| name | teacher-trace-curation |
| description | Use weekly. The teacher log captures every non-local Claude call — curate the best ones into training data so the local model learns from your actual work. |
| version | 1.0.0 |
| author | kbot |
| license | MIT |
| metadata | {"kbot":{"tags":["training","fine-tuning","mlx","local-model","dataset"],"related_skills":["autopoiesis-loop","skill-self-authorship"]}} |
Teacher Trace Curation
Every time kbot calls Claude, the prompt + response is written to ~/.kbot/teacher/traces.jsonl. Left alone, this is just a log. Curated, it's the dataset that teaches the local model to answer your questions without touching the API.
Iron Law
ONLY SUCCESSFUL, CORRECTED, AND USER-APPROVED TRACES ENTER THE DATASET.
Failed traces teach the model to fail. Garbage in is not "more data."
The Weekly Ritual
kbot train-self --mode default --max-examples 500 --iters 200 --num-layers 8 — curates + fine-tunes in one pass. The curator runs first, scores traces, and writes ~/.kbot/teacher/dataset-default.jsonl.- Review the top 50 entries in the dataset file. Skim titles + first 200 chars.
- Remove anything you wouldn't want the local model to imitate:
- Responses you corrected mid-session.
- Hallucinated library names or APIs.
- Advice you later decided was wrong.
- Re-run step 1 with the cleaned dataset if you made significant deletions.
- Test:
ollama run kernel-self:<timestamp> on a task from the last week. Compare against the Claude baseline.
For longer cycles of evaluation + retraining, use kbot train-cycle which chains curate → train → evaluate → merge across multiple iterations.
The Quality Signal That Matters Most
Was this answer used without correction? The curator scores partly on: no correction in the next 5 turns, no follow-up question asking for clarification, no user rephrasing. Approved-by-silence is the strongest endorsement.
What You're Actually Building
A local model that answers "how do I deploy this?" using your deploy flow, not Anthropic's generic best practice. Your infrastructure, your naming, your conventions, your past decisions. That's what the local model becomes over weeks.
Anti-Pattern
Training on everything. Larger datasets with noisy data fine-tune worse models than small curated datasets. 200 excellent examples beat 2,000 mediocre ones every time.
Integration
KBOT_TEACHER_LOG=1 in ~/.zshrc keeps the logger always-on.launchd plist at ~/Library/LaunchAgents/com.kernel.kbot-train-self.plist runs the curation + training weekly.- The trained model gets tagged
kernel-self:<timestamp> in Ollama and becomes available to every kbot command via --model kernel-self:latest.
