Claude Skills by hollandkevint

github.com/hollandkevint

24 skillsA× 240 installs44 views

Arbitrage Audit DataA3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks "is AI going to replace this?" or "what's our moat?"datarustgoVotes: 0GitHub stars: 3
Dashboards To DecisionsAConvert dashboard requests into decision specifications. The missing layer between "build me a dashboard" and "help me decide." Use when receiving dashboard requests, reviewing analytics backlogs, prioritizing data team work, or when someone asks "what dashboard do you need?" Apply this BEFORE building anything.datagitperformanceVotes: 0GitHub stars: 3
Data Consumer DiscoveryADiscover what internal data consumers actually need. Adapted Mom Test and JTBD for data teams. Use when conducting user research, interviewing stakeholders, gathering consumer requirements, running discovery sessions, or when someone asks "what do they need?" or "how do I figure out what to build?"datapythonrustVotes: 0GitHub stars: 3
Data Model DesignADimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization strategies, or when someone asks "how should I model this data?" or "should I denormalize?" For OMOP CDM patterns specifically, see healthcare-data-domain.databasesgodatabaseVotes: 0GitHub stars: 3
Data Pipeline QualityAAutomated data quality checks for pipelines. Testing pyramids, dbt test patterns, data contracts, circuit breakers, and monitoring. Use when implementing data quality checks, writing dbt tests, defining data contracts, setting up pipeline validation, building automated quality monitoring, or when someone asks "how do I test my data pipeline?" For quality scoring methodology (the 5-dimension rubric), see data-quality-assessment.testingrustsqlVotes: 0GitHub stars: 3
Data Product ThinkingAFirst-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"datarustgoVotes: 0GitHub stars: 3
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Data Product ValidationAScore whether a data product idea is worth building before committing resources. Validation scorecard, experiment design, and go/kill decisions. Use when evaluating feasibility, making go/no-go decisions, validating demand, sizing bets, or when someone asks "is this worth building?" or "should we invest in this?"datagosqlVotes: 0GitHub stars: 3
Data Quality AssessmentASystematic data quality evaluation covering completeness, accuracy, timeliness, consistency, and validity. Use when assessing data pipelines, reviewing data product quality, auditing data sources, defining quality SLAs, building data quality monitors, or when someone asks "is this data trustworthy?" or "how do we measure data quality?"datarustgoVotes: 0GitHub stars: 3
Data StorytellingAData presentation and storytelling for data product operators. Narrative structures, chart selection, headline formulas, and anti-patterns. Use when presenting data to stakeholders, building a data presentation, writing an executive summary of findings, telling a data story, or making a case with data. For stakeholder alignment process, see stakeholder-alignment. For metric definitions, see metrics-definition.datagoperformanceVotes: 0GitHub stars: 3
Data Team Operating ModelATeam structure and operating rhythm for data product teams. Product squads, Shape Up 6-week cycles, handoff contracts, and role assignments by lifecycle stage. Use when organizing a data team, planning data product development cycles, defining roles and responsibilities, improving team handoffs, or when someone asks "how should we structure our data team?" or "why do we keep losing context between discovery and delivery?"businessgoapiVotes: 0GitHub stars: 3
Data Team PositioningAPosition data teams as strategic partners, not order-takers. The organizational "why" behind doing discovery work. Use when discussing team positioning, value exchange, demand shaping, escaping the order-taker trap, or when someone asks "how do we stop being order-takers?" or "how does the data team become strategic?"businessrustgoVotes: 0GitHub stars: 3
Ethical Risk AssessmentAEthical data risk evaluation, bias testing protocols, and governance practices for data products. Use when evaluating ML/AI features for fairness, designing bias testing protocols, planning phased rollouts for high-risk changes, reviewing data governance practices, or when someone asks "could this model be biased?" or "how do we ship AI features responsibly?" For HIPAA-specific guidance, see healthcare-data-domain.securityrustgoVotes: 0GitHub stars: 3
Grill Me DataADecision tree exploration for data product plans. Interview relentlessly about schema decisions, consumer contracts, quality SLAs, and delivery choices. Use when planning a data product, designing a schema, choosing a delivery method, or when someone asks "grill me on this data product" or "what am I missing in this design?"datasqlgitVotes: 0GitHub stars: 3
Healthcare Data DomainAHealthcare data domain context covering FHIR, HL7, OMOP CDM, real-world evidence, and clinical terminology systems. Use when working on clinical data pipelines, EHR integrations, claims data products, HIPAA-governed data, OMOP transformations, or when the conversation involves PHI, ICD-10, SNOMED, CPT, LOINC, or RxNorm. Skip this skill for non-healthcare data products.datagoawsVotes: 0GitHub stars: 3
Metrics DefinitionAPrecise metric definitions for data products. Outcome metric trees, naming conventions, grain specification, and the "what does this number mean?" problem. Use when defining KPIs, writing metric specifications, resolving conflicting metric definitions, building a metrics catalog, or when someone asks "how should we measure success?" or "why don't these numbers match?"datarustgoVotes: 0GitHub stars: 3
Research Synthesis DataAConvert raw discovery notes into structured insights using atomic research methods adapted for data products. Use when synthesizing findings, reviewing evidence, summarizing research, writing problem briefs, or when someone asks "what did we learn?" or "what does the evidence say?"researchrustapiVotes: 0GitHub stars: 3
Stakeholder AlignmentATranslates between technical data teams and business stakeholders. Use when preparing stakeholder updates, translating technical data work for executives, shaping vague business requests into buildable specs, navigating competing priorities across data consumers, or when someone asks "how do I explain this to my VP?" or "the business team wants X but that's not how data works."businessgoapiVotes: 0GitHub stars: 3
AnalystABuild or update an evidence graph for a data analysis by linking the question, definitions, source versions, checks, findings and decisions. Use when an analysis spans multiple skills or someone asks what supports a conclusion, what changed, or what to check next. Does not grant data access or approve release.datatestingVotes: 0GitHub stars: 3
Data Product OperatorAHelp a data leader or data product manager choose the next decision, coordinate delivery and review outcomes across one product or a portfolio. Use to establish or improve a data-product operating rhythm, connect existing skills and preserve decision context. Domain agnostic; does not replace the team's tools or grant execution authority.datagoVotes: 0GitHub stars: 3
Databricks Data Product ReviewAReview a Databricks data product or proposed release using approved metadata and query evidence. Use for Unity Catalog scope, Delta change handling, reconciliation, or a release handoff. Does not provision infrastructure or authorize production writes.datagosqlVotes: 0GitHub stars: 3
Duckdb Data ProfilingAProfile approved CSV or Parquet files with DuckDB and produce reproducible quality and join checks. Use for local exploratory analysis, schema drift, identifier preservation or source reconciliation. Does not authorize sensitive-data downloads or arbitrary SQL execution.datarustsqlVotes: 0GitHub stars: 3
Healthcare Cohort DesignATurn a healthcare population question into a reviewable cohort specification and acceptance cases. Use for claims or EHR cohorts, OMOP studies, quality-reporting denominators or trial-feasibility counts. Does not determine clinical trial eligibility or supply licensed measure logic.datagosqlVotes: 0GitHub stars: 3
Healthcare Data Readiness DebriefADebrief one healthcare data project where preparation took more work than expected. Use a project summary or short interview to identify evidence gaps, trace their effect on the intended use, and produce a practical next-check brief. Works without patient records or database access.datagotestingVotes: 0GitHub stars: 3
Healthcare Data ReconciliationAInvestigate disagreements between healthcare source records and downstream outputs. Use for claims/RCM totals, HL7/FHIR record pulls, mappings, clinical-result routing or patient-operations state. Produce an evidence trace and discriminating checks without assuming a root cause or authorizing data changes.datagogitVotes: 0GitHub stars: 3