Sbir

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,504679 across 16931759 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16931759 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,504679 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨54 unchanged words⟩ FRED observations — params:{series_id:"UNRATE"}), "patent_grant" (new USPTO grants for an applicant), "clinical_trial" (new/updated ClinicalTrials.gov studies), "form_d" (new SEC Form D fundraise notices — params:{state?, industry?, min_amount?, exemption?}, all optional), "news_keyword" (GDELT keyword news alerts, metadata+link only — params:{query, lang?, domains?}), "job_post" (new public job-board postings — params:{ats?, board_token?} or {domain?}, plus optional title_query/location_query), "tool_diff" (re-runs any read-only tool on a schedule and fires on change — params:{tool, args?, fields[], schedule}), "new_entity" (new business registrations from a state business-entity registry — params:{state:"CO", name_query?, entity_type?}). Delivery channels: feed (always on —  ⟨29 unchanged words⟩ 

 ⟨183 unchanged words⟩ \"PHASE3\"} (sponsor or condition required). form_d: {state?:\"CA\", industry?:\"Biotechnology\", min_amount?:500000, exemption?:\"06B\"} (all optional — omit all to watch every new Form D nationwide). news_keyword: {query:\"\\\"semiconductor sanctions\\\"\", lang?:\"eng\", domains?:[\"reuters.com\"]} (query required). job_post: EITHER {ats:\"greenhouse\", board_token:\"acme\"} (exact board) OR {domain:\"acme.com\"} (auto-detected), plus optional title_query/location_query substrings. tool_diff: {tool:\"form5500\", args?:{...}, fields:[\"headcount\"], schedule:\"daily\"} — re-runs that tool on schedule and fires when the named fields change; tool must be read-only and not need a Pipeworx-held platform key; schedule \"hourly\" needs the paid tier. new_entity: {state:\"CO\", name_query?:\"dental\", entity_type?:\"limited liability\"} — state is required and must be one of FL, NY, CO, OR, CT, TX, PA. Unknown keys in form_d/news_keyword/job_post/tool_diff/new_entity params are rejected at subscribe time.","type":"object"},"type":{"description" ⟨4 unchanged words⟩ ","polymarket_edge","fred_series","patent_grant","clinical_trial","form_d","news_keyword","job_post","tool_diff","new_entity"],"type":"string"}},"required" ⟨4 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,453504 across 16821693 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16821693 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,453504 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Get"How SBIR/STTRmany SBIR awards did DOE make last year, and how much total funding?" / "SBIR award countscount byand agencydollar total for NASA in 2024" — per-agency SBIR/STTR TOTALS, live from www.sbir.gov/awards.www.sbir.gov/awards (the same source sbir_search_awards uses). Specify agency`agency` (e.g., "DOD", "NASA", "NSFDOE") orand, omitoptionally, `year` to seescope allthe majorcount agencies.— Unlike`year` is the closedCALENDAR APIyear thissbir.gov's replaces,own sbir.gov's"Award award-searchYear" field assigns to the award (verified against sbir.gov/awards' own year[YYYY] filter and each award's detail page, statesnot ana federal fiscal year). `award_count` is always exact: totalsbir.gov/awards (states a precise "Showing 1-10X-Y of N results") total, never an "at least" estimate. When `agency` is given, this also returns `funding_total`: the dollar sum of award amounts across the matched awards. sbir.gov/awards has no funding-total aggregate of its own — the dollar amount lives only on each award's own detail page, one fetch per award — so funding_total sums at most the first 25 matching awards and sets `funding_total_complete: true` only when that covers every match; otherwise `funding_total_complete: false` and `funding_note` says how many of how many were actually summed (narrow with sbir_search_awards' company/state/keyword filters to bring a large match count hereunder is25 for an exact sum — never antrust "ata least"`false` estimate.total as the real figure). Omit `agency` to see counts only (no funding_total — aggregating funding across all major agencies in one call would mean hundreds of detail-page fetches) across the major agencies (DOD, HHS, NASA, NSF, DOE, USDA) at once.

{"examples":[{"agency":"NASA"},{"agency":"DOE","year":2025},{}],"properties":{"agency":{"description": ⟨9 unchanged words⟩ \"NSF\". Omit to cover all major agencies.agencies (count-only, no funding_total).","type":"string"},"year":{"description":"Filter by award year (e.g., 2025) — sbir.gov's own calendar-year \"Award Year\" field, same as sbir_search_awards' `year` filter. Omit for an all-time total.","type":"number"}},"type":"object"}

{"properties":{"agency":{"description":"Canonical agency code the search was narrowed to; null when none was given","type":["string","null"]},"count":{"description":"Number of solicitations returned ⟨2 unchanged words⟩ },"keyword":{"description":"Search keyword usedused; null when the call was not narrowed by keyword","type":["string","null"]},"open_only":{"description":"Whether only  ⟨10 unchanged words⟩ :{"description":"Sponsoring agency","type":["string","null"]},"branch":{"description":"Agency branch" ⟨45 unchanged words⟩ "type":["string","null"]},"source":{"description":"Which live source this row came from","enum":["sbir.gov","dodsbirsttr.mil","grants.gov"],"type":"string"},"topic_number":{"description":"Topic number identifier", ⟨24 unchanged words⟩ ,"type":"object"},"type":"array"},"sources_used":{"description":"How many rows each live source contributed before the limit was applied","properties":{"dodsbirsttr.mil":{"type":"number"},"grants.gov":{"type":"number"},"sbir.gov":{"type":"number"}},"type":"object"}},"required":["keyword","open_only" ⟨4 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,432453 across 16801682 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16801682 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,432453 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,431432 across 1680 sources, fills arguments, fetches the ⟨87 unchanged words⟩ 

 ⟨69 unchanged words⟩ routes each to the right one of 6,431432 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨462 unchanged words⟩ (zero releases across every requested category) returns errorthe SAME shape as a populated one — release_count:0, releases:[] — plus empty_reason:"no_releases_in_window" withand a widen-the-window`hint` hinttelling ratheryou thanto anwiden, emptyso arrayyou —never econbranch on the response shape and never have to guess whether zero means "nothing is scheduled" or "the lookup failed". Econ releases especially cluster on specific dates each month ⟨9 unchanged words⟩ 

 ⟨7 unchanged words⟩ :"string"},"type":"array"},"empty_reason":{"description":"Present ONLY on a zero-release window: \"no_releases_in_window\". The window was queried and nothing is scheduled — this is an answer, not a failure.","type":"string"},"hint":{"description":"Present ONLY on a zero-release window: what to change (usually a wider `hours`).","type":"string"},"notes":{"items":{"type":"string" ⟨117 unchanged words⟩ 

Get SBIR/STTR award counts by agency.agency, live from www.sbir.gov/awards. Specify agency (e.g., "DOD", "NASA", "NSF") or omit to see all major agencies. Unlike the closed API this replaces, sbir.gov's award-search page states an exact total ("Showing 1-10 of N results"), so every count here is exact — never an "at least" estimate.

{"examples":[{"agency":"DOWNASA"},{}],"properties":{"agency":{"description":"Specific agency to count, e.g. \"DOW\DOD\" (formerly DOD), \"NASA\", \"NSF\". Omit to cover all major agencies. Note SBIR publishes no total-count field, so a result at the page cap reports `award_count_at_least` rather than a total.","type":"string"}},"type":"object"}

Get complete SBIR/STTR award history for a company.company from www.sbir.gov/awards. Returns all awards with amounts, agencies, topics, and funding phases.

{"examples":[{"company":"LunaRYDBERG","limit":505}],"properties":{"company":{"description":"Company name to search for (substring match).","type":"string"},"limit":{"description":"Number of results to return (default 50, max 100).","type":"number"}},"required": ⟨3 unchanged words⟩ 

 ⟨10 unchanged words⟩  contract number or agency tracking number (the `award_id`/`contract``contract`/`agency_tracking_number` fields returned by sbir_search_awards). SBIRsbir.gov has no lookup-by-id endpoint, so this searches www.sbir.gov/awards by that identifier as a keyword and verifies thea identifiercandidate's own contract/tracking number matches before returning it — start from sbir_search_awards rather than guessing an id.

{"examples":[{"award_id":"W911NF-22-C-00332537984"}],"properties":{"award_id":{"description":"Contract number or agency tracking number, exactly as returned in `award_id``contract` or `contract``agency_tracking_number` by sbir_search_awards.","type":"string"}}, ⟨4 unchanged words⟩ 

 ⟨75 unchanged words⟩  abstract, fiscal year, and Phase (I/II/III), live from www.sbir.gov/awards (219,000+ awards, 1983-present). This is the tool for who WON  ⟨15 unchanged words⟩ 

{"examples":[{"agency":"DOW","keyword":"machineliquid learningbiopsy","limit":20,"year":20245},{"keyword":"renewablequantum energysensing","limit":50,"state":"CA"5}],"properties":{"agency":{"description":"Filter by funding agency: DOW (Department of War, formerly DOD — \"DOD\" is accepted and translated), HHS, NASA, NSF, DOE, USDA, EPA, DOC, ED, DOT, DHS.DHS, NIH, DARPA, ARMY, NAVY, USAF, and others sbir.gov itself lists. \"DOW\" is also accepted and translated to \"DOD\" — sbir.gov's own form still uses \"DOD\".","type":"string"},"company":{"description":"Filter by company name (substring match against sbir.gov's own company_name filter — a partial name like \"Rydberg\" matches \"RYDBERG TECHNOLOGIES INC\").","type":"string"},"keyword":{"description":"Search term to match against award titles, abstracts, and topicsabstracts.","type":"string"},"limit":{ ⟨3 unchanged words⟩ results to return (default 20, max 100). Award amounts and other detail-page fields are only fetched for the first 25 rows (a bounded number of extra HTTP calls); rows beyond that carry list-level fields only and `enriched: false`.","type":"number"},"state":{ ⟨7 unchanged words⟩  (e.g., \"CA\", \"MA\") — sent as sbir.gov's own state filter.","type":"string"},"year":{ ⟨12 unchanged words⟩ 

 ⟨17 unchanged words⟩  SBIR solicitations" / "SBIR proposal deadlines" / "SBIR opportunities for [research area]" — active SBIR/STTR funding opportunities andmerged from three live sources: www.sbir.gov/topics (multi-agency topic solicitationslisting), fromthe DoD/DARPA topics-app JSON API (Pre-Release and Open DoD topics sbir.gov itself does not yet carry), and Grants.gov (NIH/HHS SBIR & STTR Notices of Funding Opportunity, which never appear as sbir.gov "topics" at all — e.g. the federalNIH parent SBIR/STTR announcements and NCI omnibus solicitations). Every result names its own `program` (SBIR programor STTR) and `source`, byand keyworda Grants.gov hit is only ever labelled SBIR/STTR when its own title or NIH activity code says so — a plain, unrelated grant is never presented as SBIR. Filter by keyword, agency (e.g., "DOD", "DARPA", "NSF", "NASA", "DOE", "HHS"). Returns topic descriptions, sponsoring agency/branch, Phase"NIH"), and open/closeopen/closed dates.status. This is the tool for open federal small-business ⟨12 unchanged words⟩ 

{"examples":[{"agency":"NSFNIH","keyword":"artificialcancer intelligence","open_onlyliquid biopsy diagnostic":true},{"keywordagency":"biotechnology","limitARMY":10}],"properties":{"agency":{"description":"Filter by agency: DOW (Department of War, formerly DOD — \"DOD\" is accepted and translated), HHS, NASA, NSF, DOE, USDA, EPA, DOC, ED, DOT, DHS.DHS, NIH, DARPA, ARMY, NAVY, USAF, and others sbir.gov itself lists. \"DOW\" is also accepted and translated to \"DOD\".","type":"string"},"keyword":{"description":"Search term to match against solicitationsolicitation/topic topicstitles and descriptionsdescriptions.","type":"string"},"limit":{"description":"Number of results to return across all sources combined 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,402431 across 16741680 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16741680 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,402431 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,350402 tools, same arguments, same response shape)  ⟨68 unchanged words⟩ 

 ⟨6 unchanged words⟩  US unemployment rate?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.", ⟨25 unchanged words⟩  Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"}," ⟨11 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,350402 across 16591674 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨5 unchanged words⟩  fiscal 2023 revenue?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.", ⟨23 unchanged words⟩  Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"}," ⟨11 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16591674 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,350402 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨17 unchanged words⟩  the past year?"}],"properties":{"ask":{"description":"Alias for question.","type":"string"},"depth":{"description":"How many facets to ⟨43 unchanged words⟩ ,"thorough"],"type":"string"},"input":{"description":"Alias for question.","type":"string"},"message":{"description":"Alias for question.","type":"string"},"prompt":{"description":"Alias for question.","type":"string"},"q":{"description":"Alias for question.","type":"string"},"query":{"description":"Alias for question.","type":"string"},"question":{"description":"The research question, in natural language. Broad/multi-part is fine — decomposition is the point. Accepts query, q, prompt, text, input, ask, message as aliases.","type":"string"},"text":{"description":"Alias for question.","type":"string"}},"required": ⟨3 unchanged words⟩ 

{"examples":[{"key":"user_research_topic"}],"properties":{"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key to deletedelete. Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"}},"required": ⟨3 unchanged words⟩ 

 ⟨2 unchanged words⟩ :"user_research_topic"},{}],"properties":{"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key to retrieve (omit to list all keys). Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"}},"required": ⟨2 unchanged words⟩ 

 ⟨3 unchanged words⟩ "value":"AAPL"}],"properties":{"content":{"description":"Alias for value.","type":"string"},"data":{"description":"Alias for value.","type":"string"},"k":{"description":"Alias for key.","type":"string"},"key":{"description":"Memory key (e.g., \"subject_property\", \"target_ticker\", \"user_preference\"). Accepts name, k, label as aliases.","type":"string"},"label":{"description":"Alias for key.","type":"string"},"name":{"description":"Alias for key.","type":"string"},"text":{"description":"Alias for value.","type":"string"},"v":{"description":"Alias for value.","type":"string"},"value":{"description":"Value to store (any text — findings, addresses, preferences, notes). Accepts content, text, data, v as aliases; a non-string value is stored as JSON.","type":"string"}},"required": ⟨4 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,332350 across 16531659 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16531659 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,332350 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,320332 across 16491653 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16491653 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,320332 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,061320 across 15721649 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 15721649 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,061320 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,019061 tools, same arguments, same response shape)  ⟨68 unchanged words⟩ 

{"examples":[{"question":"What is the current US unemployment rate?"}],"properties":{"input":{"description":"Alias  ⟨55 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,019061 across 15671572 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

{"examples":[{"question":"What was Apple's fiscal 2023 revenue?"}],"properties":{"input":{"description":"Alias  ⟨53 unchanged words⟩ 

{"examples":[{"type":"company","values":["AAPL","MSFT"]}],"properties":{"type":{"description":"Entity  ⟨48 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 15671572 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,019061 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

{"examples":[{"depth":"quick","question":"What is the current US unemployment rate and how has it changed over the past year?"}],"properties":{"depth":{"description":"How  ⟨71 unchanged words⟩ 

 ⟨213 unchanged words⟩  real "no data", not a bug. `sources_skipped` is the third state: a leg we deliberately did NOT run, each entry carrying a `reason` token and a plain-English `detail` (the Purple Book is skipped for a filer SEC classifies outside the life-science SIC bands, since it lists only 351(a)/(k) biologics licence holders). Pass a ticker ("AAPL"), zero-padded CIK ⟨46 unchanged words⟩ 

{"examples":[{"type":"company","value":"AAPL"}],"properties":{"type":{"description":"\" ⟨62 unchanged words⟩ 

{"examples":[{"url":"https://pipeworx.io"}],"properties":{"max_links":{"description":"Maximum  ⟨37 unchanged words⟩ 

{"examples":[{"message":"Fleet #2184 smoke test: verifying pipeworx_feedback example call returns non-empty.","type":"other"}],"properties":{"claim_token":{"description":"Read  ⟨144 unchanged words⟩ 

{"examples":[{"window":"7d"}],"properties":{"window":{"description":"24h  ⟨25 unchanged words⟩ 

{"examples":[{"topic":"Fed rate decision"}],"properties":{"event":{"description":"Single-event  ⟨77 unchanged words⟩ 

 ⟨181 unchanged words⟩ daily closes of edge_pp_net (net of default slippage AND Polymarket's own taker fee — see polymarket_edges), not intraday.

{"examples":[{"days":14,"window":"1wk"}],"properties":{"days":{"description":"Lookback  ⟨31 unchanged words⟩ 

 ⟨129 unchanged words⟩  85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (afternet of slippage AND Polymarket's own taker fee — fees_pp_applied itemises the fee component; see fees.ts for the published per-category schedule), kelly_fraction + kelly_fraction_half (capped at 0.25) ⟨111 unchanged words⟩ 

{"examples":[{"limit":5,"window":"1wk"}],"properties":{"category_filter":{"description":"Comma-separated  ⟨85 unchanged words⟩ default 0.5). Edge is evaluated NET of slippage.slippage and Polymarket's own taker fee.","type":"number"},"min_kelly":{ ⟨159 unchanged words⟩  in percentage points per leg (default 0.3)., for bid/ask + thin depth cost that a last-trade price does not show. Subtracted from raw |edge| before ranking and Kelly sizing.sizing, PolymarketON hasTOP OF Polymarket's own taker fee — which is NOT zero trading(rate fees0.04-0.07 asdepending ofon 2024category, butread bid/askoff +each thinmarket's depthown typicallypublished eatsfee 20-50bpschedule; persee trade.fees_pp_applied on every row and fees.ts for the full schedule). Bump slippage for very thin partitions; drop to 0 if you have a smarter fill model.model — the fee still applies regardless.","type":"number"},"window":{ ⟨17 unchanged words⟩ 

 ⟨165 unchanged words⟩  dominant loss mode in real arb-bot P&L). FEES ARE NOT MODELLED HERE: vwap_fill_price/profit_usd are GROSS of Polymarket's own taker fee (rate 0.04-0.07 by category — see polymarket_edges/fees.ts), on top of which this tool prices depth-crossing cost; a thin-margin fill that looks clean here can still be net-negative after the fee.

{"examples":[{"market":"will-the-fed-increase-interest-rates-by-25-bps-after-the-december-2026-meeting-20260729232808636","side":"buy_yes","size_usd":1000}],"properties":{"event":{"description":"Basket  ⟨95 unchanged words⟩ 

 ⟨221 unchanged words⟩ query ran and why that event won). Fleet #2064: when two Polymarket candidates tie on resolution time `polymarket_selected_by` now SAYS so, names every tied slug, names the tie-break that actually decided it (the candidate whose metric_type matches the Kalshi series, else lexicographic slug order), and states whether the winner's metric matches the Kalshi series — it used to assert "picked the soonest-resolving" byte-identically on calls that returned DIFFERENT events, because the tie was settled by upstream fetch arrival order. RESPONSE: each venue's leg-by-leg prices (raw probability  ⟨52 unchanged words⟩  probably not the same question), temporal_mismatch (theythe resolvetwo inevents are about different SUBJECT months — e.g. Kalshi "CPI in October" vs Polymarket "September Inflation"), temporal_alignment_unknown (the resolutionsubject month could not be parsed on one or both sides — NOT the same as confirmed-aligned; check each event's close/strike dateevent yourself), non_equivalent_bet_shapes, no_candidate_pairs, unclassified_legs_excluded, pairing_unverified  ⟨67 unchanged words⟩ ,aligned} tells you whether the two events resolveare inabout the same SUBJECT calendar period, in EITHER mode;mode — this is the period the question is ABOUT (e.g. "September" for a CPI release that settles in October), not necessarily when either side settles; null means it could not be computed (see temporal_alignment_unknown), not that the two sides align. Fleet #2062: this used to compare Polymarket's settlement date against Kalshi's subject month and call a match — fixed to compare subject month to subject month on both sides. FEES: every top_spreads_pp and low_confidence_pairs[] row  ⟨325 unchanged words⟩ 

{"examples":[{"since":"30d","type":"company","value":"AAPL"}],"properties":{"since":{"description":"Window  ⟨62 unchanged words⟩ 

{"examples":[{"key":"target_ticker","value":"AAPL"}],"properties":{"key":{"description":"Memory  ⟨29 unchanged words⟩ 

{"examples":[{"type":"company","value":"AAPL"}],"properties":{"type":{"description":"Entity  ⟨89 unchanged words⟩ 

{"examples":[{"entities":["Pipeworx","Zapier"]}],"properties":{"_apiKey":{"description":"Optional  ⟨97 unchanged words⟩ 

{"examples":[{"package":"left-pad"}],"properties":{"package":{"description":"npm  ⟨35 unchanged words⟩ 

{"examples":[{"query":"supply-chain risk","text":"Apple Inc. reported fiscal 2023 revenue of $383.285 billion, driven by strong iPhone and Services growth. Net income was $96.995 billion. The company faced supply-chain risk in China during the quarter."}],"properties":{"limit":{"description":"Max  ⟨55 unchanged words⟩ 

{"examples":[{"topic":"finance"}],"properties":{"topic":{"description":"Optional  ⟨28 unchanged words⟩ 

{"examples":[{"claim":"Apple's fiscal 2023 revenue was $383 billion"}],"properties":{"claim":{"description":"Natural-language  ⟨67 unchanged words⟩ 

 ⟨8 unchanged words⟩ as ask_pipeworx — picks the right tool from 56,961019 across 15521567 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 15521567 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ , routes each to the right one of 56,961019 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨191 unchanged words⟩  partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires,  ⟨38 unchanged words⟩ 

 ⟨419 unchanged words⟩ temporal_alignment_unknown), not that the two sides align. spread.fees_noteFEES: isevery atop_spreads_pp standingand disclosure:low_confidence_pairs[] Kalshirow chargescarries per-contractedge_pp_gross trading(== fees|spread_pp|), Polymarketfees_pp, doesedge_pp_net, notnet_positive, and thisBOTH toolvenues' doestaker notfees modelitemised Kalshi'sas kalshi_fee_pp and polymarket_fee_pp (plus polymarket_fee_rate, polymarket_fee_category, polymarket_fee_basis). Kalshi leg: fee schedule= —ceil(0.07 every* spread_ppcontracts is* grossP * (1-P) * 100) / 100 dollars per order, notverified aagainst netkalshi.com/docs tradeableand edge.corroborating spread.resolution_source_noteexplainers isas theof second2026-09-12. standingPolymarket disclosureleg: thefee two= venues'shares RESOLUTION× SOURCESrate are× neverp compared,× so(1-p) awith Polymarketrate marketby settlingcategory on(crypto a0.07, Binancesports/economics/culture/weather/other 1-minute0.05, candlefinance/politics/mentions/tech 0.04, geopolitics and aworld Kalshievents eventfee-free), strikingverified onagainst Kalshi'sPolymarket's own indexdocs as of 2026-09-13 and read off each market's published fee parameters rather than inferred. Both amortized at a different100-contract hourreference willsize. Before fleet #1927 the Polymarket leg carried modeled gas only, which made every edge_pp_net here optimistic by up to ~1.75pp; spreads that no longer clear are the correction. Spread-crossing cost is still beNOT shownmodeled sideon the Polymarket leg (no live order book is fetched by this tool). spread.fees_note carries the same disclosure. RESOLUTION EQUIVALENCE (fleet #1909): every top_spreads_pp and low_confidence_pairs[] row now also carries resolution_equivalent ("true"|"false"|"unclear") and, when not "true", resolution_warning naming what differs — computed ONCE per event pair (not per leg) via resolution_audit/resolution_diff off one representative leg from each side, since the settlement mechanism is normally shared across every leg in one event. A non-equivalent or unclear pair is NEVER suppressed, only labelled — partread ofresolution_warning anybefore spread_pptreating mayspread_pp beas a differencereal incross-venue contractdisagreement rather than a difference in opinion.contract. spread.resolution_audit carries the full underlying audit (source/timestamp/timezone/precision/evidence_standard/void_handling for both sides) and spread.resolution_source_note is the standing disclosure explaining the methodology and its "unclear" caveat. Call resolution_audit/resolution_diff directly for a specific pair of legs if you need a non-representative-sample breakdown. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair ⟨40 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 5,957961 across 1552 sources, fills arguments, fetches the ⟨87 unchanged words⟩ 

 ⟨69 unchanged words⟩ routes each to the right one of 5,957961 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩