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/envs/_template/config_template.yaml

@@ -31,7 +31,7 @@ optimizer:
   learning_rate: 4                # Max edits per step (edit budget)
   lr_scheduler: cosine            # cosine | linear | constant | autonomous
   use_slow_update: true           # Epoch-boundary momentum
-  use_meta_skill: true            # Cross-epoch teacher memory
+  use_meta_skill: true            # Cross-epoch optimizer memory
 
 # ── Evaluation ───────────────────────────────────
 evaluation:
@@ -41,5 +41,5 @@ evaluation:
 # ── Model ────────────────────────────────────────
 model:
   backend: azure_openai           # azure_openai | openai_chat | claude_code_exec | qwen
-  teacher: gpt-5.5
-  student: gpt-5.5
+  optimizer: gpt-4o
+  target: gpt-4o

skillopt/envs/_template/env_template.py

@@ -4,7 +4,7 @@ Benchmark Environment Template
 Copy this file and implement the TODO sections to add a new benchmark.
 
 The EnvAdapter is responsible for:
-1. Executing tasks using the student model + current skill document
+1. Executing tasks using the target model + current skill document
 2. Evaluating predictions against ground truth
 3. Returning structured results for the training loop
 """
@@ -25,12 +25,12 @@ class TemplateBenchmarkEnv(EnvAdapter):
 
     async def execute(self, item, skill: str, model):
         """
-        Execute a single task with the student model.
+        Execute a single task with the target model.
 
         Args:
             item: DataItem with .id, .input, .ground_truth, .metadata
             skill: Current skill document content (Markdown string)
-            model: Student model backend instance
+            model: Target model backend instance
 
         Returns:
             TaskResult with prediction, score, and trajectory
@@ -38,7 +38,7 @@ class TemplateBenchmarkEnv(EnvAdapter):
         # Step 1: Build the prompt combining skill + task input
         prompt = self.build_prompt(item, skill)
 
-        # Step 2: Call the student model
+        # Step 2: Call the target model
         # TODO: Customize the message format for your benchmark
         messages = [
             {"role": "system", "content": skill},