[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DAPWFV59X2TGJGE8X5A7QH":3,"skill-same:01M6DAPWFV59X2TGJGE8X5A7QH":90},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":70,"commitSha":71,"skillDirectory":72,"forksCount":14,"repositoryHtmlUrl":73,"repositoryHomepageUrl":6,"ownerHtmlUrl":74,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":75,"install":76,"resources":89},"01M6DAPWFV59X2TGJGE8X5A7QH","tooluniverse-drug-regulatory",null,"Drug regulatory and approval research -- FDA substance registry lookup, drug classification by ATC\u002FEPC\u002FMoA via RxClass, Orange Book generic availability and patent status, DailyMed label parsing (adverse reactions, dosing, contraindications), and clinical trial search. Use when users ask about FDA-approved drugs, drug regulatory status, generic availability, patent expiration, drug class membership, drug labeling, or substance identification.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-drug-regulatory\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"tooluniverse drug regulatory","regulatory","patent","clinical","registry lookup","reactions","fda","use","approval","research","ask","classification","parsing","lookup","search","registry","code","status","2026-11-10 10:12:30",false,"---\nname: tooluniverse-drug-regulatory\ndescription: >\n  Drug regulatory and approval research -- FDA substance registry lookup, drug classification\n  by ATC\u002FEPC\u002FMoA via RxClass, Orange Book generic availability and patent status, DailyMed\n  label parsing (adverse reactions, dosing, contraindications), and clinical trial search.\n  Use when users ask about FDA-approved drugs, drug regulatory status, generic availability,\n  patent expiration, drug class membership, drug labeling, or substance identification.\ntriggers:\n  - keywords: [FDA, Orange Book, generic drug, UNII, RxClass, ATC code, drug class, NDA, ANDA, patent, exclusivity, DailyMed, drug label, adverse reactions, regulatory, approval]\n  - patterns: [\"FDA approved\", \"generic available\", \"patent expiration\", \"drug class\", \"ATC code\", \"Orange Book\", \"DailyMed\", \"drug labeling\", \"UNII\"]\n---\n\n# Drug Regulatory Research\n\n**Regulatory status depends on jurisdiction.** FDA approval does not equal EMA approval — check the specific market the user is asking about. Generic availability depends on BOTH patent expiry AND regulatory approval — a patent may have expired but no ANDA may yet be filed or approved. Exclusivity codes (NCE, ODE, PED) can block generics even after patent expiry; always check `FDA_OrangeBook_get_exclusivity` before concluding a generic can enter. A 505(b)(2) NDA is not a generic — it requires its own clinical data and gets its own exclusivity period.\n\n**LOOK UP DON'T GUESS**: never assume NDA numbers, exclusivity dates, or ATC codes — always call FDAGSRS, Orange Book, and RxClass tools to retrieve current data; regulatory status changes with new approvals and expirations.\n\nRegulatory intelligence for drugs: identify FDA substances, classify drugs by therapeutic\ncategory, check approval and generic status, retrieve label sections, and find clinical trials.\n\n## When to Use\n\n- \"What is the FDA regulatory status of semaglutide?\"\n- \"Is there a generic for Humira?\"\n- \"What ATC class does metformin belong to?\"\n- \"Get adverse reactions from the ibuprofen drug label\"\n- \"When does the patent for Eliquis expire?\"\n- \"List all drugs in the ACE inhibitor class\"\n- \"Find clinical trials for a biosimilar of adalimumab\"\n\n## NOT for (use other skills instead)\n\n- Drug-drug interactions -> Use `tooluniverse-drug-drug-interaction`\n- Pharmacogenomics \u002F dosing by genotype -> Use `tooluniverse-pharmacogenomics`\n- Drug mechanism of action \u002F target binding -> Use `tooluniverse-drug-mechanism-research`\n- Drug repurposing \u002F new indications -> Use `tooluniverse-drug-repurposing`\n\n---\n\n## Workflow Overview\n\n```\nInput (drug name \u002F brand name \u002F UNII)\n  |\n  v\nPhase 1: Substance Identification  -- FDAGSRS_search_substances, FDAGSRS_get_substance\n  |\n  v\nPhase 2: Drug Classification       -- RxClass_get_drug_classes, RxClass_find_classes\n  |\n  v\nPhase 3: Approval & Generic Status -- FDA_OrangeBook_search_drug, FDA_OrangeBook_check_generic_availability\n  |\n  v\nPhase 4: Patent & Exclusivity      -- FDA_OrangeBook_get_patent_info, FDA_OrangeBook_get_exclusivity\n  |\n  v\nPhase 5: Label Parsing             -- DailyMed_parse_adverse_reactions, DailyMed_parse_dosing, etc.\n  |\n  v\nPhase 6: Clinical Trials           -- search_clinical_trials\n  |\n  v\nPhase 7: Pharmacovigilance         -- FAERS_count_reactions_by_drug_event (param: medicinalproduct)\n  |\n  v\nPhase 8: Literature & Approval     -- PubMed_search_articles, OpenFDA_get_approval_history, RxNorm_get_drug_names\n```\n\n> **Supplementary tools** (not in core phases but useful):\n> - `OpenFDA_get_approval_history` — full FDA submission\u002Fapproval history (requires `operation` param)\n> - `FAERS_count_reactions_by_drug_event` — top adverse events by report count (param: `medicinalproduct`, ALL CAPS)\n> - `RxNorm_get_drug_names` — resolve drug to RXCUI and brand names\n> - `drugbank_vocab_search` — DrugBank ID, CAS, UNII lookup\n> - `PubMed_search_articles` — regulatory and clinical literature\n\n---\n\n## Phase 1: Substance Identification (FDAGSRS)\n\n**FDAGSRS_search_substances**: `query` (string REQUIRED -- drug name, UNII, InChIKey, or formula), `substance_class` (string, optional: \"chemical\"\u002F\"protein\"\u002F\"nucleic acid\"\u002F\"polymer\"\u002F\"mixture\"), `limit` (int, 1-50, default 10).\nReturns `{status, data: {substances: [{unii, name, substance_class, status, cross_references: [{type, value}]}]}}`.\n- `cross_references` contains DrugBank IDs, WHO-ATC codes, CAS numbers, CFR citations.\n- Use to get the official UNII identifier before calling `FDAGSRS_get_substance`.\n\n**FDAGSRS_get_substance**: `unii` (string REQUIRED, 10-char FDA UNII code).\nReturns complete substance record including all synonyms, names, structure, and cross-references.\n- Provides definitive list of all registered names (INN, USAN, brand, chemical).\n\n**FDAGSRS_get_structure**: `unii` (string REQUIRED).\nReturns `{status, data: {smiles, formula, inchikey, molfile, molecular_weight, stereochemistry, optical_activity}}`.\n- Only works for chemical substances; returns error for biologics, mixtures, polymers.\n\n```python\n# Full substance lookup workflow\nsearch = tu.tools.FDAGSRS_search_substances(query=\"semaglutide\")\nunii = search[\"data\"][\"substances\"][0][\"unii\"]\nfull = tu.tools.FDAGSRS_get_substance(unii=unii)\n```\n\n---\n\n## Phase 2: Drug Classification (RxClass)\n\n**RxClass_get_drug_classes**: `drug_name` (string, drug name), `rxcui` (string, RxNorm RXCUI -- alternative to drug_name), `rela_source` (string, optional: \"ATC\"\u002F\"FDASPL\"\u002F\"MESH\"\u002F\"VA\"), `limit` (int, default 20).\nReturns `{status, data: {classes: [{class_id, class_name, class_type, rela}]}}`.\n- Returns ALL classification systems unless `rela_source` filters to one.\n- `class_type` values: \"ATC1-4\", \"EPC\" (FDA Established Pharmacologic Class), \"MoA\", \"VA\", \"MESH\".\n- Use to find a drug's ATC code, pharmacological class, mechanism of action label.\n\n**RxClass_find_classes**: `query` (string REQUIRED, keyword e.g., \"beta blocker\"), `class_type` (string, optional: \"ATC1-4\"\u002F\"EPC\"\u002F\"MoA\"), `limit` (int, default 20).\nReturns matching drug classes with class IDs.\n- Use when you need to find a class ID before calling `RxClass_get_class_members`.\n\n**RxClass_get_class_members**: `class_id` (string REQUIRED, e.g., \"M01AE\"), `rela_source` (string, optional: \"ATC\"\u002F\"FDASPL\"), `ttys` (string, optional: \"IN\" for ingredients), `limit` (int, default 50).\nReturns all drug ingredients in the class with RXCUIs and names.\n- `ttys=\"IN\"` restricts to active ingredient-level entries (recommended).\n\n```python\n# Find all proton pump inhibitors\nclasses = tu.tools.RxClass_find_classes(query=\"proton pump inhibitor\", class_type=\"EPC\")\nclass_id = classes[\"data\"][\"classes\"][0][\"class_id\"]\nmembers = tu.tools.RxClass_get_class_members(class_id=class_id, ttys=\"IN\")\n```\n\n---\n\n## Phase 3: Approval & Generic Status (FDA Orange Book)\n\n**FDA_OrangeBook_search_drug**: `brand_name` (string), `generic_name` (string), `application_number` (string), `limit` (int, default 10).\nReturns `{status, data: {products: [{brand_name, generic_name, dosage_form, strength, te_code, application_number, approval_date}]}}`.\n- Use brand name (UPPERCASE) or generic name to find NDA\u002FANDA numbers and approval info.\n- `te_code`: Therapeutic Equivalence code (e.g., \"AB\" = therapeutically equivalent).\n\n**FDA_OrangeBook_check_generic_availability**: `brand_name` (string), `generic_name` (string).\nReturns `{status, data: {reference_listed_drug, generics_available: bool, generics_count, generic_products: [...]}}`.\n- Primary tool for \"is there a generic?\" questions.\n\n**FDA_OrangeBook_get_te_code**: No special params beyond `brand_name`\u002F`application_number`.\nReturns therapeutic equivalence codes for substitutability assessment.\n\n**FDA_OrangeBook_get_approval_history**: `application_number` (string, e.g., \"NDA020402\").\nReturns chronological approval history including supplemental approvals and label changes.\n\n```python\n# Check generic availability\nresult = tu.tools.FDA_OrangeBook_check_generic_availability(brand_name=\"LIPITOR\")\n# result[\"data\"][\"generics_available\"] -> True\n# result[\"data\"][\"generics_count\"] -> N\n```\n\n---\n\n## Phase 4: Patent & Exclusivity\n\n**FDA_OrangeBook_get_patent_info**: `application_number` (string), `brand_name` (string).\nReturns patent information. Note: Full patent numbers and expiration dates require Orange Book data files.\n\n**FDA_OrangeBook_get_exclusivity**: `application_number` (string), `brand_name` (string).\nReturns `{status, data: {exclusivities: [{exclusivity_code, exclusivity_date, description}]}}`.\n- `exclusivity_code` values: \"NCE\" (New Chemical Entity, 5 years), \"ODE\" (Orphan Drug, 7 years), \"PED\" (Pediatric, 6 months), \"NP\" (New Product), \"M\" (new formulation).\n\n---\n\n## Phase 5: Label Parsing (DailyMed)\n\nAll DailyMed parse tools accept either `setid` (SPL Set ID UUID) OR `drug_name` (auto-lookup).\nUsing `drug_name` is recommended when the setid is unknown.\n\n**DailyMed_parse_adverse_reactions**: `setid` or `drug_name`. Returns structured adverse reaction table with frequencies and severity.\n\n**DailyMed_parse_dosing**: `setid` or `drug_name`. Returns dosage and administration section (doses, schedules, renal\u002Fhepatic adjustments).\n\n**DailyMed_parse_contraindications**: `setid` or `drug_name`. Returns contraindications section.\n\n**DailyMed_parse_drug_interactions**: `setid` or `drug_name`. Returns drug-drug interaction section with clinical management guidance.\n\n**DailyMed_parse_clinical_pharmacology**: `setid` or `drug_name`. Returns PK\u002FPD data (Cmax, AUC, half-life, protein binding, metabolism pathway).\n\n**DailyMed_search_spls**: `drug_name` (string), returns SPL Set IDs for that drug. Use to find `setid` when needed explicitly.\n\n```python\n# Parse adverse reactions for apixaban\nae = tu.tools.DailyMed_parse_adverse_reactions(drug_name=\"apixaban\")\n```\n\n---\n\n## Phase 6: Clinical Trials\n\n**search_clinical_trials**: `condition` (string), `intervention` (string), `query_term` (string), `pageSize` (int, alias: `max_results`\u002F`limit`), `overall_status` (array, alias: `status`).\nReturns `{status, data: {studies: [{NCT ID, brief_title, brief_summary, overall_status, phase}], total_count}}`.\n- Use `intervention` for drug name, `condition` for disease.\n- Filter `overall_status=[\"RECRUITING\"]` for active enrollment.\n- `total_count` may be None even when results exist; check `len(studies) > 0`.\n\n```python\n# Find recruiting trials for a biosimilar\ntrials = tu.tools.search_clinical_trials(\n    intervention=\"adalimumab biosimilar\",\n    overall_status=[\"RECRUITING\"],\n    pageSize=10\n)\n```\n\n---\n\n## Example Workflows\n\n### Workflow 1: Full Regulatory Profile for a Drug\n\n```\n1. FDAGSRS_search_substances(query=\"apixaban\")\n   -> UNII, substance class, ATC\u002FDrugBank cross-refs\n\n2. RxClass_get_drug_classes(drug_name=\"apixaban\", rela_source=\"ATC\")\n   -> ATC code B01AF02 (direct factor Xa inhibitor)\n\n3. FDA_OrangeBook_search_drug(brand_name=\"ELIQUIS\")\n   -> NDA206518, approval date, TE code\n\n4. FDA_OrangeBook_check_generic_availability(brand_name=\"ELIQUIS\")\n   -> Generic availability status\n\n5. FDA_OrangeBook_get_exclusivity(brand_name=\"ELIQUIS\")\n   -> Exclusivity codes and expiration dates\n\n6. DailyMed_parse_adverse_reactions(drug_name=\"apixaban\")\n   -> Bleeding rates and other AEs from label\n```\n\n### Workflow 2: List All Drugs in a Therapeutic Class\n\n```\n1. RxClass_find_classes(query=\"ACE inhibitor\", class_type=\"EPC\")\n   -> class_id for \"Angiotensin-Converting Enzyme Inhibitor\"\n\n2. RxClass_get_class_members(class_id=\u003Cid>, ttys=\"IN\")\n   -> All ACE inhibitors (enalapril, lisinopril, ramipril, etc.)\n\n3. For each drug: RxClass_get_drug_classes(drug_name=drug)\n   -> Confirm ATC code and additional classifications\n```\n\n### Workflow 3: Drug Label Review\n\n```\n1. DailyMed_parse_adverse_reactions(drug_name=\"metformin\")\n   -> AE frequencies (GI: lactic acidosis, nausea, diarrhea)\n\n2. DailyMed_parse_contraindications(drug_name=\"metformin\")\n   -> eGFR thresholds, renal impairment contraindications\n\n3. DailyMed_parse_drug_interactions(drug_name=\"metformin\")\n   -> Iodinated contrast, carbonic anhydrase inhibitor interactions\n\n4. DailyMed_parse_clinical_pharmacology(drug_name=\"metformin\")\n   -> Half-life, renal clearance, bioavailability\n```\n\n---\n\n## Common Mistakes\n\n- Orange Book `brand_name` must be UPPERCASE (e.g., `\"LIPITOR\"`)\n- `FDAGSRS_get_substance` requires UNII, not drug name — call `FDAGSRS_search_substances` first\n- `FDAGSRS_get_structure` only works for chemical substances, not biologics\n- `RxClass_get_class_members`: pass `ttys=\"IN\"` to restrict to active ingredients\n- `search_clinical_trials` `overall_status` must be an array: `[\"RECRUITING\"]`\n\n---\n\n## Reasoning Framework\n\n### Interpretation Guidance\n\n**Approval pathways**: A 505(b)(1) NDA is a full new drug application with complete safety\u002Fefficacy data from the sponsor. A 505(b)(2) NDA relies partly on published literature or FDA findings for an already-approved drug (common for reformulations, new routes). An ANDA (Abbreviated NDA) is the generic pathway requiring only bioequivalence to the reference listed drug.\n\n**Orange Book patent and exclusivity**: NCE (New Chemical Entity) exclusivity gives 5 years of data protection. ODE (Orphan Drug Exclusivity) gives 7 years. PED (Pediatric) adds 6 months to existing patents\u002Fexclusivity. A TE code of \"AB\" means the generic is therapeutically equivalent and substitutable. No TE code or \"BX\" means substitutability is not established.\n\n**DailyMed label sections**: The \"Adverse Reactions\" section distinguishes clinical trial rates (controlled) from post-marketing reports (uncontrolled, signal-only). \"Contraindications\" are absolute; \"Warnings and Precautions\" are conditional risks. \"Clinical Pharmacology\" provides PK parameters (Cmax, AUC, half-life) essential for drug interaction and dosing assessment.\n\n### Synthesis Questions\n\nA complete drug regulatory report should answer:\n1. What is the current FDA approval status and pathway (NDA vs ANDA vs 505(b)(2))?\n2. Are generic equivalents available, and what is their therapeutic equivalence rating?\n3. When do key patents and exclusivities expire (or have they already)?\n4. What drug class does this belong to (ATC, EPC, MoA), and what are peer drugs in the class?\n5. What are the most clinically significant adverse reactions and contraindications from the label?\n\n","# Drug Regulatory Research\n\n**Regulatory status depends on jurisdiction.** FDA approval does not equal EMA approval — check the specific market the user is asking about. Generic availability depends on BOTH patent expiry AND regulatory approval — a patent may have expired but no ANDA may yet be filed or approved. Exclusivity codes (NCE, ODE, PED) can block generics even after patent expiry; always check `FDA_OrangeBook_get_exclusivity` before concluding a generic can enter. A 505(b)(2) NDA is not a generic — it requires its own clinical data and gets its own exclusivity period.\n\n**LOOK UP DON'T GUESS**: never assume NDA numbers, exclusivity dates, or ATC codes — always call FDAGSRS, Orange Book, and RxClass tools to retrieve current data; regulatory status changes with new approvals and expirations.\n\nRegulatory intelligence for drugs: identify FDA substances, classify drugs by therapeutic\ncategory, check approval and generic status, retrieve label sections, and find clinical trials.\n\n## When to Use\n\n- \"What is the FDA regulatory status of semaglutide?\"\n- \"Is there a generic for Humira?\"\n- \"What ATC class does metformin belong to?\"\n- \"Get adverse reactions from the ibuprofen drug label\"\n- \"When does the patent for Eliquis expire?\"\n- \"List all drugs in the ACE inhibitor class\"\n- \"Find clinical trials for a biosimilar of adalimumab\"\n\n## NOT for (use other skills instead)\n\n- Drug-drug interactions -> Use `tooluniverse-drug-drug-interaction`\n- Pharmacogenomics \u002F dosing by genotype -> Use `tooluniverse-pharmacogenomics`\n- Drug mechanism of action \u002F target binding -> Use `tooluniverse-drug-mechanism-research`\n- Drug repurposing \u002F new indications -> Use `tooluniverse-drug-repurposing`\n\n---\n\n## Workflow Overview\n\n```\nInput (drug name \u002F brand name \u002F UNII)\n  |\n  v\nPhase 1: Substance Identification  -- FDAGSRS_search_substances, FDAGSRS_get_substance\n  |\n  v\nPhase 2: Drug Classification       -- RxClass_get_drug_classes, RxClass_find_classes\n  |\n  v\nPhase 3: Approval & Generic Status -- FDA_OrangeBook_search_drug, FDA_OrangeBook_check_generic_availability\n  |\n  v\nPhase 4: Patent & Exclusivity      -- FDA_OrangeBook_get_patent_info, FDA_OrangeBook_get_exclusivity\n  |\n  v\nPhase 5: Label Parsing             -- DailyMed_parse_adverse_reactions, DailyMed_parse_dosing, etc.\n  |\n  v\nPhase 6: Clinical Trials           -- search_clinical_trials\n  |\n  v\nPhase 7: Pharmacovigilance         -- FAERS_count_reactions_by_drug_event (param: medicinalproduct)\n  |\n  v\nPhase 8: Literature & Approval     -- PubMed_search_articles, OpenFDA_get_approval_history, RxNorm_get_drug_names\n```\n\n> **Supplementary tools** (not in core phases but useful):\n> - `OpenFDA_get_approval_history` — full FDA submission\u002Fapproval history (requires `operation` param)\n> - `FAERS_count_reactions_by_drug_event` — top adverse events by report count (param: `medicinalproduct`, ALL CAPS)\n> - `RxNorm_get_drug_names` — resolve drug to RXCUI and brand names\n> - `drugbank_vocab_search` — DrugBank ID, CAS, UNII lookup\n> - `PubMed_search_articles` — regulatory and clinical literature\n\n---\n\n## Phase 1: Substance Identification (FDAGSRS)\n\n**FDAGSRS_search_substances**: `query` (string REQUIRED -- drug name, UNII, InChIKey, or formula), `substance_class` (string, optional: \"chemical\"\u002F\"protein\"\u002F\"nucleic acid\"\u002F\"polymer\"\u002F\"mixture\"), `limit` (int, 1-50, default 10).\nReturns `{status, data: {substances: [{unii, name, substance_class, status, cross_references: [{type, value}]}]}}`.\n- `cross_references` contains DrugBank IDs, WHO-ATC codes, CAS numbers, CFR citations.\n- Use to get the official UNII identifier before calling `FDAGSRS_get_substance`.\n\n**FDAGSRS_get_substance**: `unii` (string REQUIRED, 10-char FDA UNII code).\nReturns complete substance record including all synonyms, names, structure, and cross-references.\n- Provides definitive list of all registered names (INN, USAN, brand, chemical).\n\n**FDAGSRS_get_structure**: `unii` (string REQUIRED).\nReturns `{status, data: {smiles, formula, inchikey, molfile, molecular_weight, stereochemistry, optical_activity}}`.\n- Only works for chemical substances; returns error for biologics, mixtures, polymers.\n\n```python\n# Full substance lookup workflow\nsearch = tu.tools.FDAGSRS_search_substances(query=\"semaglutide\")\nunii = search[\"data\"][\"substances\"][0][\"unii\"]\nfull = tu.tools.FDAGSRS_get_substance(unii=unii)\n```\n\n---\n\n## Phase 2: Drug Classification (RxClass)\n\n**RxClass_get_drug_classes**: `drug_name` (string, drug name), `rxcui` (string, RxNorm RXCUI -- alternative to drug_name), `rela_source` (string, optional: \"ATC\"\u002F\"FDASPL\"\u002F\"MESH\"\u002F\"VA\"), `limit` (int, default 20).\nReturns `{status, data: {classes: [{class_id, class_name, class_type, rela}]}}`.\n- Returns ALL classification systems unless `rela_source` filters to one.\n- `class_type` values: \"ATC1-4\", \"EPC\" (FDA Established Pharmacologic Class), \"MoA\", \"VA\", \"MESH\".\n- Use to find a drug's ATC code, pharmacological class, mechanism of action label.\n\n**RxClass_find_classes**: `query` (string REQUIRED, keyword e.g., \"beta blocker\"), `class_type` (string, optional: \"ATC1-4\"\u002F\"EPC\"\u002F\"MoA\"), `limit` (int, default 20).\nReturns matching drug classes with class IDs.\n- Use when you need to find a class ID before calling `RxClass_get_class_members`.\n\n**RxClass_get_class_members**: `class_id` (string REQUIRED, e.g., \"M01AE\"), `rela_source` (string, optional: \"ATC\"\u002F\"FDASPL\"), `ttys` (string, optional: \"IN\" for ingredients), `limit` (int, default 50).\nReturns all drug ingredients in the class with RXCUIs and names.\n- `ttys=\"IN\"` restricts to active ingredient-level entries (recommended).\n\n```python\n# Find all proton pump inhibitors\nclasses = tu.tools.RxClass_find_classes(query=\"proton pump inhibitor\", class_type=\"EPC\")\nclass_id = classes[\"data\"][\"classes\"][0][\"class_id\"]\nmembers = tu.tools.RxClass_get_class_members(class_id=class_id, ttys=\"IN\")\n```\n\n---\n\n## Phase 3: Approval & Generic Status (FDA Orange Book)\n\n**FDA_OrangeBook_search_drug**: `brand_name` (string), `generic_name` (string), `application_number` (string), `limit` (int, default 10).\nReturns `{status, data: {products: [{brand_name, generic_name, dosage_form, strength, te_code, application_number, approval_date}]}}`.\n- Use brand name (UPPERCASE) or generic name to find NDA\u002FANDA numbers and approval info.\n- `te_code`: Therapeutic Equivalence code (e.g., \"AB\" = therapeutically equivalent).\n\n**FDA_OrangeBook_check_generic_availability**: `brand_name` (string), `generic_name` (string).\nReturns `{status, data: {reference_listed_drug, generics_available: bool, generics_count, generic_products: [...]}}`.\n- Primary tool for \"is there a generic?\" questions.\n\n**FDA_OrangeBook_get_te_code**: No special params beyond `brand_name`\u002F`application_number`.\nReturns therapeutic equivalence codes for substitutability assessment.\n\n**FDA_OrangeBook_get_approval_history**: `application_number` (string, e.g., \"NDA020402\").\nReturns chronological approval history including supplemental approvals and label changes.\n\n```python\n# Check generic availability\nresult = tu.tools.FDA_OrangeBook_check_generic_availability(brand_name=\"LIPITOR\")\n# result[\"data\"][\"generics_available\"] -> True\n# result[\"data\"][\"generics_count\"] -> N\n```\n\n---\n\n## Phase 4: Patent & Exclusivity\n\n**FDA_OrangeBook_get_patent_info**: `application_number` (string), `brand_name` (string).\nReturns patent information. Note: Full patent numbers and expiration dates require Orange Book data files.\n\n**FDA_OrangeBook_get_exclusivity**: `application_number` (string), `brand_name` (string).\nReturns `{status, data: {exclusivities: [{exclusivity_code, exclusivity_date, description}]}}`.\n- `exclusivity_code` values: \"NCE\" (New Chemical Entity, 5 years), \"ODE\" (Orphan Drug, 7 years), \"PED\" (Pediatric, 6 months), \"NP\" (New Product), \"M\" (new formulation).\n\n---\n\n## Phase 5: Label Parsing (DailyMed)\n\nAll DailyMed parse tools accept either `setid` (SPL Set ID UUID) OR `drug_name` (auto-lookup).\nUsing `drug_name` is recommended when the setid is unknown.\n\n**DailyMed_parse_adverse_reactions**: `setid` or `drug_name`. Returns structured adverse reaction table with frequencies and severity.\n\n**DailyMed_parse_dosing**: `setid` or `drug_name`. Returns dosage and administration section (doses, schedules, renal\u002Fhepatic adjustments).\n\n**DailyMed_parse_contraindications**: `setid` or `drug_name`. Returns contraindications section.\n\n**DailyMed_parse_drug_interactions**: `setid` or `drug_name`. Returns drug-drug interaction section with clinical management guidance.\n\n**DailyMed_parse_clinical_pharmacology**: `setid` or `drug_name`. Returns PK\u002FPD data (Cmax, AUC, half-life, protein binding, metabolism pathway).\n\n**DailyMed_search_spls**: `drug_name` (string), returns SPL Set IDs for that drug. Use to find `setid` when needed explicitly.\n\n```python\n# Parse adverse reactions for apixaban\nae = tu.tools.DailyMed_parse_adverse_reactions(drug_name=\"apixaban\")\n```\n\n---\n\n## Phase 6: Clinical Trials\n\n**search_clinical_trials**: `condition` (string), `intervention` (string), `query_term` (string), `pageSize` (int, alias: `max_results`\u002F`limit`), `overall_status` (array, alias: `status`).\nReturns `{status, data: {studies: [{NCT ID, brief_title, brief_summary, overall_status, phase}], total_count}}`.\n- Use `intervention` for drug name, `condition` for disease.\n- Filter `overall_status=[\"RECRUITING\"]` for active enrollment.\n- `total_count` may be None even when results exist; check `len(studies) > 0`.\n\n```python\n# Find recruiting trials for a biosimilar\ntrials = tu.tools.search_clinical_trials(\n    intervention=\"adalimumab biosimilar\",\n    overall_status=[\"RECRUITING\"],\n    pageSize=10\n)\n```\n\n---\n\n## Example Workflows\n\n### Workflow 1: Full Regulatory Profile for a Drug\n\n```\n1. FDAGSRS_search_substances(query=\"apixaban\")\n   -> UNII, substance class, ATC\u002FDrugBank cross-refs\n\n2. RxClass_get_drug_classes(drug_name=\"apixaban\", rela_source=\"ATC\")\n   -> ATC code B01AF02 (direct factor Xa inhibitor)\n\n3. FDA_OrangeBook_search_drug(brand_name=\"ELIQUIS\")\n   -> NDA206518, approval date, TE code\n\n4. FDA_OrangeBook_check_generic_availability(brand_name=\"ELIQUIS\")\n   -> Generic availability status\n\n5. FDA_OrangeBook_get_exclusivity(brand_name=\"ELIQUIS\")\n   -> Exclusivity codes and expiration dates\n\n6. DailyMed_parse_adverse_reactions(drug_name=\"apixaban\")\n   -> Bleeding rates and other AEs from label\n```\n\n### Workflow 2: List All Drugs in a Therapeutic Class\n\n```\n1. RxClass_find_classes(query=\"ACE inhibitor\", class_type=\"EPC\")\n   -> class_id for \"Angiotensin-Converting Enzyme Inhibitor\"\n\n2. RxClass_get_class_members(class_id=\u003Cid>, ttys=\"IN\")\n   -> All ACE inhibitors (enalapril, lisinopril, ramipril, etc.)\n\n3. For each drug: RxClass_get_drug_classes(drug_name=drug)\n   -> Confirm ATC code and additional classifications\n```\n\n### Workflow 3: Drug Label Review\n\n```\n1. DailyMed_parse_adverse_reactions(drug_name=\"metformin\")\n   -> AE frequencies (GI: lactic acidosis, nausea, diarrhea)\n\n2. DailyMed_parse_contraindications(drug_name=\"metformin\")\n   -> eGFR thresholds, renal impairment contraindications\n\n3. DailyMed_parse_drug_interactions(drug_name=\"metformin\")\n   -> Iodinated contrast, carbonic anhydrase inhibitor interactions\n\n4. DailyMed_parse_clinical_pharmacology(drug_name=\"metformin\")\n   -> Half-life, renal clearance, bioavailability\n```\n\n---\n\n## Common Mistakes\n\n- Orange Book `brand_name` must be UPPERCASE (e.g., `\"LIPITOR\"`)\n- `FDAGSRS_get_substance` requires UNII, not drug name — call `FDAGSRS_search_substances` first\n- `FDAGSRS_get_structure` only works for chemical substances, not biologics\n- `RxClass_get_class_members`: pass `ttys=\"IN\"` to restrict to active ingredients\n- `search_clinical_trials` `overall_status` must be an array: `[\"RECRUITING\"]`\n\n---\n\n## Reasoning Framework\n\n### Interpretation Guidance\n\n**Approval pathways**: A 505(b)(1) NDA is a full new drug application with complete safety\u002Fefficacy data from the sponsor. A 505(b)(2) NDA relies partly on published literature or FDA findings for an already-approved drug (common for reformulations, new routes). An ANDA (Abbreviated NDA) is the generic pathway requiring only bioequivalence to the reference listed drug.\n\n**Orange Book patent and exclusivity**: NCE (New Chemical Entity) exclusivity gives 5 years of data protection. ODE (Orphan Drug Exclusivity) gives 7 years. PED (Pediatric) adds 6 months to existing patents\u002Fexclusivity. A TE code of \"AB\" means the generic is therapeutically equivalent and substitutable. No TE code or \"BX\" means substitutability is not established.\n\n**DailyMed label sections**: The \"Adverse Reactions\" section distinguishes clinical trial rates (controlled) from post-marketing reports (uncontrolled, signal-only). \"Contraindications\" are absolute; \"Warnings and Precautions\" are conditional risks. \"Clinical Pharmacology\" provides PK parameters (Cmax, AUC, half-life) essential for drug interaction and dosing assessment.\n\n### Synthesis Questions\n\nA complete drug regulatory report should answer:\n1. What is the current FDA approval status and pathway (NDA vs ANDA vs 505(b)(2))?\n2. Are generic equivalents available, and what is their therapeutic equivalence rating?\n3. When do key patents and exclusivities expire (or have they already)?\n4. What drug class does this belong to (ATC, EPC, MoA), and what are peer drugs in the class?\n5. What are the most clinically significant adverse reactions and contraindications from the label?\n\n",{"name":5,"triggers":47,"description":69},[48,63],{"keywords":49},[50,51,52,53,54,55,56,57,58,26,59,60,61,62,25,32],"FDA","Orange Book","generic drug","UNII","RxClass","ATC code","drug class","NDA","ANDA","exclusivity","DailyMed","drug label","adverse reactions",{"patterns":64},[65,66,67,56,55,51,60,68,53],"FDA approved","generic available","patent expiration","drug labeling","Drug regulatory and approval research -- FDA substance registry lookup, drug classification by ATC\u002FEPC\u002FMoA via RxClass, Orange Book generic availability and patent status, DailyMed label parsing (adverse reactions, dosing, contraindications), and clinical trial search. Use when users ask about FDA-approved drugs, drug regulatory status, generic availability, patent expiration, drug class membership, drug labeling, or substance identification.\n","main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Flibrary\u002Fskills\u002Ftooluniverse-drug-regulatory","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":77,"prompt":87,"zipAvailable":88},[78,81,84],{"tool":79,"command":80},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-drug-regulatory",{"tool":82,"command":83},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-drug-regulatory",{"tool":85,"command":86},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill tooluniverse-drug-regulatory","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-drug-regulatory\u002FSKILL.md",true,[],[91,112,141,168,181,197],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":42},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[98,99,100,101,102,103,104,105,106,31,107,108,109,110,111],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":121,"publishedAt":42},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[118],{"categoryNo":119,"categoryName":120,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[122,123,124,125,126,127,128,129,130,131,132,133,134,135,31,136,137,138,139,140],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do","MCP",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":153,"publishedAt":42},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[147,150],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":151,"categoryName":152,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[154,155,156,157,158,159,160,161,162,163,123,164,165,166,167],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":169,"name":41,"nameZh":6,"description":170,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":42},"01M6DF73AQVCBZKEN0YXEX9FXP","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[41,174,175,176,177,178,179,180],"setup","report","project","installation","project setup","issues","state",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":188,"publishedAt":42},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[187],{"categoryNo":119,"categoryName":120,"taxonomyType":21,"taxonomyTypeDesc":22},[183,189,103,190,191,192,31,193,194,195,196],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":198,"name":174,"nameZh":6,"description":199,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":200,"categories":201,"keywords":202,"publishedAt":42},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[174,176,138,203,178,204],"files","interactive"]