intake

intake — collect inputs, scaffold the project

Turn a vague wish ("make this agent better at X") into a runnable project: a filled capevolve.yaml, an adapter ready to implement, and every NEEDED input resolved before any budget is spent. Intake is cheap; an unresolved input found three phases later is a wasted run and a meaningless number.

Ask, never fabricate — the core discipline of this phase

inputs/INPUTS.md classifies every input NEEDED or RECOMMENDED. For each NEEDED input that is not already present, do not proceed:

• Interactive / chat mode — ASK THE USER and wait. Quote all three, they are in INPUTS.md per input: (a) the exact path the input is expected at, (b) the command or option that produces it, (c) the alternatives. Say what breaks without it.
• Non-interactive (cap-evolve run / the orchestrate skill, nobody to ask) — write BLOCKED: <input> — why it is needed — how to provide it into PROJECT.md and exit non-zero. A blocked-but-honest stop is correct; a green run on a guessed input is not.

A fabricated dataset, scorer, trajectories path or gold answer does not unblock the run — it produces a number that measures nothing and hides that fact. A missing tasks file is a question for the user, not a gap for you to paper over.

RECOMMENDED inputs may take their default, but log every default in PROJECT.md with its honesty cost (e.g. "num_trials=1 — single-trial scores, so the significance gate will correctly reject marginal gains"), so the cost is visible at report time.

Step 0 — mine, then inspect, then ask once

1. Mine the conversation first. Anything the user already said is an answer you must not re-ask — "optimize my airline policy on the flight-change tasks" already fixed the capability, the artifact and the task subset. Harvest that, and any correction the user made, before asking anything.
2. Run the miner. python scripts/run.py --base .capevolve --workdir <repo-root> scaffolds and returns discovered — task files, capability artifacts, existing adapters. Reuse what it found; never re-author it.
3. Inspect what discovered leaves open: the entrypoint, how one eval runs, where traces and scores land, candidate metrics, a natural train/val/test split, cost caps. Run gh auth status. Fan subagents out over the benchmark repo (entrypoint, scorer, trace dir, task schema) while the user answers instead of serializing — come prepared, so the user carries as little of the research as possible.
4. Then ask the FEWEST questions, as ONE numbered batch, each with the detected value pre-filled as a default plus a free-text escape — including the ones only a human can answer: which metric gates accept/reject and each shown metric's direction, GitHub mirroring, deterministic vs agent orchestration (plus stop_condition in agent mode), splits, trials, budget. inputs/INPUTS.md → RECOMMENDED is the authority on each key; SKILL.md only fixes when to ask. Define jargon in a clause before using it ("pass^k — how often it succeeds on all k tries"); the user may be a domain expert, not an ML one.
5. Confirm before scaffolding. Echo the resolved spec back as one block — capability, optimizer, algorithm, dataset, splits, budget, every RECOMMENDED input you are defaulting — and get a yes. A misread is cheapest to fix here.