tinker-ai-skill

---
name: tinker-ai-skill
description: Turn your own coding-agent session transcripts (Claude Code JSONL) into a supervised fine-tuning dataset and train a LoRA on Tinker with the tinker-cookbook. Use when the user wants to fine-tune a model on their agent sessions, build an SFT dataset from transcripts, anonymize or de-identify transcripts, slice long sessions to a context length with compaction context, or train and evaluate on Tinker. Covers collection, cleaning (secrets, PII, paths), chunking, train/val splits, training, sampling, and the pitfalls that cost us runs.
license: MIT
compatibility: Python 3.11+, uv or pip, tinker-cookbook>=0.6.0 (installs tinker, torch, tml-renderers), a Tinker API key, optionally a Weights & Biases API key. Scripts run from the skill directory; dataset steps are offline, training and sampling call the Tinker service and cost money.
metadata:
  author: Migel Tissera
  version: "1.0.0"
  source: https://github.com/drost-ai/tinker-ai-skill
---

# Fine-tune a coding model on your own agent transcripts (Tinker)

You are helping someone turn the sessions they already ran with a coding agent into
training data, and fine-tune an open model on it with [Tinker](https://thinkingmachines.ai/tinker).
This skill distils a pipeline that shipped several public models. Follow the steps
in order; each script is small, offline unless stated, and prints what it did.

All commands below run from this skill's directory (`tinker-ai-skill`). Read `references/` when a step
needs judgement; the scripts' docstrings (`python scripts/<name>.py --help`) explain
every flag.

## Before you start

1. **Keep the transcripts.** Claude Code deletes transcripts after 30 days by default.
   Tell the user to add `"cleanupPeriodDays": 3650` to `~/.claude/settings.json` now.
2. **Keys.** Copy `.env.example` to `.env` and fill in `TINKER_API_KEY`
   (console at tinker.thinkingmachines.ai) and `WANDB_API_KEY` (wandb.ai/authorize).
   Never print, commit, or paste these values anywhere; the scripts read `.env`.
3. **Environment.** `uv venv && uv pip install -r requirements.txt` (or `pip install -r requirements.txt`).
   Python 3.11+. Use that interpreter for every script.
4. **Budget.** Training costs tokens x price. Always run a script's default (offline)
   mode first; only `--train`, `sample_checkpoint.py` and `export_checkpoint.py` touch
   the service. Confirm the estimated cost with the user before launching.
5. **For local serving (steps 8-9).** A built [llama.cpp](https://github.com/ggml-org/llama.cpp)
   checkout recent enough for the model's architecture, disk for the base model plus
   the merged copy (about 2 bytes per parameter each), and RAM for the largest shard.

## The workflow

### Step 1. Collect transcripts into a dataset location

```
python scripts/collect_sessions.py --data-dir ./data            # ~/.claude/projects by default
python scripts/collect_sessions.py --data-dir ./data --project my-repo --since 2026-01-01