diff --git a/workshop/23-ab-experiments.md b/workshop/23-ab-experiments.md index c48ec592..7368dd04 100644 --- a/workshop/23-ab-experiments.md +++ b/workshop/23-ab-experiments.md @@ -119,7 +119,7 @@ Write a report according to the output_style: ${{ experiments.output_style }}. 1. Using your confirmed 1:1 counts for `concise` and `detailed`, predict the next three assignments. 2. Run the workflow three times and compare your prediction with activation logs and `experiment` counts. -## Understand how the round-robin works +## Understand how the [round-robin](https://github.github.com/gh-aw/experimental/experiments/#statistical-balancing) works @@ -135,7 +135,7 @@ On each run, gh-aw: 1. Loads state from `experiments/{workflow-id}` (created on first run). 2. Picks the variant with the lowest invocation count (ties are broken by first-in-array order). 3. Saves the updated counts. -4. Uploads the `experiment` artifact. +4. Uploads the `experiment` [artifact](https://github.github.com/gh-aw/reference/artifacts/). 5. Injects the selected variant into your template conditionals. diff --git a/workshop/24-self-hosted-runners.md b/workshop/24-self-hosted-runners.md index 3d80b01b..e8faedf8 100644 --- a/workshop/24-self-hosted-runners.md +++ b/workshop/24-self-hosted-runners.md @@ -78,7 +78,7 @@ registration settings (Settings → Actions → Runners). > [!TIP] > Labels act as filters. A workflow job is dispatched to the first idle runner that satisfies all labels in the list. Adding `linux` alongside `self-hosted` ensures the job only lands on Linux runners when your fleet is mixed. -Running in an enterprise environment? See [Side Quest: Self-Hosted Runner Infrastructure Deep Dive](side-quest-24-01-runner-infrastructure.md) for guidance on ephemeral and JIT runners, proxy configuration, and network isolation for air-gapped environments. +Running in an enterprise environment? See [Side Quest: Self-Hosted Runner Infrastructure Deep Dive](side-quest-24-01-runner-infrastructure.md) for guidance on ephemeral and JIT runners, proxy configuration, and [network](https://github.github.com/gh-aw/reference/network/) isolation for air-gapped environments. ## :pencil2: Exercise: Compile and commit diff --git a/workshop/25-audit-and-observability.md b/workshop/25-audit-and-observability.md index 6fc69f06..f0f26a67 100644 --- a/workshop/25-audit-and-observability.md +++ b/workshop/25-audit-and-observability.md @@ -6,7 +6,7 @@ ## :dart: What You'll Do -Use `gh aw logs` and `gh aw audit` to review the built-in [artifacts](https://github.github.com/gh-aw/reference/artifacts/) that every agentic workflow run produces, understand token usage, and debug unexpected behavior. By the end you know where to look when a run behaves unexpectedly or when a compliance review asks what the agent did. +Use `gh aw logs` and `gh aw audit` to review the built-in [artifacts](https://github.github.com/gh-aw/reference/artifacts/) that every agentic workflow run produces, understand [token usage](https://github.github.com/gh-aw/reference/cost-management/#monitoring-costs-with-gh-aw-logs), and debug unexpected behavior. By the end you know where to look when a run behaves unexpectedly or when a compliance review asks what the agent did. ## :clipboard: Before You Start @@ -73,7 +73,7 @@ suggest how to reduce it. ``` -The skill understands agentic workflow [frontmatter](https://github.github.com/gh-aw/reference/frontmatter/) and safe-output rules. It can suggest a more efficient prompt, validate your changes, or walk you through a fix — all without leaving the chat. Ask the agent to make edits directly so it can run `gh aw compile` to validate before committing. +The skill understands agentic workflow [frontmatter](https://github.github.com/gh-aw/reference/frontmatter/) and [safe-output](https://github.github.com/gh-aw/reference/safe-outputs/) rules. It can suggest a more efficient prompt, validate your changes, or walk you through a fix — all without leaving the chat. Ask the agent to make edits directly so it can run `gh aw compile` to validate before committing. ### Browse artifacts in the GitHub UI diff --git a/workshop/26-manage-costs-and-budgets.md b/workshop/26-manage-costs-and-budgets.md index 74a9f68d..1baa2d71 100644 --- a/workshop/26-manage-costs-and-budgets.md +++ b/workshop/26-manage-costs-and-budgets.md @@ -54,7 +54,7 @@ Look at the **AIC** column. Average the last five runs, then multiply: monthly cost = average AIC per run × runs per day × 30 ``` -If your workflow averages 1.5 AIC and runs once a day: `1.5 × 1 × 30 = 45 AIC per month`. Share this estimate with your GitHub administrator before enabling a high-frequency schedule. +If your workflow averages 1.5 AIC and runs once a day: `1.5 × 1 × 30 = 45 AIC per month`. Share this estimate with your GitHub administrator before enabling a high-frequency [schedule](https://github.github.com/gh-aw/reference/triggers/#scheduled-triggers-schedule). ### Project costs with [gh aw forecast](https://github.github.com/gh-aw/setup/cli/#forecast-experimental) diff --git a/workshop/27-evaluate-workflow-quality.md b/workshop/27-evaluate-workflow-quality.md index b4901d28..52eacade 100644 --- a/workshop/27-evaluate-workflow-quality.md +++ b/workshop/27-evaluate-workflow-quality.md @@ -18,7 +18,7 @@ ## :dart: What You'll Do -You'll add an `evals:` block to your workflow, define binary quality questions, run the workflow, and verify that results are recorded in the `evals` artifact and persisted for historical comparison. +You'll add an `evals:` block to your workflow, define binary quality questions, run the workflow, and verify that results are recorded in the [`evals` artifact](https://github.github.com/gh-aw/reference/artifacts/#evals) and persisted for historical comparison. ## :clipboard: Before You Start diff --git a/workshop/side-quest-05-01-actions-power-user.md b/workshop/side-quest-05-01-actions-power-user.md index 858dcd70..90324290 100644 --- a/workshop/side-quest-05-01-actions-power-user.md +++ b/workshop/side-quest-05-01-actions-power-user.md @@ -66,7 +66,7 @@ Key differences at a glance: Think of agentic workflows as a superset of Actions: - Frontmatter remains compatible with the Actions model you already know. -- The Markdown body becomes the runtime prompt and can include templating and inline agent features. +- The Markdown body becomes the runtime prompt and can include [templating](https://github.github.com/gh-aw/reference/templating/) and inline agent features. - You can still keep deterministic logic when that is the right tool for the job. ## Hybrid pattern for real teams