Conduct post-training research for LLMs using the Tinker API — replicate paper results, explore new training ideas, run and monitor experiments, and document findings. Use this skill whenever the user wants to do research, replicate experiments from a paper or repo, investigate training hypotheses, run experiment sweeps, explore post-training techniques (SFT, RL, DPO, distillation, etc.), set up training, write training code, choose a model, tune hyperparameters, manage checkpoints, export weights, or analyze training logs — even if they just say "try this idea" or "let's see what happens if...".
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Low-risk findings worth noting
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The required workflow involves searching the web and fetching arXiv/PDF papers or HuggingFace datasets, which can expose the agent to untrusted external text.
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