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.

EPISODE 2 (2026-11-03). Both episode-1 batches settled, nothing refused, nothing paused. The plane keeps datasets (profile-pilot has a dataset with totals), so tables work. DISCOVER RESULT: 14 items, two merges (merge-01 296 rows, merge-02 229 rows), 1165 raw rows, about 400 distinct names after dedup. Both merges are JSON {"companies":[...]} despite the .md name. Quality: good breadth (China about 170 names, USA about 45, Japan 36, Korea 21, Europe about 55, India/MEA about 40, LatAm/Oceania/SE Asia about 30). Weak spots: 8 of 14 items had flagged sources (dead or unverifiable URLs; USA 2, Canada/LatAm 1, Japan 3, Europe-west 5 failed sources per the merge note), many LatAm/Africa/East-Europe entries are hobby, student or Unitree-reseller projects, and the trackers item used humanoid.guide heavily. Discovery is therefore good enough to profile from, with a small targeted second pass (discover-2) for gaps. CANONICAL LIST RULE I APPLIED: a company is profiled when any discover row says include=yes, or it is borderline but has a full-size own-design body publicly shown (kept: Realbotix, Awakening Health, Rice Robotics, SMERT, Robopreneur, Doozy, AXIBO, Chengdu Humanoid Robot Innovation Center, Shanghai Humanoid Robotics Innovation Center (OpenLoong), Li-Gong, Wuxi Longsheng, Simplexity, pi4 Robotics, Aldebaran, SoftBank Robotics, ATOM (Japan), Unlimited Robotics, Wonik Robotics, Almond, Beyond Imagination, IntBot). EXCLUDED and why, so the write-up can list them: under 60 cm or desktop (EZ-Robot, Hiwonder, Kondo Kagaku, FUJI SOFT PALRO, Sony QRIO, ROBO-HI, Figurobot, Tinbot, SIMA, NUWA Kebbi, Furhat, isento pib, Avishkaar); no legs or base, upper body only or stationary (Synria, AheadForm, Red Rabbit RX1, HIRO, Kokoro, Toshiba, Promobot, Macco, Boardwalk is kept since marked yes); one arm or no head (Diligent Moxi, Dexterity, AgileX, Ati, CynLr, Nachi-Fujikoshi, Keigan, NAVER AMBIDEX, RoboForce, Roboligent, Droid/OpenDroids, Svaya); exoskeleton (Angel Robotics); another maker's body or no own body (Hyundai = Boston Dynamics row, Samsung = Rainbow investor, Huawei, Lenovo, China Mobile, Tianqi, Lingyi ODM, Meta unshown, Hugging Face HopeJR via Pollen row, Kind Humanoid acquired by 1X, Vandri, DiusAI, Aristo, YS Lootah, Tavo robotas, donut robotics, BSG, Foxconn is kept as Hon Hai); announced or planned only (Doosan, HIKROBOT, SDIC, Yijiahe, CSSC, TCL, POLCERO, REVEL, LD Robots, Ljubicic, Syntheva, NLX, DOF, HUN, MAMULA, MAXimuz, SunDynamics, xSpecies, KHONDROID, Biorobotec, B drive, Bioztronics, Octopy, Roomie, Robotics Creators Bolivia, QX Alpha, KenBot, ZeroBionic, Big Hero, iCare, Robomiracle, H-Bots, Humanoid Robotics India); historical or retired (TOSY, Hanool, Hitachi EMIEW, MHI wakamaru, Fujitsu HOAP, Flowered, tmsuk consortium, Man-Machine Synergy, Twolabs, Eurobotics locomotion only, MAJU EDGAR, Sefidanis, Si Robotics, ROBOT Russia, Qihan Sanbot, DX Intech, AnyWit, HARIBIT, TetraBOT, Maum AI, ROBOCARE, Wyron, Flexiv, StarBot, ROBO, Robot.com). Name merges: Galaxea = Galaxea Dynamics; KinetixAI = Kinetix AI; Kepler Robotics = Kepler Exploration Robot; Hubei Jingchu = JCBOT; Sichuan TLIBOT = Tlibot; Yunmu = YMBot; Huashu Robot is the Huazhong CNC robot unit; X-Humanoid = Beijing Humanoid Robot Innovation Center; OneRobotics is SwitchBot's Onero H1 brand; Qingbao Robot = Qingbao Engine Robotics; Kawada Robotics covers Kawada Technologies; Haier Smart Home = Haier; AeiROBOT = AEI Robot; Menlo Research = Menlo; Chery (Mojia/Moyin) and AiMOGA (Mornine) are kept as two rows. PILOT LESSONS (5 rows, totals funding_usd 2276700000, units_shipped 6771) and the mends in the profiles-a brief: (1) Unitree row used Wikipedia for founded_source and put only the B2 round in funding_usd while calling it a public company; brief now bans Wikipedia/aggregators and defines listed-company funding = pre-IPO rounds + IPO gross + placements. (2) UBTech left funding_usd empty despite an IPO and placements; same fix. (3) Agility left out the press-reported 2025 round because the company never confirmed it; brief now counts reputable-press rounds marked press-reported and tells the worker to find another source when a page is blocked. (4) Unitree units_shipped 5500 and UBTech 921 are period figures; brief now asks cumulative first, else latest full-year/half-year, labelled in units_shipped_note, never orders or capacity. (5) Figure counted a $100M self-financed seed and an over-$1B Series C as $1B: acceptable lower bound, brief states the 'over X counts as X' rule. (6) Company key must match my canonical name exactly (pilot wrote UBTech/Unitree); brief says write it exactly so. The 5 pilot companies are re-run inside profiles-a as its canary so the whole table comes from one brief; the pilot dataset stays as a cross-check. SPAWNED THIS EPISODE: profiles-a, 275 research items (one per company, vars company + hint), table with 21 columns (company key; hq_country; website; status; what_it_builds; what_it_builds_source; founded_year int; founded_source; funding_usd decimal USD; funding_basis; funding_source; funding_note; latest_funding_date date; valuation_usd decimal USD; latest_model; latest_model_year int; latest_model_source; units_shipped int; units_shipped_source; units_shipped_note; as_of date), totals funding_usd and units_shipped, maxRows 1, json 12000 bytes, merge groupSize 20 so 14 merges. Optional columns: founded_year, founded_source, funding_usd, funding_source, latest_funding_date, valuation_usd, latest_model_year, units_shipped and its source/note. Canary = Figure AI, Agility Robotics, Unitree Robotics, 1X Technologies, UBTECH Robotics. discover-2, 5 items (europe-northwest, usa-seed-2025-2026, china-groups-2, oceania-mideast, directories-crosscheck), same JSON shape as discover, 60 companies max per item. Reads: all profiles-a merges and the 5 discover-2 items. Wake when both settle or after 300 minutes. Budget after this episode: 19 + 275 + 5 items used of 1000. EPISODE 3 SHOULD: (a) If profiles-a paused in canary, read failures: if the table rejected rows for types (e.g. founded_year sent as a string, dates not ISO), restate types in the brief and re-run with itemsFrom both; if sources were flagged, tighten the source rule; otherwise resume. (b) Read the merges and goal.json failures; re-run failed items once via {"batch":"profiles-a","items":"failed"} under a brief mended for the commonest reason, as profiles-a-rerun. (c) From discover-2, build the list of genuinely new companies (not in profiles-a, not in the excluded list above) and spawn profiles-b with the identical table (column names and types must match exactly so datasets can be unioned). (d) Spawn verify-top: a table batch with replicas {"size":3} for the ~30 largest funding_usd rows plus Tesla, Boston Dynamics, Xiaomi, XPENG, Toyota, Honda, LG (division rows with no funding): columns company, funding_usd, valuation_usd, units_shipped, latest_model, latest_model_year, each with a source URL; the ranking rests on these. (e) Spawn signals (markdown, ~25 items) for ranking evidence outside the table: named paying customers and deployments, production capacity and 2026 output, headcount, autonomy vs teleoperation, notable demos, each with a URL. (f) Then finish, target episode 5-7: deliverable = one table keyed by company (union of profiles-a, profiles-b and re-runs, with the dataset totals quoted exactly), the ranking with criteria stated (funding, units shipped, latest model maturity, customers, manufacturing scale), and the gaps: companies with no disclosed funding, disputed replica cells, excluded borderline names.