diff --git a/docs/guided_tour.md b/docs/guided_tour.md
index 42c3c24..87fb53e 100644
--- a/docs/guided_tour.md
+++ b/docs/guided_tour.md
@@ -1,12 +1,12 @@
 # Guided tour
 
-In this guided tour, you'll learn how to create a code action agent using freeact. 
+In this guided tour, you'll learn how to create **code action agents** using `freeact`.
 
 We'll explore how to leverage different language models, execute code securely, enhance agent capabilities through skills, and guide agent behavior using natural language instructions.
 
 ## Setup
 
-Before you start, install the freeact package:
+Before you start, install the `freeact` package:
 
 ```bash
 pip install freeact
@@ -20,7 +20,7 @@ GEMINI_API_KEY=<your-api-key>
 
 ## Building an agent
 
-Let's explore the essential components you'll need to build a freeact agent:
+Let's explore the essential components you'll need to build a `freeact` agent:
 
 * [`CodeActModel`][freeact.model.CodeActModel]: a language model that generates Python code or provides final answers upon task completion. It acts as the decision-making engine of the agent.
 
@@ -63,7 +63,7 @@ The examples below show how to do this using [`Claude`][freeact.model.claude.mod
 
         Support for Qwen models is still experimental. Larger Qwen 2.5 Coder models work reasonably well, but smaller models may require optimization of prompt templates.
 
-The `stream_turn` helper function uses freeact's [streaming protocol](streaming.md) to handle intermediate and final outputs of the agent. This happens in a sequence of:
+The `stream_turn` helper function uses `freeact`'s [streaming protocol](streaming.md) to handle intermediate and final outputs of the agent. This happens in a sequence of:
 
 * **model turns** ([`CodeActModelTurn`][freeact.model.CodeActModelTurn]) containing the model response and an optional code action and
 * **code executions** ([`CodeExecution`][freeact.agent.CodeExecution]) containing the result from running a code action in the execution environment.
@@ -98,7 +98,7 @@ Code actions generated by an agent are executed in a secure, containerized envir
 
 ## Agent skills
 
-In freeact, agents gain their capabilities through skills - **reusable Python modules that implement specific functionality**. Each skill module contains code that can be used by the agent to perform tasks like searching the web, analyzing data, or interacting with external services. 
+In `freeact`, agents gain their capabilities through skills - **reusable Python modules that implement specific functionality**. Each skill module contains code that can be used by the agent to perform tasks like searching the web, analyzing data, or interacting with external services. 
 You can use predefined skills from the [freeact-skills](https://github.com/gradion-ai/freeact-skills) library, create custom skills yourself, or develop them collaboratively through [interactive sessions with an agent](tutorials/skills.md).
 
 Skill modules need to be available to the Docker container running the execution environment. This can be done using one of the following methods:
@@ -194,7 +194,7 @@ curl -o workspace/skills/shared/weather/weather_report.py https://raw.githubuser
 
 ### The anatomy of a skill
 
-Let's take a closer look at how skills are implemented in freeact. Skills are Python modules that can be structured as a single module, multiple modules, or a complete package. 
+Let's take a closer look at how skills are implemented in `freeact`. Skills are Python modules that can be structured as a single module, multiple modules, or a complete package. 
 
 While all modules of a skill must be available to the execution environment, you can select which specific parts of the skill to include in the model prompt for the agent. This design lets you control whether the agent sees the complete skill implementation or just its public interface.