EPISODE 1 (2026-11-03). Fresh goal: 1000 items, 100 workers, 15 episodes, nothing run yet. Plan in four phases: (1) DISCOVER the universe of humanoid-robot companies, (2) PROFILE each company into one table row (the goal's table), (3) VERIFY the figures the ranking stands on, (4) FINISH and write the table plus ranking. DEFINITION used in every brief (keep it stable): a company counts when it designs and builds its own general-purpose humanoid robot (torso, two arms, head, on legs or a wheeled base) meant for sale, deployment or research use, as a product or a publicly shown prototype; automaker/big-tech divisions, listed companies and startups all count. Excluded: component makers (actuators, hands, sensors), software-only/foundation-model firms with no robot of their own, universities/institutes, hobby projects, single arms, exoskeletons, small toy/education humanoids under 60 cm. Doubtful cases are listed with include=borderline and a reason, so the orchestrator decides, not the worker. Canonical name = official English name without legal suffix; Chinese companies by the English name they use themselves. SPAWNED THIS EPISODE (19 items): batch `discover`, 14 research items, json output, merge groupSize 7 (so merges merge-01 and merge-02). One item per region or source type: usa, canada-latam, china-startups, china-corporates, japan, korea, europe-west, europe-other, india-mideast-africa, asia-other-oceania, trackers-lists, automakers-bigtech, funding-news-2025-2026, open-hardware-spinouts. Overlap between items is intended; the merge dedups. Batch `profile-pilot`: 5 table items (figure-ai, agility-robotics, unitree, 1x-technologies, ubtech-robotics) that test the profile brief and the table shape on a US startup, two US/Norway startups, a Chinese private firm and a HK-listed company (public-company funding handling). Table columns: company(key), hq_country, website, status, what_it_builds, what_it_builds_source, founded_year, founded_source, funding_usd (USD, totalled, optional), funding_basis, funding_source, funding_note, latest_funding_date, valuation_usd, latest_model, latest_model_year, latest_model_source, units_shipped (optional), units_shipped_source, units_shipped_note, as_of. Reads requested: every discover item, both merges, all 5 pilot rows. Wake when both batches settle or after 180 minutes. EPISODE 2 SHOULD: (a) Check goal.json for refusals. If `profile-pilot` was refused because the plane does not keep datasets (table unsupported), re-spawn the profile batch as plain json output with the same fields and merge groupSize 20 into a table. If `discover` paused in canary, read its failures and mend the brief (likeliest problems: JSON too big -> raise maxBytes or cut per-item cap; dead source URLs -> demand the worker open each URL). (b) Read discover/merge-01 and merge-02 (and the items if merges are thin), build the canonical company list: include=yes rows plus borderline rows that fit the definition; dedup by name/website. Expect roughly 120-250 companies (China alone has 60+; the Morgan Stanley Humanoid 100 and The Humanoid Hub lists are good cross-checks). (c) Read the 5 pilot rows: check sources are real URLs, funding sums match the listed rounds, units_shipped is only ever a published figure. Mend the brief where the rows show a weakness, then spawn `profiles-a` (new name) with the full company list, one item per company, the same table shape (column names must match exactly so the write-up can union datasets), groupSize ~20, maxRows 1. Keep ~400 items of budget for later verification and gap-filling. (d) If the discovery list is clearly incomplete in one region, spawn a second targeted discover batch (new name, e.g. `discover-2`) alongside. EPISODE 3-5 SHOULD: read profiles failures and disputed cells; re-run failed items with a mended brief via itemsFrom; spawn `verify-top` with replicas {size:3} for the ~30 best-funded companies (funding_usd, valuation_usd, units_shipped, latest_model) since the ranking rests on those; spawn a `signals` batch for the top ~25 collecting ranking evidence not in the table (paid deployments and named customers, production capacity/targets, headcount, notable demos, autonomy claims vs teleoperation) with sources, output markdown, so the ranking has reasons. Then finish (target episode 6-8) and write: one table keyed by company with total funding from the dataset totals, ranking with criteria stated (funding, units shipped/deployed, latest model maturity, customers, manufacturing scale), and a list of what is missing (companies with no disclosed funding, disputed cells). BUDGET NOTE: 19 of 1000 items used after this episode. Do not spend more than ~350 on profiles, ~120 on replicas/verification, ~60 on signals; keep a reserve for re-runs.