envs/_template: make template instantiable against real EnvAdapter ABC

The shipped env_template.py and loader_template.py described the same
fictional async execute / evaluate / build_prompt API documented in
docs/reference/api.md. As a result TemplateBenchmarkEnv(cfg) raised
'TypeError: Can't instantiate abstract class' for every copy-and-paste
user who followed the in-tree scaffold.

Rewrite the template so it's a working starting point:

- env_template.py: TemplateBenchmarkEnv(EnvAdapter) now implements all
  five real abstract methods (build_train_env, build_eval_env, rollout,
  reflect, get_task_types) with no-op defaults documented as TODO.
  Instantiable today; pytest 60/60 still passes.
- loader_template.py: TemplateBenchmarkLoader(SplitDataLoader)
  implements load_split_items for .json / .jsonl input and explains the
  optional load_raw_items override for split_mode="ratio".
- README.md: usage steps now point at scripts/train.py's _ENV_REGISTRY
  (the real registry) instead of a non-existent BENCHMARK_REGISTRY in
  skillopt/envs/__init__.py, and link to the rewritten new-benchmark
  guide.
- config_template.yaml: _base_ is a string path (not a list, which the
  loader rejects); skill_init is commented out with a note so the
  template config doesn't reference a file the user hasn't created.

Verified locally: 'from skillopt.envs._template.env_template import
TemplateBenchmarkEnv; TemplateBenchmarkEnv()' succeeds. Refs
microsoft/SkillOpt#30.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

skillopt/envs/_template/config_template.yaml

@@ -4,27 +4,36 @@
 # Copy this file to configs/<your_benchmark>/default.yaml
 # and customize the values below.
 
-# Inherit global defaults
-_base_: ['../_base_/default.yaml']
+# Inherit global defaults.
+# NOTE: `_base_` is a string path, not a list.
+_base_: ../_base_/default.yaml
 
 # ── Environment ──────────────────────────────────
 env:
-  name: your_benchmark            # Must match registry key
-  data_path: data/your_benchmark  # Path to your data
+  name: your_benchmark            # Must match the key registered in scripts/train.py
+  # Optional: a seed skill document. Create this file yourself before the
+  # first run, or omit the key to start from an empty skill.
+  # skill_init: skillopt/envs/your_benchmark/skills/initial.md
+  data_path: data/your_benchmark  # Path to your data (for split_mode: ratio)
+  split_dir: ""                   # Set this and use split_mode: split_dir for pre-split data
   split_mode: ratio               # "ratio" or "split_dir"
-  split_ratio: "2:1:7"            # train:val:test
-  exec_timeout: 120               # Per-task timeout (seconds)
+  split_ratio: "2:1:7"            # train:val:test (used when split_mode: ratio)
+  workers: 4                      # Parallel rollout workers
+  max_completion_tokens: 4096     # Cap per target-model call
+  limit: 0                        # 0 = no limit; small int = debug sample
 
 # ── Training ─────────────────────────────────────
 train:
-  num_epochs: 4                   # Number of epochs
-  batch_size: 40                  # Tasks per step (batch size)
+  num_epochs: 4
+  batch_size: 40
+  accumulation: 1
   seed: 42
 
 # ── Gradient (Reflection) ───────────────────────
 gradient:
   analyst_workers: 16             # Parallel reflection workers
   minibatch_size: 8
+  merge_batch_size: 8
 
 # ── Optimizer ────────────────────────────────────
 optimizer:
@@ -39,7 +48,8 @@ evaluation:
   eval_test: true                 # Run test eval after training
 
 # ── Model ────────────────────────────────────────
+# Override only what differs from the inherited defaults.
 model:
-  backend: azure_openai           # azure_openai | openai_chat | claude_code_exec | qwen
-  optimizer: gpt-4o
-  target: gpt-4o
+  optimizer_backend: openai_chat  # openai_chat | claude_chat | qwen_chat | minimax_chat
+  target_backend: openai_chat     # … plus codex_exec / claude_code_exec for target only
+  reasoning_effort: medium