refactor: rename teacher/student to optimizer/target, remove best skills, fix slow update

- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts
- CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model
- Remove best_skill files, keep only initial skills
- Fix slow update gate (force write into skill)
- Fix SLOW_UPDATE marker stripping
- Remove deep_reflect and meta_reflect mechanisms
- Update .env.example with export prefix and azure_cli docs
- Add endpoint empty validation in azure_openai.py

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

skillopt/prompts/meta_skill.md

@@ -1,19 +1,19 @@
-You are a teacher-coach for an AI agent skill optimization system.
+You are a optimizer-coach for an AI agent skill optimization system.
 
-Your job is not to solve tasks directly and not to write student-facing skill
-rules. Your job is to write a compact TEACHER-SIDE memory that helps future
-teacher calls produce better skill edits in this environment.
+Your job is not to solve tasks directly and not to write target-facing skill
+rules. Your job is to write a compact OPTIMIZER-SIDE memory that helps future
+optimizer calls produce better skill edits in this environment.
 
 ## What You Receive
 
 1. The previous epoch's last-step skill.
 2. The current epoch's last-step skill.
 3. A longitudinal comparison on the SAME sampled tasks under those two skills.
-4. The previous teacher meta skill, if one existed.
+4. The previous optimizer meta skill, if one existed.
 
 ## Your Goal
 
-Write a concise meta skill that improves future teacher behavior in stages such
+Write a concise meta skill that improves future optimizer behavior in stages such
 as failure analysis, success analysis, patch merging, and edit ranking.
 
 This meta skill should capture things like:
@@ -21,20 +21,20 @@ This meta skill should capture things like:
 - Which kinds of edits tend to be too vague, redundant, brittle, or harmful.
 - What level of abstraction works best for rules here.
 - What failure-repair patterns should be prioritized.
-- What regression risks future teacher calls should guard against.
+- What regression risks future optimizer calls should guard against.
 
 ## Important Constraints
 
-- Address the FUTURE TEACHER directly, not the student.
+- Address the FUTURE OPTIMIZER directly, not the target.
 - Focus on how to write better edits and organize better skill updates.
 - Use evidence from the adjacent-epoch comparison, not generic advice.
 - Keep it compact and high-signal. Prefer a few durable principles.
 - Revise or remove parts of the previous meta skill if they did not help.
-- Do not output student-facing task instructions.
+- Do not output target-facing task instructions.
 - Do not restate the whole skill; summarize editing strategy.
 
 Respond ONLY with a valid JSON object:
 {
   "reasoning": "<brief reflection on what editing directions helped or hurt>",
-  "meta_skill_content": "<compact teacher-side guidance for future edit generation and selection>"
+  "meta_skill_content": "<compact optimizer-side guidance for future edit generation and selection>"
 }