Merge in claude-code skill and bump version (0.3.9)

README.md

@@ -107,7 +107,25 @@ For more advanced examples, including [Triton](/examples/triton/README.md), [CUD
 |---------|-------------|-------------|
 | `weco run [options]` | Direct optimization execution | **For advanced users** - When you know exactly what to optimize and how |
 | `weco resume <run-id>` | Resume an interrupted run | Continue from the last completed step |
+| `weco login` | Authenticate with Weco | First-time setup or switching accounts |
 | `weco logout` | Clear authentication credentials | To switch accounts or troubleshoot authentication issues |
+| `weco credits balance` | Check your current credit balance | Monitor usage |
+| `weco credits topup [amount]` | Purchase additional credits | When you need more credits (default: 10) |
+| `weco credits autotopup` | Configure automatic top-up | Set up automatic credit replenishment |
+
+### Setup Commands (Experimental)
+
+| Command | Description |
+|---------|-------------|
+| `weco setup claude-code` | Set up Weco skill for Claude Code |
+
+The `setup` command installs Weco skills for AI coding assistants. Currently supports Claude Code:
+
+```bash
+weco setup claude-code
+```
+
+This clones the Weco skill to `~/.claude/skills/weco/` and optionally updates your `~/.claude/CLAUDE.md` to enable automatic skill discovery.
 
 ### Model Selection
 

pyproject.toml

@@ -8,7 +8,7 @@ name = "weco"
 authors = [{ name = "Weco AI Team", email = "contact@weco.ai" }]
 description = "Documentation for `weco`, a CLI for using Weco AI's code optimizer."
 readme = "README.md"
-version = "0.3.8"
+version = "0.3.9"
 license = { file = "LICENSE" }
 requires-python = ">=3.8"
 dependencies = [

weco/cli.py

@@ -137,6 +137,13 @@ def configure_run_parser(run_parser: argparse.ArgumentParser) -> None:
 {default_models_for_providers}
 """,
     )
+    run_parser.add_argument(
+        "--output",
+        type=str,
+        choices=["rich", "plain"],
+        default="rich",
+        help="Output mode: 'rich' for interactive terminal UI (default), 'plain' for machine-readable text output suitable for LLM agents.",
+    )
 
 
 def configure_credits_parser(credits_parser: argparse.ArgumentParser) -> None:
@@ -178,6 +185,12 @@ def _parse_credit_amount(value: str) -> float:
     )
 
 
+def configure_setup_parser(setup_parser: argparse.ArgumentParser) -> None:
+    """Configure the setup command parser and its subcommands."""
+    setup_subparsers = setup_parser.add_subparsers(dest="tool", help="AI tool to set up")
+    setup_subparsers.add_parser("claude-code", help="Set up Weco skill for Claude Code")
+
+
 def configure_resume_parser(resume_parser: argparse.ArgumentParser) -> None:
     """Configure arguments for the resume command."""
     resume_parser.add_argument(
@@ -208,6 +221,13 @@ def configure_resume_parser(resume_parser: argparse.ArgumentParser) -> None:
 Supported provider names: {supported_providers}.
 """,
     )
+    resume_parser.add_argument(
+        "--output",
+        type=str,
+        choices=["rich", "plain"],
+        default="rich",
+        help="Output mode: 'rich' for interactive terminal UI (default), 'plain' for machine-readable text output suitable for LLM agents.",
+    )
 
 
 def execute_run_command(args: argparse.Namespace) -> None:
@@ -253,6 +273,7 @@ def execute_run_command(args: argparse.Namespace) -> None:
         api_keys=api_keys,
         apply_change=args.apply_change,
         require_review=args.require_review,
+        output_mode=args.output,
     )
 
     exit_code = 0 if success else 1
@@ -269,7 +290,9 @@ def execute_resume_command(args: argparse.Namespace) -> None:
         console.print(f"[bold red]Error parsing API keys: {e}[/]")
         sys.exit(1)
 
-    success = resume_optimization(run_id=args.run_id, api_keys=api_keys, apply_change=args.apply_change)
+    success = resume_optimization(
+        run_id=args.run_id, api_keys=api_keys, apply_change=args.apply_change, output_mode=args.output
+    )
 
     sys.exit(0 if success else 1)
 
@@ -322,6 +345,10 @@ def _main() -> None:
     )
     configure_resume_parser(resume_parser)
 
+    # --- Setup Command Parser Setup ---
+    setup_parser = subparsers.add_parser("setup", help="Set up Weco for use with AI tools")
+    configure_setup_parser(setup_parser)
+
     args = parser.parse_args()
 
     if args.command == "login":
@@ -349,6 +376,11 @@ def _main() -> None:
         sys.exit(0)
     elif args.command == "resume":
         execute_resume_command(args)
+    elif args.command == "setup":
+        from .setup import handle_setup_command
+
+        handle_setup_command(args, console)
+        sys.exit(0)
     else:
         # This case should be hit if 'weco' is run alone and chatbot logic didn't catch it,
         # or if an invalid command is provided.

weco/optimizer.py

@@ -25,7 +25,7 @@
 )
 from .auth import handle_authentication
 from .browser import open_browser
-from .ui import OptimizationUI, LiveOptimizationUI
+from .ui import OptimizationUI, LiveOptimizationUI, PlainOptimizationUI
 from .utils import read_additional_instructions, read_from_path, write_to_path, run_evaluation_with_file_swap
 
 
@@ -324,7 +324,11 @@ def _offer_apply_best_solution(
 
 
 def resume_optimization(
-    run_id: str, api_keys: Optional[dict] = None, poll_interval: float = 2.0, apply_change: bool = False
+    run_id: str,
+    api_keys: Optional[dict] = None,
+    poll_interval: float = 2.0,
+    apply_change: bool = False,
+    output_mode: str = "rich",
 ) -> bool:
     """
     Resume an interrupted run using the queue-based optimization loop.
@@ -337,11 +341,12 @@ def resume_optimization(
         api_keys: Optional API keys for LLM providers.
         poll_interval: Seconds between polling attempts.
         apply_change: If True, automatically apply best solution; if False, prompt user.
+        output_mode: "rich" for interactive terminal UI, "plain" for machine-readable output.
 
     Returns:
         True if optimization completed successfully, False otherwise.
     """
-    console = Console()
+    console = Console(force_terminal=output_mode == "rich")
 
     # Authenticate
     weco_api_key, auth_headers = handle_authentication(console)
@@ -431,9 +436,17 @@ def resume_optimization(
 
     result: Optional[OptimizationResult] = None
     try:
-        with LiveOptimizationUI(
-            console, run_id, run_name, total_steps, dashboard_url, model=model_name, metric_name=metric_name
-        ) as ui:
+        # Select UI implementation based on output mode
+        if output_mode == "plain":
+            ui_instance = PlainOptimizationUI(
+                run_id, run_name, total_steps, dashboard_url, model=model_name, metric_name=metric_name
+            )
+        else:
+            ui_instance = LiveOptimizationUI(
+                console, run_id, run_name, total_steps, dashboard_url, model=model_name, metric_name=metric_name
+            )
+
+        with ui_instance as ui:
             # Populate UI with best solution from previous run if available
             if best_metric_value is not None and best_step is not None:
                 ui.on_metric(best_step, best_metric_value)
@@ -459,7 +472,10 @@ def resume_optimization(
 
         # Show resume message if interrupted
         if result.status == "terminated":
-            console.print(f"\n[cyan]To resume this run, use:[/] [bold]weco resume {run_id}[/]\n")
+            if output_mode == "plain":
+                print(f"\nTo resume this run, use: weco resume {run_id}\n", flush=True)
+            else:
+                console.print(f"\n[cyan]To resume this run, use:[/] [bold]weco resume {run_id}[/]\n")
 
         # Offer to apply best solution
         _offer_apply_best_solution(
@@ -507,6 +523,7 @@ def optimize(
     poll_interval: float = 2.0,
     apply_change: bool = False,
     require_review: bool = False,
+    output_mode: str = "rich",
 ) -> bool:
     """
     Simplified queue-based optimization loop.
@@ -528,11 +545,12 @@ def optimize(
         api_keys: Optional API keys for LLM providers.
         poll_interval: Seconds between polling attempts.
         apply_change: If True, automatically apply best solution; if False, prompt user.
+        output_mode: "rich" for interactive terminal UI, "plain" for machine-readable output.
 
     Returns:
         True if optimization completed successfully, False otherwise.
     """
-    console = Console()
+    console = Console(force_terminal=output_mode == "rich")
 
     # Authenticate
     weco_api_key, auth_headers = handle_authentication(console)
@@ -596,7 +614,13 @@ def optimize(
 
     result: Optional[OptimizationResult] = None
     try:
-        with LiveOptimizationUI(console, run_id, run_name, steps, dashboard_url, model=model, metric_name=metric) as ui:
+        # Select UI implementation based on output mode
+        if output_mode == "plain":
+            ui_instance = PlainOptimizationUI(run_id, run_name, steps, dashboard_url, model=model, metric_name=metric)
+        else:
+            ui_instance = LiveOptimizationUI(console, run_id, run_name, steps, dashboard_url, model=model, metric_name=metric)
+
+        with ui_instance as ui:
             result = _run_optimization_loop(
                 ui=ui,
                 run_id=run_id,
@@ -618,7 +642,10 @@ def optimize(
 
         # Show resume message if interrupted
         if result.status == "terminated":
-            console.print(f"\n[cyan]To resume this run, use:[/] [bold]weco resume {run_id}[/]\n")
+            if output_mode == "plain":
+                print(f"\nTo resume this run, use: weco resume {run_id}\n", flush=True)
+            else:
+                console.print(f"\n[cyan]To resume this run, use:[/] [bold]weco resume {run_id}[/]\n")
 
         # Offer to apply best solution
         _offer_apply_best_solution(