131 件 ・ 関連度順
概要と使いどころ
Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).
日本語の概要は準備中です。原文の説明を表示しています。
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
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Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.
日本語の概要は準備中です。原文の説明を表示しています。
Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Screen a Medicare/Medicaid claims corpus for fraud, waste, and abuse and produce ranked, fully-cited investigation referrals for an SIU / program-integrity team. Use when asked to run a fraud sweep, screen claims for FWA, find billing anomalies, or generate investigation referrals over a claims dataset.
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Connect to a hospital's FHIR R4 server (Epic, Oracle Health/Cerner, MEDITECH, athenahealth, or any SMART-on-FHIR endpoint), pull a patient's clinical data and notes, and extract structured findings. Use when users say "connect to the EHR", "connect to Epic/Cerner", "pull notes for patient X", "what do the last 6 months of notes say about Y", or any task that starts from a live EHR rather than pasted text.
日本語の概要は準備中です。原文の説明を表示しています。
Extract plain text from a document file - PDF, DOCX, XLSX, PPTX, RTF, or plain text/markdown/HTML. Use when a binary document needs to be turned into text, for example a contract PDF or an EHR DocumentReference attachment. Other skills (fhir) invoke scripts/extract.ts directly; the contracts MCP server bundles its own copy (servers/documents/src/extract.mjs) so its bundle stays self-contained — port fixes to both.
日本語の概要は準備中です。原文の説明を表示しています。
Audit applications and infrastructure handling Protected Health Information against HIPAA — Security Rule (administrative, physical, technical safeguards), Privacy Rule, Breach Notification Rule, plus HITECH. Covers ePHI scoping, the 18 HIPAA identifiers, Business Associate Agreement (BAA) chain-of-liability, minimum-necessary standard, and breach notification timing. Use when the user mentions 'HIPAA,' 'HIPAA Security Rule,' 'HIPAA Privacy Rule,' 'PHI,' 'ePHI,' 'protected health information,' 'BAA,' 'business associate agreement,' 'covered entity,' 'business associate,' 'minimum necessary,' 'HIPAA breach,' 'HITECH,' 'healthcare compliance,' 'medical data,' 'patient data,' or audits any system that creates, receives, maintains, or transmits PHI.
日本語の概要は準備中です。原文の説明を表示しています。
Enterprise-grade compliance architecture for SOC 2, HIPAA, GDPR, PCI-DSS. Provides compliance checklists, security controls, audit guidance, and regulatory requirements for serverless and cloud architectures. Activates for compliance, HIPAA, SOC2, SOC 2, GDPR, PCI-DSS, PCI DSS, regulatory, healthcare data, payment card, data protection, audit, security standards, regulated industry, BAA, business associate agreement, DPIA, data protection impact assessment.
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Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).
日本語の概要は準備中です。原文の説明を表示しています。
Guides DPIA for health and medical data processing covering Art. 9(2)(h)-(j) exemptions, HIPAA crosswalk for transatlantic operations, clinical trial data protection under EU CTR 536/2014, and genetic data specifics under Art. 9(1). Activate for healthcare systems, clinical research, health apps, or medical device data. Keywords: health data, DPIA, Art. 9, clinical trial, genetic data, HIPAA, medical records, special category.
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Implements HIPAA Privacy Rule requirements under 45 CFR §164.500-534 for covered entities and business associates. Covers minimum necessary standard, treatment-payment-operations exceptions, directory opt-out, personal representative rules, and authorization requirements. Keywords: HIPAA Privacy Rule, PHI, minimum necessary, TPO, authorization, covered entity.
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Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".
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Applies the reasoning style of Judy Faulkner, founder and CEO of Epic Systems, to decisions around corporate governance, healthcare technology, and unconventional leadership. Use this skill whenever Claude is asked for advice on going public vs. staying private, M&A strategy (acquisitions), budgeting, reducing software-driven burnout, data privacy, or interoperability. Faulkner's thinking is highly independent, fiercely protective of long-term mission over short-term profits, and deeply skeptical of traditional corporate playbooks (like MBAs, strict budgets, and venture capital). Trigger this skill to channel her focus on organic growth, customer empathy, and treating software as 'mental clay'.
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Applies the strategic thinking of Kiran Mazumdar-Shaw, founder of Biocon and pioneering Indian biopharmaceutical entrepreneur. Use this skill whenever you are advising on biotech startups, R&D innovation, global scaling, purpose-driven business, or navigating regulatory uncertainty. Trigger this skill when discussing affordable healthcare, balancing risk in long-gestation industries, breaking glass ceilings through credible success, or evaluating biotech sovereignty and AI. Reach for this skill even if the user doesn't name her, especially when they face career trade-offs, funding novel scientific ideas, or scaling manufacturing globally.
日本語の概要は準備中です。原文の説明を表示しています。
Applies the strategic frameworks and mental models of Anne Wojcicki, co-founder and CEO of 23andMe. Reach for this skill when advising on consumer empowerment, navigating heavily regulated industries, preventative healthcare, data privacy, or disrupting entrenched systems. Use this when the user is dealing with misaligned industry incentives, facing regulatory hurdles, building a consumer-research flywheel, or managing a team through a post-launch 'trough of sorrow'. Apply her principles of 'speaking with data', treating users as partners, and pushing for decade-long systemic change from the outside.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive clinical laboratory data harmonization for multi-source healthcare analytics. Convert between US conventional and SI units, standardize numeric formats, and clean data quality issues. This skill should be used when you need to harmonize lab values from different sources, convert units for clinical analysis, fix formatting inconsistencies (scientific notation, decimal separators, whitespace), or prepare lab panels for research.
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Shared review framework that every domain reviewer (pci, oracle, gov, edtech, healthcare, mlops, etc.) MUST follow. Defines the output artifact (TM-{slug}.md), mandatory sections, severity scale, verdict format, the workflow scaffold (when-invoked, Step-0 read-inputs, HANDOFF), and the "domain heuristic vs generic check" boundary. Eliminates duplication across the ~30 reviewer prompts.
日本語の概要は準備中です。原文の説明を表示しています。
Retirement projection with Monte Carlo simulation logic. Calculates required nest egg via 4% rule and 25x expenses, projected vs needed gap, contribution recommendations, asset allocation by age, Social Security claim optimization, healthcare cost projections, and withdrawal sequence planning. Use when the user says "/finance retirement", "am I on track for retirement", "how much do I need to retire", "Social Security timing", or any retirement question.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill for insurance-claims operations with Stedi whenever the user asks to submit claims, validate claim payloads, check claim status, retrieve/interpret 277CA or 835 responses, troubleshoot payer/provider enrollment requirements, or build deterministic claims workflows. Trigger this skill even if the user does not say "Stedi" explicitly but mentions 837/835/277CA/ERA, payer IDs, claim rejection codes, claim resubmission, COB, or healthcare clearinghouse automation.
日本語の概要は準備中です。原文の説明を表示しています。
Automatically detect and de-identify PII (Personal Identifiable Information) and PHI (Protected Health Information) from clinical/medical text to ensure HIPAA compliance. Trigger when processing medical records, patient data, clinical notes, insurance information, or any healthcare-related text containing potential patient identifiers.
日本語の概要は準備中です。原文の説明を表示しています。
Guide creation of new industry use cases for the Adobe Experience Platform blueprints repository. Use this skill when adding a new use case to an industry vertical (retail, automotive, healthcare, financial services, insurance, media & entertainment, telecommunications, travel & hospitality, B2B), when the user wants to add business scenarios to industry pages, or when updating the use case catalog. Handles the full workflow: gathering use case details, assessing maturity level, generating content, updating the industry overview page and use case catalog, and invoking the use-case-icon-generator skill for icon creation.
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专业的医保申请(Health Insurance)顾问助手，帮助您申请省级医疗保险。当用户询问以下问题时使用：(1) 医疗服务查找 (2) 就医流程指导 (3) 保险申请和使用 (4) 健康咨询 (5) 常见问题解答
日本語の概要は準備中です。原文の説明を表示しています。
Generate Claim, ClaimResponse, and ExplanationOfBenefit (EOB) resources for healthcare billing including professional, institutional, pharmacy (Rx), dental, and vision claims. Use when user mentions claims, EOB, explanation of benefit, billing, reimbursement, adjudication, pharmacy claims, Rx claims, denied claims, copay, deductible, or allowed amount.
日本語の概要は準備中です。原文の説明を表示しています。