ce45592730
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>
53 lines
1.5 KiB
Go
53 lines
1.5 KiB
Go
// internal/skills/trainer/skill.go
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package trainer
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import (
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"context"
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"encoding/json"
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"github.com/mathiasbq/supervisor/internal/registry"
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)
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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 trainer skill.
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type Config struct {
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ReaderPrompt string
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WriterPrompt string
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DefaultModel string
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CompleteFunc CompleteFunc
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SessionsDir string
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BrainDir string // root of brain/ directory
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}
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// Skill implements the trainer MCP tool.
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type Skill struct{ cfg Config }
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// New creates a new trainer Skill.
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func New(cfg Config) *Skill { return &Skill{cfg: cfg} }
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// Name returns the skill identifier.
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func (s *Skill) Name() string { return "trainer" }
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// Tools returns the MCP tool definitions for this skill.
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func (s *Skill) Tools() []registry.ToolDef {
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schema := func(required []string, props map[string]any) json.RawMessage {
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b, _ := json.Marshal(map[string]any{"type": "object", "required": required, "properties": props})
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return b
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}
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return []registry.ToolDef{
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{
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Name: "trainer",
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Description: "Consult a local model to identify learning moments from a session log and suggest knowledge to preserve in the brain.",
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InputSchema: schema(
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[]string{"session_id"},
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map[string]any{
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"session_id": map[string]any{"type": "string"},
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"model": map[string]any{"type": "string"},
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},
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),
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},
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}
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}
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