tinker-cookbook: Instructions file for Claude Code

AGENTS.md

tinker-cookbook AGENTS.md is an instructions file for Claude Code, Codex, OpenCode from thinking-machines-lab/tinker-cookbook. It costs 3,438 tokens per session, scanned A, original, Apache-2.0.

Project instructions for working on Tinker Cookbook, a code library for training and evaluating language models through the Tinker service.

In plain words
What is it for?
They help agents work with the repository, understand its nested data types, follow its skills for research, debugging, and Inkling models, and respect public-repository practices.
Why use it?
They explain the project’s structure, conventions, data types, and how its training and evaluation work is divided between local code and remote GPU processing.

Instructions file for Claude CodeCodexOpenCode

Written for Claude Code and Codex and OpenCode: Claude Code plugin machinery, but also the file is AGENTS.md. Also seen: mentions Claude Code.

This is thinking-machines-lab/tinker-cookbook's own configuration. It tells Claude Code, Codex and OpenCode how to work on tinker-cookbook itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything tinker-cookbook configures →

About the project

Tinker Cookbook is a collection of libraries and examples for fine-tuning language models through the Tinker training service, which handles distributed training behind an API. It is used by researchers and developers for language-model post-training and experimentation, with examples built around common fine-tuning tasks. The catalogue entries provide agent workflows and integrations for using the cookbook.

thinking-machines-lab/tinker-cookbook · 4,236 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to thinking-machines-lab/tinker-cookbook. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/thinking-machines-lab/tinker-cookbook/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/thinking-machines-lab/tinker-cookbook

Made for: Claude Code, Codex, OpenCode.

Wrote this? Show the measurements

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<a href="https://agentmods.dev/instructions/thinking-machines-lab/tinker-cookbook/agents-md"><img src="https://agentmods.dev/badge/instructions/thinking-machines-lab/tinker-cookbook/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 3,438 This file is loaded in full into every session.
When invoked 3,438 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.03438 $0.03438
Opus 5.5 $0.01375 $0.01375
Sonnet 5.5 $0.00688 $0.00688
Haiku 4.5 $0.00344 $0.00344

Measured today against content hash a7b6f99c2613, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tinker-cookbook AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Tinker Cookbook Agent Guide

Quick reference for agents working on tinker-cookbook. Detailed guidance is in the skills under skills/.

tinker-cookbook is a client library with training and eval code built on the Tinker service (hosted by Thinking Machines Lab) and the Tinker SDK (a separate repo with just the API). You author training/eval loops that run on a CPU machine; Tinker executes the heavy GPU work.

Skills: This repo ships four Claude Code skills in skills/: research (SFT, RL, DPO, distillation, evaluation, model selection, experiment methodology), debug (performance, correctness, renderer, and error triage), inkling (thinking effort, rendering, post-training, and multimodal input for Inkling series models, including Inkling 2 Small), and rollout-ui (rollout content, messages, tool calls, transcript comparisons, and browser validation; metrics stay in W&B). Install via /plugin marketplace add thinking-machines-lab/tinker-cookbook, then install the tinker plugin from Discover (/plugin). Use /tinker:research, /tinker:debug, /tinker:inkling, or /tinker:rollout-ui.

Composing Types

Agents often struggle with the nested type hierarchy.

Core types:

  • Datum = model_input (ModelInput) + loss_fn_inputs (dict of TensorData)
  • ModelInput = list of chunks (EncodedTextChunk, ImageChunk)
  • TensorData = wrapper for numpy/torch arrays with shape info

Helper functions (use these instead of manual construction):

  • datum_from_model_input_weights(model_input, weights, max_length) - SL datum creation (supervised/common.py)
  • conversation_to_datum(messages, renderer, max_length, train_on_what) - Full pipeline (supervised/data.py)
  • renderer.build_supervised_example(messages) - Returns (ModelInput, weights)
  • ModelInput.from_ints(tokens) - Create from token list
  • TensorData.from_numpy(arr) / TensorData.from_torch(tensor) - Wrap arrays

Architecture

Builder pattern: Config objects are chz dataclasses (SupervisedDatasetBuilder, RLDatasetBuilder, EnvGroupBuilder). They expose .build()/__call__() returning runtime objects.

Read the full file on GitHub · 157 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. today Changed · +44 lines · +1,519 tokens per session a7b6f99c2613
  2. 29d ago Changed · +2 lines · +84 tokens per session 3bdfb79295c2
  3. 2mo ago First seen · 111 lines · 1,835 tokens per session scan A 1ccc02bb74c1

Subscribe to this mod's changes

tinker-cookbook AGENTS.md is an instructions file published in the GitHub repository thinking-machines-lab/tinker-cookbook (4,236 stars, last pushed yesterday), licensed Apache-2.0. It adds 3,438 tokens to every session, about $0.0138 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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deepseek-harness AGENTS.mddeepseek-ai/deepseek-harnessInstructions file274k2d agoMITA4,313Codex, OpenCode