refactor: replace orchestrator/verifier chain with direct LiteLLM calls

Drop the three-layer Claude subprocess orchestration (local model →
Claude verifier → cloud escalation). Skills now call LiteLLM directly
and return plain text to Claude Code, which decides what to do with it.

- Delete executor, orchestrator, verifier, result, attempts packages
- Simplify LiteLLMExecutor: Run(Request)→Result becomes Complete(model,sys,user)→(string,int64,error)
- Replace ExecutorFn with CompleteFunc in all 6 skill configs
- Rewrite all skill handlers to call Complete and return {"text","model","duration_ms"}
- Simplify config/models: remove Verifier/LlamaSwapURL, add ModelFor
- Bump version to v0.5.0

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

internal/skills/trainer/skill.go

@@ -5,21 +5,20 @@ import (
 	"context"
 	"encoding/json"
 
-	iexec "github.com/mathiasbq/supervisor/internal/exec"
 	"github.com/mathiasbq/supervisor/internal/registry"
 )
 
-// ExecutorFn is the function signature for running a worker subprocess.
-type ExecutorFn func(ctx context.Context, req iexec.Request) (iexec.Result, error)
+// CompleteFunc is the function used to call a local model.
+type CompleteFunc func(ctx context.Context, model, system, user string) (string, int64, error)
 
 // Config holds dependencies for the trainer skill.
 type Config struct {
 	ReaderPrompt string
 	WriterPrompt string
 	DefaultModel string
-	ExecutorFn   ExecutorFn
+	CompleteFunc CompleteFunc
 	SessionsDir  string
-	BrainDir     string // root of brain/ directory; writer writes to BrainDir/training-data/
+	BrainDir     string // root of brain/ directory
 }
 
 // Skill implements the trainer MCP tool.
@@ -40,7 +39,7 @@ func (s *Skill) Tools() []registry.ToolDef {
 	return []registry.ToolDef{
 		{
 			Name:        "trainer",
-			Description: "Extract SFT and DPO training pairs from a session log. Runs a reader→writer chain: reader identifies learning moments, writer formats and writes pairs to brain/training-data/.",
+			Description: "Consult a local model to identify learning moments from a session log and suggest knowledge to preserve in the brain.",
 			InputSchema: schema(
 				[]string{"session_id"},
 				map[string]any{