Housing Intel

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,402453 across 16741682 sources, fills arguments, fetches the data — ⟨85 unchanged words⟩ 

 ⟨11 unchanged words⟩ ":"string"},"newest_observation_date":{"type":["string","null"]},"oldest_observation_date":{"type":["string","null"]},"release_schedule":{"type":"string"}, ⟨29 unchanged words⟩ ":"number"},"latest_age_days":{"type":["number","null"]},"latest_date":{"type":["string","null"]},"latest_value":{"type":["number","null"]},"matched_name":{"type":"string"},"peak_date":{"type":["string","null"]},"peak_value":{"type":["number","null"]},"requested":{"type":"string"}, ⟨31 unchanged words⟩ 

 ⟨30 unchanged words⟩ on every tier. Grounded multi-source research across Pipeworx's 16741682 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,402453 tools IN PARALLEL, and returns a findings  ⟨216 unchanged words⟩ 

 ⟨28 unchanged words⟩ ":"number"},"case_shiller_index_as_of":{"type":["string","null"]},"estimated_monthly_payment":{"description":"Estimated monthly  ⟨23 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]}},"type":"object"},"type": ⟨3 unchanged words⟩ ":"string"},"newest_observation_date":{"type":["string","null"]},"oldest_age_days":{"type":["number","null"]},"oldest_component":{"type":"string"},"oldest_observation_date":{"type":["string","null"]},"span_days":{"type":"number"}, ⟨41 unchanged words⟩ "Median home price (USD)","type":["number","null"]},"median_home_price_as_of":{"type":["string","null"]},"median_home_price_note":{"type":"string"}, ⟨10 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"series":{"type":"string"}, ⟨9 unchanged words⟩  30-year mortgage rate (percent)","type":["number","null"]},"mortgage_rate_as_of":{"type":["string","null"]},"state_resolved":{"type":"string"}} ⟨2 unchanged words⟩ 

 ⟨8 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨29 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨16 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨16 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨11 unchanged words⟩ "Today's date in YYYY-MM-DD format","type":["string","null"]},"total_nonfarm":{"properties":{"cadence": ⟨4 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨20 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨11 unchanged words⟩ 

 ⟨19 unchanged words⟩ ":"object"},"home_value_as_of":{"type":["string","null"]},"markets":{"items":{"properties": ⟨8 unchanged words⟩ ":"number"},"typical_home_value":{"type":["number","null"]}},"type":"object"},"type": ⟨9 unchanged words⟩ ":"string"},"rent_as_of":{"type":["string","null"]}},"type":"object"}

 ⟨8 unchanged words⟩ ":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨11 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"}, ⟨20 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"}, ⟨5 unchanged words⟩ ":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨22 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]}},"type":"object"},"type": ⟨3 unchanged words⟩ type":[null,"string"]},"type":["array","null"]},"newest_component":{"type":"string"},"newest_observation_date":{"type":["string","null"]},"oldest_age_days":{"type":["number","null"]},"oldest_component":{"type":"string"},"oldest_observation_date":{"type":["string","null"]},"span_days":{"type":"number"}, ⟨16 unchanged words⟩ ":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨12 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"}, ⟨23 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"recent_data":{"items":{"properties":{"date":{"type":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨30 unchanged words⟩ ":"number"},"4wk_ago_date":{"type":["string","null"]},"cadence":{"type":"string"}, ⟨8 unchanged words⟩ ":"number"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"},"trend":{"type":"string"}},"type":["object","null"]},"note":{"description":"Explanation of  ⟨21 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"}, ⟨5 unchanged words⟩ ":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨5 unchanged words⟩ "Today's date in YYYY-MM-DD format","type":["string","null"]},"unemployment":{"properties":{"cadence": ⟨12 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"scope":{"type":"string"}, ⟨5 unchanged words⟩ ":"string"},"value":{"type":["number","null"]}},"type":"object"},"type": ⟨24 unchanged words⟩ ":"ZHVI value in USD","type":["number","null"]}},"type":"object"},"inventory": ⟨9 unchanged words⟩ ":"For-sale inventory (smoothed)","type":["number","null"]}},"type":"object"},"median_sale_price": ⟨10 unchanged words⟩  median sale price in USD","type":["number","null"]}},"type":"object"},"new_listings": ⟨8 unchanged words⟩ "description":"Monthly new listings","type":["number","null"]}},"type":"object"},"region": ⟨19 unchanged words⟩ ":"ZORI value in USD/month","type":["number","null"]}},"type":"object"},"sales_count": ⟨8 unchanged words⟩ "description":"Monthly sales count","type":["number","null"]}},"type":"object"},"scope": ⟨13 unchanged words⟩ 

 ⟨8 unchanged words⟩ :{"properties":{"acs_5yr_release_date":{"type":["string","null"]},"age_days_since_release":{"type":"number"}, ⟨47 unchanged words⟩ 

 ⟨17 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"publishes":{"type":"string"}, ⟨31 unchanged words⟩ ":"null"},"observation_date":{"type":["string","null"]},"points_15y":{"type":"null"}, ⟨21 unchanged words⟩ ":"number"},"from_date":{"type":["string","null"]},"max_30y":{"type":"number"}, ⟨5 unchanged words⟩ ":"number"},"to_date":{"type":["string","null"]},"window":{"type":"string"}} ⟨6 unchanged words⟩ ":"number"},"from_date":{"type":["string","null"]},"from_pct":{"type":"number"}, ⟨5 unchanged words⟩ ":"string"},"to_date":{"type":["string","null"]},"to_pct":{"type":"number"}} ⟨4 unchanged words⟩ 

 ⟨12 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"periodChange":{"type":"number"}, ⟨37 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"periodChange":{"type":"number"}, ⟨45 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"series":{"type":"string"}} ⟨12 unchanged words⟩ "Today's date in YYYY-MM-DD format","type":["string","null"]},"signals":{"description":"Detected anomalies  ⟨16 unchanged words⟩ ":"string"},"observation_date":{"type":["string","null"]},"series":{"type":"string"}, ⟨17 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⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,332402 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 6,332402 across 16531674 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 16531674 STRUCTURED data sources (SEC filings, FRED/BLS economics ⟨23 unchanged words⟩ routes each to the right one of 6,332402 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⟩ 

 ⟨3 unchanged words⟩ "Average hourly earnings for production/nonsup workers (USD)"," (type":" varies; observed both null and non-null)"},"avg_hourly_earnings_as_of":{"typedescription":"(type varies; observed both null and non-null)"},"case_shiller_index":{"type":"number"} ⟨149 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 6,061332 across 15721653 sources, fills arguments, fetches the data — ⟨85 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⟩ 

Beta version of ask_pipeworx: identical universal router (same 6,020061 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 6,020061 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⟩ 

 ⟨100 unchanged words⟩ , San Francisco, Seattle, Tampa, Washington DC. NOT A CURRENT-MARKET READ: Case-Shiller is monthly, published on a roughly two-month lag, and each value is a three-month moving average — so the newest observation is typically 90-120 days old and describes a trailing quarter. The `freshness` block states the exact age of the call you made.

{"properties":{"errorfreshness":{"descriptionproperties":{"Error message if request failedage_days",:{"type":"stringnumber"},"metrosas_of":{"descriptiontype":"Ranked metros from softest to strongeststring"},"itemscadence":{"propertiestype":{"all_time_peakstring"},"newest_observation_date":{"descriptiontype":"Highest value ever recordedstring"},"oldest_observation_date":{"type":"numberstring"},"change_12morelease_schedule":{"descriptiontype":"12-month change in index pointsstring"},"smoothing":{"type":"numberstring"},"change_3mostatement":{"descriptiontype":"3-month change in index pointsstring"}},"type":"numberobject"},"currentmethodology":{"type":"string"},"metros":{"description":"LatestRanked Case-Shillermetros indexfrom valuesoftest to strongest","items":{"properties":{"change_12m_pct":{"type":"number"},"drawdown_from_peakchange_3m_pct":{"descriptiontype":"Percent decline from all-time peaknumber"},"drawdown_from_peak_pct":{"type":"number"},"metrolatest_age_days":{"descriptiontype":"Metro namenumber"},"latest_date":{"type":"string"},"pct_change_12molatest_value":{"descriptiontype":"12-month percent changenumber"},"matched_name":{"type":"numberstring"},"pct_change_3mopeak_date":{"descriptiontype":"3-month percent changestring"},"peak_value":{"type":"number"},"requested":{"type":"string"},"series_id":{"description":"FRED series ID ( ⟨9 unchanged words⟩  change is negative","type":"boolean"},"trend":{"description":"Trend label based on recent values","enum":["rising","falling","stable"],"type":"string"}},"type":"object"},"type":"array"},"snapshot_datesoftest_to_strongest":{"descriptionitems":{"Today's date in YYYY-MM-DD formattype":"string"},"type":"stringarray"}},"type":"object"}

{"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 6,020061 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⟩ 

 ⟨18 unchanged words⟩  Optionally specify metro (e.g., "Denver"). The inputs are official statistics on DIFFERENT cadences — a weekly mortgage rate against a quarterly median price — so the monthly payment is an illustrative ratio of published figures, not a live quote. `freshness` states the span and the age of each input.

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 ⟨12 unchanged words⟩  building employment, unemployment rate, and job openings. All BLS monthly releases; each series carries its own `observation_date` and `cadence` (JOLTS runs an extra month behind CES/LAUS), and `freshness` states the span. `snapshot_date` is the call date, not the data date.

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 ⟨74 unchanged words⟩ = lowest), limit, and optional home-value bounds. rent_as_of and home_value_as_of are two independent Zillow queries and can land on different months; `freshness` says whether they matched this call.

 ⟨23 unchanged words⟩  comparing Case-Shiller across multiple metros use case_shiller_metro_compare instead. THE COMPONENTS DO NOT SHARE A DATE — they are weekly, monthly and quarterly official statistics, each published on its own lag, so one call can span months of vintages. `freshness` states the span in days and names the oldest component; read it before comparing components against each other.

 ⟨13 unchanged words⟩ :"object"},"type":"array"},"cadence":{"type":"string"},"current":{"description":"Latest Case-Shiller index value","type":"number"},"errorobservation_date":{"type":"string"},"scope":{"enumtype":["nationalstring"]},"smoothing":{"type":"string"}},"type":"object"},"construction_employment":{"properties":{"cadence":{"type":"string"},"current":{"description":"Construction employment in thousands ⟨2 unchanged words⟩ ,"error":{"type":"string"},"scopeobservation_date":{"enumtype":["nationalstring"]},"scope":{"type":"string"},"trend":{"items" ⟨9 unchanged words⟩ ,"type":"array"},"unit":{"enumtype":"string"}},"type":"object"},"freshness":{"properties":{"as_of":{"type":"string"},"components":{"items":{"properties":{"age_days":{"type":"number"},"cadence":{"type":"string"},"component":{"type":"string"},"observation_date":{"type":"string"}},"type":"object"},"type":"array"},"components_without_a_date":{"items":{"type":[null,"thousandsstring"]},"type":"array"},"newest_component":{"type":"string"},"newest_observation_date":{"type":"string"},"oldest_age_days":{"type":"number"},"oldest_component":{"type":"string"},"oldest_observation_date":{"type":"string"},"span_days":{"type":"number"},"statement":{"type":"string"}},"type":"object"},"housing_starts":{"properties":{"3mo_trend ⟨13 unchanged words⟩ :"object"},"type":"array"},"cadence":{"type":"string"},"current":{"description":"Latest housing starts in thousands","type":"number"},"errorobservation_date":{"type":"string"},"scope":{"enum":["national"],"type":"string"},"unit":{"enum":["thousands"],"type":"string"}},"type":"object" ⟨7 unchanged words⟩ "string"},"metro_hpi":{"properties":{"currentcadence":{"descriptiontype":"Latest metro HPI valuestring"},"current":{"type":"number"},"errormetro":{"type":"string"},"metroobservation_date":{"description":"Metro name","type":"string"},"recent_data":{"items" ⟨9 unchanged words⟩ ,"type":"array"},"series":{"description":"FRED series ID for metro FHFA HPI","type":"string"},"trend":{"enum":["rising","falling","stable"],"type":"string"},"yoy":{"properties":{"change":{"description":"Year-over-year absolute change","type":"number"},"pctChange":{"description":"Year-over-year percent change","type":"number"}},"type":"object"}},"type":["objectnull","nullobject"]},"mortgage_rate":{"properties":{"4wk_ago ⟨4 unchanged words⟩  weeks ago","type":"number"},"current4wk_ago_date":{"descriptiontype":"Current 30-year mortgage rate (percent)string"},"cadence":{"type":"numberstring"},"errorcurrent":{"description":"ErrorCurrent message30-year ifmortgage datarate unavailable(percent)","type":"stringnumber"},"scopeobservation_date":{"enum":["national"],"type":"string"},"trendscope":{"enumtype":["rising","fallingstring"},"stabletrend"],:{"type":"string"}},"type":"object ⟨9 unchanged words⟩ "string"},"owners_equiv_rent":{"properties":{"cadence":{"type":"string"},"current":{"description":"Owners' equivalent rent index ⟨2 unchanged words⟩ ,"error":{"type":"string"},"scopeobservation_date":{"enumtype":["nationalstring"]},"scope":{"type":"string"},"trend":{"items" ⟨21 unchanged words⟩ "string"},"unemployment":{"properties":{"cadence":{"type":"string"},"current":{"description":"Current unemployment rate ( ⟨3 unchanged words⟩ ,"error":{"type":"string"},"scopeobservation_date":{"enumtype":["nationalstring"]},"scope":{"type":"string"},"trend":{"items" ⟨12 unchanged words⟩ "object"},"zillow":{"properties":{"error":{"description":"Error message if Zillow data unavailable","type":"string"},"home_value_index":{"properties":{"date":{"description ⟨15 unchanged words⟩ ":"ZHVI value in USD","type":["number","null"]}},"type":"object"},"inventory": ⟨9 unchanged words⟩ ":"For-sale inventory (smoothed)","type":["number","null"]}},"type":"object"},"median_sale_price": ⟨10 unchanged words⟩  median sale price in USD","type":["number","null"]}},"type":"object"},"new_listings": ⟨8 unchanged words⟩ "description":"Monthly new listings","type":["number","null"]}},"type":"object"},"region": ⟨19 unchanged words⟩ ":"ZORI value in USD/month","type":["number","null"]}},"type":"object"},"sales_count": ⟨8 unchanged words⟩ "description":"Monthly sales count","type":["number","null"]}},"type":"object"},"scope":{"description":"Whether data is metro-specific or national","enum":["metro","national"],"type":"string"}},"type":" ⟨3 unchanged words⟩ 

 ⟨68 unchanged words⟩  metro is a trap. No API key needed. HIGHEST-LAG SOURCE IN THIS PACK: figures are a Census ACS 5-YEAR rolling average (currently 2018-2022, released Dec 2023) — for structural demand, not a current-quarter read. `freshness` states the exact age.

{"properties":{"as_of_snapshotfreshness":{"properties":{"dateage_days":{"descriptiontype":"Closest observation on or before requested as_of datenumber"},"cadence":{"type":"string"},"rate_30yrcurrent_for_cadence":{"descriptiontype":"30-year fixed mortgage rate (percent)boolean"},"instrument":{"type":["numberstring"},"nullnext_publication_expected"]:{"type":"string"},"observation_date":{"type":"string"},"publishes":{"type":"string"},"statement":{"type":"string"}},"type":"object"},"latestnote":{"propertiestype":"string"},"series":{"dateitems":{"descriptionproperties":{"ISO date of latest observation",:{"type":"string"},"rate_30yrrate_15y_fixed":{"descriptiontype":"30-year fixed mortgage rate (percent)number"},"rate_30y_fixed":{"type":["number"},"rate_5_1_arm":{"type":"null"]}},"type":"object"},"statstype":"array"},"snapshot":{"properties":{"avgas_of_requested":{"descriptiontype":"Average rate in window (percent)null"},"typeobservation_date":[{"numbertype",:"nullstring"]},"maxpoints_15y":{"descriptiontype":"Maximum rate in window (percent)null"},"typepoints_30y":[{"numbertype",:"null"]},"minrate_15y_fixed":{"descriptiontype":"Minimum rate in window (percent)number"},"rate_30y_fixed":{"type":["number"},"rate_5_1_arm":{"type":"null"]}},"type":"object"},"time_seriessource":{"descriptiontype":"Historical mortgage rates in chronological orderstring"},"itemsstats":{"properties":{"dateavg_30y":{"descriptiontype":"ISO date of observationnumber"},"from_date":{"type":"string"},"rate_30yrmax_30y":{"descriptiontype":"30-year fixed mortgage rate (percent)number"},"min_30y":{"type":["number"},"nullobservations"]:{"type":"number"},"to_date":{"type":"string"},"window":{"type":"string"}},"type":"object"},"treasury_10y_since_observation":{"properties":{"change_bp":{"type":"arraynumber"},"windowfrom_date":{"descriptiontype":"Time window requested (1mstring"}, 3m"from_pct":{"type":"number"}, 6m"observations":{"type":"number"}, 1y"series_id":{"type":"string"}, 5y"to_date":{"type":"string"}, all)"to_pct":{"type":"number"}},"type":"stringobject"}},"type":"object"}

 ⟨14 unchanged words⟩  sales, prices, wages, unemployment, and rent. Each signal carries its own `observation_date` and `cadence` — series here span weekly (mortgage rate) to quarterly (median sales price), and Case-Shiller is additionally a 3-month moving average, so "turned negative" does not mean "this month" for every series. `freshness` states the full span and the oldest component.

{"properties":{"all_series":{"items":{"properties":{"cadence":{"type":"string"},"current":{"type":"number"},"label":{"type":"string"},"observation_date":{"type":"string"},"periodChange":{"type":"number"},"series":{"type":"string"},"signal_count":{"type":"number"},"trend":{"type":"string"},"yoy":{"properties":{"change":{"type":"number"},"pctChange":{"type":"number"}},"type":"object"}},"type":"object"},"type":"array"},"flagged_count":{"type":"number"},"flagged_series":{"items":{"properties":{"cadence":{"type":"string"},"current":{"type":"number"},"label":{"type":"string"},"observation_date":{"type":"string"},"periodChange":{"type":"number"},"prior":{"type":"number"},"series":{"type":"string"},"signals":{"items":{"properties":{"message":{"type":"string"},"type":{"type":"string"}},"type":"object"},"type":"array"},"trend":{"type":"string"},"yoy":{"properties":{"change":{"type":"number"},"pctChange":{"type":"number"}},"type":"object"}},"type":"object"},"type":"array"},"freshness":{"properties":{"as_of":{"type":"string"},"oldest_component":{"properties":{"cadence":{"type":"string"},"observation_date":{"type":"string"},"series":{"type":"string"}},"type":"object"},"span_days":{"type":"number"},"statement":{"type":"string"}},"type":"object"},"scan_date":{"description":"Today's date in YYYY-MM-DD ⟨10 unchanged words⟩ housing indicators","items":{"properties":{"indicatorcadence":{"description":"Name of the housing indicator","type":"string"},"message":{"description":"Human-readable signal description","type":"string"},"typeobservation_date":{"descriptiontype":"Type of signal detectedstring"},"enumseries":[{"reversaltype",:"unusualstring"},"acceleratingtype",:{"turndescription",:"extremeType of signal detected"],"type":"string"}},"type":"object"},"type":"array"},"total_series_scanned":{"type":"number"}},"type":"object"}

{"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⟩ 

 ⟨9 unchanged words⟩ ask_pipeworx — picks the right tool from 6,019020 across 1567 sources, fills arguments, fetches the ⟨87 unchanged words⟩ 

 ⟨69 unchanged words⟩ routes each to the right one of 6,019020 tools IN PARALLEL, and returns a findings  ⟨216 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 (fleet #1910): every opportunities[] row and partition_check.arbitrage now carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and net_positivefee_categories[]. —BOTH cost components are modeled: PolymarketPolymarket's gasown costper-category TAKER FEE (bothfee legs,= ~$0.02/legshares × 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 schedule2026-09-13) dateand 2026-09-12;Polygon wheregas the(~$0.02/leg). fill_checkThe belowtaker alreadyfee repricesdominates: against~$1.75 liveper depth100 shares on a crypto market at 50c versus $0.02 of gas, thisso doesrows NOTthat double-countlooked profitable before fleet #1927 may now show net_positive:false — that spreadis costthe correction, onlynot gas.a Knownregression. gap:Each Polymarket'sleg ownis per-categorypriced takerat 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' (rolledread outoff 2026the market, upthe tonormal ~1.8%case), on'category' crypto(mapped atfrom 50/50its fee category), is'fee_free', NOTor yet'fallback' modeled(rate unknown — treatcharged edge_pp_netat the modal 0.05 rather than assumed free, so an unreadable market is never reported as still-optimisticcostless). onWhere fee-bearingfill_check categories.reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, ⟨38 unchanged words⟩ 

 ⟨420 unchanged words⟩ ), not that the two sides align. FEES (fleet #1910): every top_spreads_pp and low_confidence_pairs[] row now carries edge_pp_gross (== |spread_pp|), fees_pp, edge_pp_net, and net_positive, —and Kalshi'sBOTH per-contractvenues' taker fee isfees modeleditemised onas thekalshi_fee_pp Kalshiand legpolymarket_fee_pp (verified against kalshi.com/docs andplus corroboratingpolymarket_fee_rate, explainerspolymarket_fee_category, aspolymarket_fee_basis). ofKalshi 2026-09-12leg: fee = ceil(0.07 * contracts * P * (1-P) * 100) / 100 dollars per order, verified against kalshi.com/docs and corroborating explainers as of 2026-09-12. Polymarket leg: fee = shares × rate × p × (1-p) with rate by category (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 read off each market's published fee parameters rather than inferred. Both amortized at a 100-contract reference size),size. Before fleet #1927 the Polymarket leg carriescarried modeled gas only, (nowhich livemade orderevery bookedge_pp_net here, sooptimistic spread-crossingby up to ~1.75pp; spreads that no longer clear are the correction. Spread-crossing cost is notstill NOT modeled on thatthe Polymarket leg (no live order book is fetched by this tool). spread.fees_note incarries the responsesame disclosure. RESOLUTION EQUIVALENCE (fleet #1909): every top_spreads_pp and low_confidence_pairs[] row now also carries thisresolution_equivalent same("true"|"false"|"unclear") disclosureand, pluswhen thenot known"true", gap:resolution_warning Polymarket'snaming ownwhat 2026differs per-category— takercomputed feeONCE isper event pair (not yetper modeled,leg) sovia edge_pp_netresolution_audit/resolution_diff stilloff understatesone truerepresentative costleg onfrom fee-bearingeach Polymarketside, categories.since spread.resolution_source_notethe settlement mechanism is thenormally secondshared standingacross disclosure:every theleg twoin venues'one RESOLUTIONevent. SOURCESA arenon-equivalent neveror comparedunclear pair is NEVER suppressed, soonly alabelled Polymarket— marketread settlingresolution_warning onbefore treating spread_pp as a Binancereal 1-minutecross-venue candledisagreement andrather than a Kalshidifference eventin strikingcontract. onspread.resolution_audit Kalshi'scarries ownthe indexfull atunderlying aaudit different(source/timestamp/timezone/precision/evidence_standard/void_handling hourfor willboth stillsides) beand shownspread.resolution_source_note sideis bythe sidestanding —disclosure partexplaining ofthe anymethodology spread_ppand mayits be"unclear" caveat. Call resolution_audit/resolution_diff directly for a differencespecific inpair contractof ratherlegs thanif inyou opinion.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⟩