAI_got_skills.ipynb
new file mode 100644
index 00000000..276f46ef
--- /dev/null
+++ b/examples/83_TxtAI_got_skills.ipynb
@@ -0,0 +1,410 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8d8cb3ad",
+   "metadata": {},
+   "source": [
+    "# TxtAI got skills\n",
+    "\n",
+    "This example will demonstrate how to use `txtai` agents with [`skill.md`](https://agentskills.io/specification) files.\n",
+    "\n",
+    "We'll setup a `skill.md` file with details on how to use TxtAI and run a series of agent requests.\n",
+    "\n",
+    "Let's get started!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00a5a0ea",
+   "metadata": {},
+   "source": [
+    "# Install dependencies\n",
+    "\n",
+    "Install `txtai` and all dependencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ff7aa19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install git+https://github.com/neuml/txtai#egg=txtai[agent]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98bcd5c3",
+   "metadata": {},
+   "source": [
+    "# Define a `skill.md` file\n",
+    "\n",
+    "Next, we'll create our `skill.md` file. This file has examples on how to build embeddings databases, how to use re-ranker pipelines, RAG pipelines and more.\n",
+    "\n",
+    "The upside of a `skill.md` file vs an `agents.md` file is that it can be dynamically added to the agent context. The description helps the agent decide if the `skill` is necessary given the request. Think of it like a dynamic knowledge base that's easy to modify."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49179643",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile skill.md\n",
+    "---\n",
+    "name: txtai\n",
+    "description: Examples on how to build txtai embeddings databases, txtai RAG pipelines, txtai reranker pipelines and txtai translation pipelines\n",
+    "---\n",
+    "\n",
+    "# Build an embeddings database\n",
+    "\n",
+    "```python\n",
+    "from txtai import Embeddings\n",
+    "\n",
+    "# Create embeddings model, backed by sentence-transformers & transformers\n",
+    "embeddings = Embeddings(path=\"sentence-transformers/nli-mpnet-base-v2\")\n",
+    "\n",
+    "data = [\n",
+    "  \"US tops 5 million confirmed virus cases\",\n",
+    "  \"Canada's last fully intact ice shelf has suddenly collapsed, \" +\n",
+    "  \"forming a Manhattan-sized iceberg\",\n",
+    "  \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\",\n",
+    "  \"The National Park Service warns against sacrificing slower friends \" +\n",
+    "  \"in a bear attack\",\n",
+    "  \"Maine man wins $1M from $25 lottery ticket\",\n",
+    "  \"Make huge profits without work, earn up to $100,000 a day\"\n",
+    "]\n",
+    "\n",
+    "# Index the list of text\n",
+    "embeddings.index(data)\n",
+    "```\n",
+    "\n",
+    "# Search an embeddings database\n",
+    "\n",
+    "```python\n",
+    "embeddings.search(\"Search query\")\n",
+    "```\n",
+    "\n",
+    "# Build a RAG pipeline\n",
+    "\n",
+    "```python\n",
+    "from txtai import Embeddings, RAG\n",
+    "\n",
+    "# Input data\n",
+    "data = [\n",
+    "  \"US tops 5 million confirmed virus cases\",\n",
+    "  \"Canada's last fully intact ice shelf has suddenly collapsed, \" +\n",
+    "  \"forming a Manhattan-sized iceberg\",\n",
+    "  \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\",\n",
+    "  \"The National Park Service warns against sacrificing slower friends \" +\n",
+    "  \"in a bear attack\",\n",
+    "  \"Maine man wins $1M from $25 lottery ticket\",\n",
+    "  \"Make huge profits without work, earn up to $100,000 a day\"\n",
+    "]\n",
+    "\n",
+    "# Build embeddings index\n",
+    "embeddings = Embeddings(content=True)\n",
+    "embeddings.index(data)\n",
+    "\n",
+    "# Create the RAG pipeline\n",
+    "rag = RAG(embeddings, \"Qwen/Qwen3-0.6B\", template=\"\"\"\n",
+    "  Answer the following question using the provided context.\n",
+    "\n",
+    "  Question:\n",
+    "  {question}\n",
+    "\n",
+    "  Context:\n",
+    "  {context}\n",
+    "\"\"\")\n",
+    "\n",
+    "# Run RAG pipeline\n",
+    "rag(\"What was won?\")\n",
+    "```\n",
+    "\n",
+    "# Translate text from English into French\n",
+    "\n",
+    "```python\n",
+    "from txtai.pipeline import Translation\n",
+    "\n",
+    "# Create and run pipeline\n",
+    "translate = Translation()\n",
+    "translate(\"This is a test translation\", \"fr\")\n",
+    "```\n",
+    "\n",
+    "# Re-ranker pipeline\n",
+    "\n",
+    "```python\n",
+    "from txtai import Embeddings\n",
+    "from txtai.pipeline import Reranker, Similarity\n",
+    "\n",
+    "# Embeddings instance\n",
+    "embeddings = Embeddings()\n",
+    "embeddings.load(provider=\"huggingface-hub\", container=\"neuml/txtai-wikipedia\")\n",
+    "\n",
+    "# Similarity instance\n",
+    "similarity = Similarity(path=\"colbert-ir/colbertv2.0\", lateencode=True)\n",
+    "\n",
+    "# Reranking pipeline\n",
+    "reranker = Reranker(embeddings, similarity)\n",
+    "reranker(\"Tell me about AI\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed26969",
+   "metadata": {},
+   "source": [
+    "# Query for TxtAI questions\n",
+    "\n",
+    "Now, let's try this out and see if the LLM is smart enough to use the defined skill vs. going out on the web.\n",
+    "\n",
+    "Let's setup the scaffolding code to create and run an agent. We'll use a [Qwen3 4B non-thinking LLM](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) as the agent's model. We'll add the `websearch` and `webview` tools to the agent along with the `skill.md` file previously created.\n",
+    "\n",
+    "Additionally, we'll add a sliding window of the last 2 responses as \"agent memory\". This will help create a rolling dialogue. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6a1aaf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from txtai import Agent\n",
+    "from IPython.display import display, Markdown\n",
+    "\n",
+    "def run(query, reset=False):\n",
+    "    answer = agent(query, maxlength=50000, reset=reset)\n",
+    "    display(Markdown(answer))\n",
+    "\n",
+    "agent = Agent(\n",
+    "    model=\"Qwen/Qwen3-4B-Instruct-2507\",\n",
+    "    tools=[\"websearch\", \"webview\", \"skill.md\"],\n",
+    "    memory=2,\n",
+    "    verbosity_level=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19302f54",
+   "metadata": {},
+   "source": [
+    "First, we'll ask how to build a TxtAI embeddings database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "121d0b8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "To create a txtai embeddings program that indexes data, use the following Python code:\n",
+       "\n",
+       "```python\n",
+       "from txtai import Embeddings\n",
+       "\n",
+       "# Create embeddings model using sentence-transformers\n",
+       "embeddings = Embeddings(path=\"sentence-transformers/nli-mpnet-base-v2\")\n",
+       "\n",
+       "# Sample data to index\n",
+       "data = [\n",
+       "  \"US tops 5 million confirmed virus cases\",\n",
+       "  \"Canada's last fully intact ice shelf has suddenly collapsed, \" +\n",
+       "  \"forming a Manhattan-sized iceberg\",\n",
+       "  \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\",\n",
+       "  \"The National Park Service warns against sacrificing slower friends \" +\n",
+       "  \"in a bear attack\",\n",
+       "  \"Maine man wins $1M from $25 lottery ticket\",\n",
+       "  \"Make huge profits without work, earn up to $100,000 a day\"\n",
+       "]\n",
+       "\n",
+       "# Index the data\n",
+       "embeddings.index(data)\n",
+       "```\n",
+       "\n",
+       "This program initializes an embeddings database using the `sentence-transformers/nli-mpnet-base-v2` model and indexes a list of text data. The indexed data can later be searched or used in other applications like retrieval, RAG, or translation."
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run(\"Write a txtai embeddings program that indexes data\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f723e07f",
+   "metadata": {},
+   "source": [
+    "The Agent pulled the correct section from the `skill.md` file. Now, let's look for an example on how to use a re-ranker pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "221a590c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "```python\n",
+       "from txtai import Embeddings\n",
+       "from txtai.pipeline import Reranker, Similarity\n",
+       "\n",
+       "# Embeddings instance\n",
+       "embeddings = Embeddings()\n",
+       "embeddings.load(provider=\"huggingface-hub\", container=\"neuml/txtai-wikipedia\")\n",
+       "\n",
+       "# Similarity instance\n",
+       "similarity = Similarity(path=\"colbert-ir/colbertv2.0\", lateencode=True)\n",
+       "\n",
+       "# Reranking pipeline\n",
+       "reranker = Reranker(embeddings, similarity)\n",
+       "reranker(\"Tell me about AI\")\n",
+       "```"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run(\"Write a txtai re-ranker pipeline\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4af3a6b6",
+   "metadata": {},
+   "source": [
+    "Great! This also works. But remember we have access to an LLM here. It doesn't have to blindly just pull the text. Let's ask it to modify the last example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c9f1eed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "```python\n",
+       "from txtai import Embeddings\n",
+       "from txtai.pipeline import Reranker, Similarity\n",
+       "\n",
+       "# Embeddings instance\n",
+       "embeddings = Embeddings()\n",
+       "embeddings.load(provider=\"huggingface-hub\", container=\"neuml/txtai-wikipedia\")\n",
+       "\n",
+       "# Similarity instance with a different reranker model and lateencode disabled\n",
+       "similarity = Similarity(path=\"BAAI/bge-reranker-base\", lateencode=False)\n",
+       "\n",
+       "# Reranking pipeline\n",
+       "reranker = Reranker(embeddings, similarity)\n",
+       "reranker(\"Tell me about AI\")\n",
+       "```"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run(\"Update the similarity path to use another reranker model. Disable lateencode.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a3bb21e",
+   "metadata": {},
+   "source": [
+    "Notice it just edited the code with a different reranker model and even added a comment to note this change.\n",
+    "\n",
+    "We can also clear the rolling dialogue and start fresh. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ad53f7a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "```python\n",
+       "from txtai.pipeline import Translation\n",
+       "\n",
+       "# Create and run pipeline for English to Spanish translation\n",
+       "translate = Translation()\n",
+       "translated_text = translate(\"This is a test translation\", \"es\")\n",
+       "print(translated_text)\n",
+       "```"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run(\"Write a txtai program that translate text from English to Spanish\", reset=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "007daee0",
+   "metadata": {},
+   "source": [
+    "# Wrapping up\n",
+    "\n",
+    "This example shows how to add a `skill.md` file to `txtai` agents. Go give it a try!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "local",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}