{"id":41087,"date":"2026-05-21T04:27:11","date_gmt":"2026-05-21T02:27:11","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/130-us-profession-profiles-25-deductively-generated-pain-bundles-structured-json-mit-regenerable\/"},"modified":"2026-05-21T04:27:11","modified_gmt":"2026-05-21T02:27:11","slug":"130-us-profession-profiles-25-deductively-generated-pain-bundles-structured-json-mit-regenerable","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/130-us-profession-profiles-25-deductively-generated-pain-bundles-structured-json-mit-regenerable\/","title":{"rendered":"130 US Profession Profiles + 25 Deductively-generated Pain Bundles &#8211; Structured JSON, MIT, Regenerable"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Open-source dataset of US professions. Two levels:<\/p>\n<p>130 profession profiles in <code>data\/professions\/us\/profiles\/<\/code>. Each is a JSON with 7 sections &#8211; daily routine, regulations, tools, jargon, career levels + fears, community channels, labor market. All sourced from .gov, law.cornell.edu, BLS, and professional associations with source URLs attached to every fact. Built by running 7 targeted WebSearch queries per profession.<\/p>\n<p>25 of those profiles also have generated pain bundles in <code>data\/professions\/us\/pains\/<\/code>. 8-15 inferred recurring pains per profession, each paired with a typed spec for the AI tool that would solve it (calculator with inputs\/outputs\/formula, checklist with steps and statutory refs, document template with variables, reference lookup keys, LLM advisor decision criteria). Generated by feeding the profile to Opus with a deductive system prompt &#8211; no web search at the generation step.<\/p>\n<p>Sample of what comes out, from <code>data\/professions\/us\/pains\/us-lawyers.json<\/code>:<\/p>\n<ul>\n<li>Billable Hours &amp; Fee Calculation (calculator)<\/li>\n<li>Statute of Limitations Lookup (reference)<\/li>\n<li>IOLTA Trust Account Reconciliation (calculator)<\/li>\n<li>Engagement Letter Drafting (template)<\/li>\n<li>Court Filing Deadline Calculator (calculator)<\/li>\n<li>&#8230; 8 more<\/li>\n<\/ul>\n<p>And from <code>data\/professions\/us\/pains\/us-auto-detailers.json<\/code>:<\/p>\n<ul>\n<li>Cost-plus detail job pricing calculator (calculator, includes 2026 IRS mileage rate)<\/li>\n<li>EPA stormwater compliance checklist (checklist, $64,618\/day Clean Water Act exposure)<\/li>\n<li>California Car Wash Act registration + surety bond (checklist, Labor Code \u00a7\u00a7 2050-2067)<\/li>\n<li>Vehicle intake \/ pre-inspection form generator (template)<\/li>\n<li>Quarterly self-employment tax estimator (calculator, 15.3% SE tax)<\/li>\n<li>&#8230; 8 more<\/li>\n<\/ul>\n<p>Each pain entry has: title, problem (2-3 sentences), affected segment, frequency, time_waste_h, money_risk_usd, source SCOPE section, skill_type, and a typed skill_spec matching the type. Schema docs in <code>data\/professions\/us\/_FORMAT.md<\/code>.<\/p>\n<p>Backstory: extending an MIT pain-mining repo I&#8217;d been running (court records based, B2B angle). Court records don&#8217;t have profession-level pain because professionals don&#8217;t litigate their own workflow tedium. Switched to web search for regulatory facts + offline LLM deduction for what&#8217;s painful given those facts.<\/p>\n<p>Honest positioning: discovery dataset, not validated pain register. Pains are inferred from regulation + daily routine, not from real users complaining. Plausible starting points for customer-development interviews, not conclusions.<\/p>\n<p>Both pipeline stages are in <code>prompts\/profession-scan\/<\/code> so the dataset is fully regenerable. Country-aware &#8211; works for any country with adequate online regulatory data.<\/p>\n<p>Repo: <a href=\"https:\/\/github.com\/AyanbekDos\/unfairgaps-os\">https:\/\/github.com\/AyanbekDos\/unfairgaps-os<\/a> Cleanest single file to open: <a href=\"https:\/\/github.com\/AyanbekDos\/unfairgaps-os\/blob\/main\/data\/professions\/us\/pains\/us-auto-detailers.json\">https:\/\/github.com\/AyanbekDos\/unfairgaps-os\/blob\/main\/data\/professions\/us\/pains\/us-auto-detailers.json<\/a><\/p>\n<p>MIT. PRs welcome for the remaining 105 profiles or non-US countries.<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Ogretape\"> \/u\/Ogretape <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1tj67xr\/130_us_profession_profiles_25\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1tj67xr\/130_us_profession_profiles_25\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-41087 jlk' href='javascript:void(0)' data-task='like' data-post_id='41087' data-nonce='a166eaf1dd' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-41087 lc'>0<\/span><\/a><\/div><\/div> <div class='status-41087 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Open-source dataset of US professions. Two levels: 130 profession profiles in data\/professions\/us\/profiles\/. Each is a JSON with&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-41087","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41087","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=41087"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/41087\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=41087"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=41087"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=41087"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}