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workshop docs: add gh-aw doc links (round 8) (#1954)
- side-quest-13-04: link AIC to cost-management reference, gh aw audit to audit reference, add Cost Management link near guardrails - side-quest-16-01: link Frontmatter to frontmatter reference - side-quest-16-02: link frontmatter to frontmatter reference - side-quest-16-05: link permissions to permissions reference Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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workshop/side-quest-13-04-token-optimization.md

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- You completed [Build Your First Event-Driven Workflow: PR Auto-Reviewer](14b-pr-reviewer-workflow.md).
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- You have a working PR reviewer workflow or another workflow with at least 5 completed runs so you can compare before-and-after usage.
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- If you want extra background on AIC, audit artifacts, or budget guardrails, continue later to [Audit and Monitor Your Agentic Workflows](25-audit-and-observability.md) and [Manage Costs and AI Credit Budgets](26-manage-costs-and-budgets.md).
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- If you want extra background on [AIC](https://github.github.com/gh-aw/reference/cost-management/#ai-credits-aic), audit artifacts, or budget guardrails, continue later to [Audit and Monitor Your Agentic Workflows](25-audit-and-observability.md) and [Manage Costs and AI Credit Budgets](26-manage-costs-and-budgets.md).
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## Build a cost baseline
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gh aw audit <run-id> --parse
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```
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> See the [gh aw audit reference](https://github.github.com/gh-aw/reference/audit/#gh-aw-audit) for full options.
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Then inspect:
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- `log.md` for long agent turns or repeated reasoning
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- Use `timeout-minutes` to stop unusually long runs.
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- Use [gh aw forecast](side-quest-26-01-forecast-costs.md) to size the guardrails from real history instead of guessing.
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> See [Cost Management](https://github.github.com/gh-aw/reference/cost-management/) for the full list of monitoring commands and guardrail options.
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## Try it yourself
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### Run one optimization cycle

workshop/side-quest-16-01-github-output.md

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Once your data is in `$GITHUB_OUTPUT`, you reference it directly inside the workflow Markdown body — which **is** the AI prompt in gh-aw. There is no separate step to invoke the AI; the body text is sent to the model after all step outputs have been resolved.
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**Frontmatter** (data-preparation step):
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**[Frontmatter](https://github.github.com/gh-aw/reference/frontmatter/)** (data-preparation step):
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```yaml
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steps:

workshop/side-quest-16-02-secrets-and-permissions.md

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<details>
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<summary>Learn how permissions frontmatter controls the built-in `GITHUB_TOKEN`</summary>
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gh-aw workflows declare required [permissions](https://github.github.com/gh-aw/reference/permissions/) in frontmatter. Only request what you need:
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gh-aw workflows declare required [permissions](https://github.github.com/gh-aw/reference/permissions/) in [frontmatter](https://github.github.com/gh-aw/reference/frontmatter/). Only request what you need:
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```yaml
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---

workshop/side-quest-16-05-long-lived-credentials.md

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### Keep `permissions:` minimal
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Even an ephemeral `GITHUB_TOKEN` carries risk if it is over-scoped. Declare only the permissions your task actually needs. Compare the two blocks below:
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Even an ephemeral `GITHUB_TOKEN` carries risk if it is over-scoped. Declare only the [permissions](https://github.github.com/gh-aw/reference/permissions/) your task actually needs. Compare the two blocks below:
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```yaml
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# ❌ Risky: broad write scopes for a read-only task

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