Refine, Test, and Improve Your Workflow

The fastest path to a better workflow is a tight loop: describe what you want, review the diff, test, and compare the result.

:dart: What You'll Do

You'll use the agentic-workflows Copilot skill — installed in your practice repository during Step 7 — to edit, debug, and optimize daily-report-status.md, then trigger a fresh run and compare the output against the previous one.

By the end of this step, your workflow will produce more useful output, and you'll have a repeatable iteration loop you can use any time the workflow output is vague, incorrect, or missing something important.

:clipboard: Before You Start

• Completed Interpret Your First Run
• Your daily-report-status workflow has at least one completed run
• .github/skills/agentic-workflows/ exists in your practice repository (created during Step 7)

What is the agentic-workflows skill?

The agentic-workflows skill is a Copilot skill installed in your practice repository. It acts as a dispatcher: when you describe a workflow task in plain English and mention the skill by name, it routes your request to the right editing, debugging, or optimizing prompt and makes changes directly in your repository.

You invoke it in Copilot CLI in your Codespace terminal:

gh copilot

Then send:

/agentic-workflows [your request here]

The skill recognizes three core task types for day-to-day workflow maintenance:

If you are working locally or in a Codespace without a Copilot session, the terminal path in each section below shows the equivalent manual change.

Start With One Concrete Observation

Open the latest run in the Actions tab and look for one thing you want to improve.

Good examples:

• The summary is too generic.
• An important detail is missing.
• The tone feels too stiff.
• The formatting is inconsistent.

Pick only one problem for this round. Small, isolated changes make it much easier to tell what actually improved the result.

Edit: improve the workflow brief

After reviewing the run output, you may have noticed the agent's comment was generic. You'll now make the brief more specific so the agent explains why the most-reacted issue matters, not just which one it is.

In your Codespace terminal, run gh copilot and paste:

/agentic-workflows update .github/workflows/daily-report-status.md
so that the agent adds one sentence explaining why resolving the most-reacted issue
would benefit the team. Keep the existing [safe-output](https://github.github.com/gh-aw/reference/safe-outputs/) constraint (at most one comment).

The skill loads the update prompt, makes the targeted change to the Markdown body, recompiles the workflow, and shows you the diff. Review the updated Markdown body and confirm the new instruction is clear and specific before committing.

After identifying the most-reacted issue, write one sentence explaining why resolving it
would benefit the team, based on the issue title and description.

gh aw compile
git add .
git commit -m "feat: add team-benefit sentence to daily-report-status brief"
git push

Debug: investigate unexpected output

If your run from Step 8 finished but the output was empty, vague, or missing entirely, use the skill to diagnose the most likely cause and propose a fix.

In your Codespace terminal, run gh copilot, then paste this prompt, replacing the bracketed text with what you actually observed:

/agentic-workflows debug .github/workflows/daily-report-status.md.
The last run [describe the problem — for example: "posted a comment but left the
summary blank" or "finished without posting anything"].
Suggest the most likely cause and propose one change to the workflow brief to fix it.

The skill reads the workflow file, identifies likely causes — such as a vague brief, a missing fallback instruction, or an over-broad safe-output surface — and proposes a targeted, minimal fix.

If no open issues have 👍 reactions, post a comment on the most recently updated
open issue instead.

Optimize: reduce token usage

Once the workflow produces correct output, you can reduce how much AI Credit it uses per run. This matters especially for workflows that run on a schedule.

In your Codespace terminal, run gh copilot, then paste:

/agentic-workflows optimize .github/workflows/daily-report-status.md
to reduce token usage. Apply only changes that do not change the workflow's outcome.

The skill applies techniques such as removing redundant instructions, consolidating repeated constraints, and trimming unused safe-output declarations.

Commit Both Workflow Files

Commit both the source workflow and the recompiled lock file:

git add .
git commit -m "refine daily-report-status workflow output"
git push

If your workflow uses a different filename, stage that .md file and its matching .lock.yml file instead.

Trigger a Fresh Run and Compare

Use workflow_dispatch from the Actions tab to trigger a new run. Then compare the latest result with the previous one.

Ask yourself:

• Did the new run reflect the change you made?
• Is the output more useful than before?
• Did you improve the original problem without creating a new one?

If yes, keep the change. If not, revert the change and try a different adjustment.

If you want a stricter review loop, score each run for accuracy, completeness, and tone before you decide what to change next.

✅ Checkpoint

• I identified one specific problem from a real workflow run
• I used the /agentic-workflows skill (or made a manual edit) to address it
• The compiled lock file was updated and committed alongside the workflow source
• Both daily-report-status.md and daily-report-status.lock.yml are committed and pushed
• I compared the new run with the previous run and decided what to change next

Next: What's Next? Keep Exploring