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>
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@@ -5,20 +5,19 @@ import (
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"context"
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"encoding/json"
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iexec "github.com/mathiasbq/supervisor/internal/exec"
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"github.com/mathiasbq/supervisor/internal/registry"
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)
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// ExecutorFn is the function signature for running a worker subprocess.
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type ExecutorFn func(ctx context.Context, req iexec.Request) (iexec.Result, error)
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// CompleteFunc is the function used to call a local model.
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type CompleteFunc func(ctx context.Context, model, system, user string) (string, int64, error)
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// Config holds dependencies for the spec skill.
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type Config struct {
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SkillPrompt string
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DefaultModel string
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ExecutorFn ExecutorFn
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CompleteFunc CompleteFunc
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SessionsDir string
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IngestBaseURL string // optional: base URL of ingestion server for brain context
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IngestBaseURL string
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}
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// Skill implements the spec MCP tool.
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@@ -40,7 +39,7 @@ func (s *Skill) Tools() []registry.ToolDef {
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return []registry.ToolDef{
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{
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Name: "spec",
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Description: "Generate a structured implementation spec from requirements. Writes the spec to output_path in the project.",
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Description: "Consult a local model to draft a structured implementation spec from requirements. Returns the spec text.",
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InputSchema: schema(
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[]string{"project_root", "requirements"},
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map[string]any{
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