Reader agent scans session logs for SFT/DPO candidates; writer receives reader output and formats+writes training pairs to brain/training-data/. Adds trainer-reader.md and trainer-writer.md discipline prompts. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
54 lines
1.6 KiB
Go
54 lines
1.6 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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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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// 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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ExecutorFn ExecutorFn
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SessionsDir string
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BrainDir string // root of brain/ directory; writer writes to BrainDir/training-data/
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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: "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/.",
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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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