Nih Reporter

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,431453 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 6,431453 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⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,404431 across 16751680 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16751680 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,404431 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

Search NIH-funded research projects. Filter by free-text query (topic/subject matter — matches title/abstract/terms, NOT the awardee), PI name, organization (the funded company/university/institution — use THIS, not query, for "what grants has <company/university> received"), fiscal year, US state, or NIH  ⟨19 unchanged words⟩ 

 ⟨59 unchanged words⟩ :"number"},"organization":{"description":"InstitutionThe namefunded organization — the company, university, hospital, or institution that RECEIVED the award (e.g., \"Stanford University\"). This is the correct filter whenever the question names a specific organization/company/institution as the funding recipient — use it INSTEAD of `query`, which searches grant CONTENT, not the awardee.","type":"string"},"pi_name":{ ⟨10 unchanged words⟩ },"query":{"description":"Free-text search acrossover titleproject title/abstract/terms — an AND match on the SUBJECT MATTER words you pass (e.g. \"cancer immunotherapy\"), abstractnot a lookup by funding recipient. Do NOT put a company, termsinstitution, or organization name here: the name rarely appears verbatim in grant text, so it instead AND-matches on the name's individual common words and can return thousands of unrelated grants. For \"what NIH grants has <company/university/institution> received\", use `organization` instead.","type":"string"},"state":{ ⟨12 unchanged words⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,402404 across 16741675 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16741675 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,402404 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⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,061332 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 6,061332 across 15721653 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":[{"attribute":"revenue","company":"AAPL","period":{"fiscal_year":2023,"type":"annual"}}],"properties":{"as_of":{"description":"Past  ⟨363 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 15721653 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,061332 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⟩ 

{"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⟩ 

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

{"examples":[{"limit":5,"window":"1wk"}],"properties":{"category_filter":{"description":"Comma-separated  ⟨369 unchanged words⟩ 

{"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⟩ 

{"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⟩ 

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

 ⟨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⟩ 

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

 ⟨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⟩ 

 ⟨89 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.

 ⟨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⟩ 

 ⟨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⟩