[docs-linker] workshop docs: add gh-aw doc links

workshop/17-add-mcp-tools.md

@@ -6,7 +6,7 @@
 
 ## 🎯 What You'll Do
 
-You'll add an MCP (Model Context Protocol) server to your workflow's [frontmatter](https://github.github.com/gh-aw/reference/frontmatter/), giving the AI agent access to a new set of [tools](https://github.github.com/gh-aw/reference/tools/) it can call at runtime. By the end, your daily-status workflow will be able to do more than just generate text — it can interact with live data sources using structured tool calls.
+You'll add an [MCP (Model Context Protocol)](https://github.github.com/gh-aw/guides/mcps/) server to your workflow's [frontmatter](https://github.github.com/gh-aw/reference/frontmatter/), giving the AI agent access to a new set of [tools](https://github.github.com/gh-aw/reference/tools/) it can call at runtime. By the end, your daily-status workflow will be able to do more than just generate text — it can interact with live data sources using structured tool calls.
 
 ## 📋 Before You Start
 

workshop/18-share-and-reuse.md

@@ -6,7 +6,7 @@
 
 ## 🎯 What You'll Do
 
-You'll copy your finished workflow file into a shared location so that teammates can add it to their own repositories with a single command. By the end of this step you'll have a reusable workflow template and know how to distribute it.
+You'll copy your finished workflow file into a shared location so that teammates can add it to their own repositories with a single command. By the end of this step you'll have a [reusable workflow template](https://github.github.com/gh-aw/guides/reusing-workflows/) and know how to distribute it.
 
 ## 📋 Before You Start
 

workshop/20-persistent-memory.md

@@ -23,7 +23,7 @@
 
 ## 🎯 What You'll Do
 
-You'll add persistent memory to your agentic workflow so it can carry state between runs. By the end of this step, your workflow will remember what it has already reported on and skip duplicates — so your team never gets the same alert twice.
+You'll add [persistent memory](https://github.github.com/gh-aw/patterns/memory-ops/) to your agentic workflow so it can carry state between runs. By the end of this step, your workflow will remember what it has already reported on and skip duplicates — so your team never gets the same alert twice.
 
 ## 📋 Before You Start
 

workshop/23-ab-experiments.md

@@ -63,7 +63,7 @@ Write according to the output_style: ${{ experiments.output_style }}.
   CI status, and a one-paragraph summary at the top.
 {{#endif}}
 
-Always call the safe output tool — even if there is no activity.
+Always call the [safe output](https://github.github.com/gh-aw/reference/safe-outputs/) tool — even if there is no activity.
 ```
 
 Compile and commit:

workshop/24-self-hosted-runners.md

@@ -13,7 +13,7 @@ rather than a GitHub-hosted machine.
 ## 📋 Before You Start
 
 - Your agentic workflow runs successfully (see [Test and Iterate](12-test-and-iterate.md)).
-- A self-hosted runner is registered and **online** for your repository or organisation.
+- A [self-hosted runner](https://github.github.com/gh-aw/reference/self-hosted-runners/) is registered and **online** for your repository or organisation.
   If you need to set one up first, see [Side Quest: Enterprise Setup Considerations](side-quest-enterprise-setup.md).
 - You know the label assigned to your runner (for example, `self-hosted`, `ubuntu-self-hosted`, or a custom label your admin configured).
 

workshop/25-audit-and-observability.md

@@ -17,7 +17,7 @@ You will use `gh aw logs` and `gh aw audit` to review the built-in artifacts tha
 
 ### Review recent runs with gh aw logs
 
-`gh aw logs` downloads artifacts from your workflow's recent runs and prints a summary table showing duration, token usage, and cost in AI Credits (AIC).
+`gh aw logs` downloads artifacts from your workflow's recent runs and prints a summary table showing duration, token usage, and cost in [AI Credits (AIC)](https://github.github.com/gh-aw/reference/cost-management/#ai-credits-aic).
 
 Run it from inside your repository: