Voice + consistency: vary stock formulas, vendor-balance orient.py, unify loop (#48,#49,#51) (#68)

Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>

modules/09-issues-and-the-task-layer/README.md

@@ -40,9 +40,9 @@ By the end of this module you can:
 
 ### What an issue actually is (for this audience)
 
-Strip the project-management vocabulary away and an issue is one thing: **a written, addressable unit
-of work that lives next to the code instead of in someone's head, a Slack thread, or a chat tab.**
-It has a title, a body, and metadata (labels, an assignee, a status). It gets a stable number. You
+An issue is **a written, addressable unit of work that lives next to the code instead of in
+someone's head, a Slack thread, or a chat tab.** The project-management vocabulary around it varies;
+that core doesn't. It has a title, a body, and metadata (labels, an assignee, a status). It gets a stable number. You
 can link to it, search it, and close it.
 
 You already know this shape — it's a ticket. Jira, Linear, ServiceNow, a help-desk queue: same idea.
@@ -198,9 +198,8 @@ You don't need any of that yet. You need issues good enough to feed it. That's t
 
 ## The AI angle
 
-A generic project-management lesson would teach the same issue tracker. What's specific to
-AI-assisted work is that **the issue has quietly become an agent's task specification**, and that
-raises the stakes on writing it well in three concrete ways:
+The issue tracker itself isn't new. What's changed is that **the issue has quietly become an agent's
+task specification**, and that raises the stakes on writing it well in three concrete ways:
 
 - **Acceptance criteria are the agent's definition of done.** A human reads fuzzy criteria and fills
   the gaps with judgment. An agent reads them literally and stops when they're satisfied — so vague