# Iterate From User > Source the next ML experiment proposal from the user via one of three entry points selected by AskUserQuestion: (a) a scientific article URL the agent must read and synthesize, (b) a resource link or path (GitHub issue / spec file / reference repo), or (c) free-text the user types directly. In every branch, the agent reads the source, synthesizes its understanding of what to implement, and confirms with the user before returning the Proposal block. Hand the confirmed Proposal back to iterate-ml-experiment, which writes it into journal/NNshortname.md and seeks the user's design-note approval. Stops at "Proposal returned, user-confirmed"; never writes a design note, never authors acceptance criteria. TRIGGER when: iterate-ml-experiment is picking a sourcing strategy and the user picks user from the menu; the user volunteers a concrete idea ("I want to try X"); the user pastes or links a scientific article, GitHub issue, spec file, or reference repo and asks us to read it. SKIP when: the user wants to mine the previous report (use iterate-from-skore); the user is asking for a symbol lookup or pipeline mechanics (use the python-api skill); the work is evaluation mechanics on a single report (route to evaluate-ml-pipeline). HOW TO USE: open with an AskUserQuestion for the entry point — article-link / resource-link / free-text. In each branch: gather the source material with the appropriate tool (WebFetch, gh issue view, Read), synthesize what you understand into the three shaping questions, and confirm with the user via plain text (one or two sentences: "you'd like to implement X by changing src/