Resource Estimation Analyst
Guide resource estimation workflows, geostatistics, and JORC-compliant classification
What You Can Do
You'll walk through complete resource estimation workflows that translate geological evidence (drill core, assays, samples) into defensible mineral deposit quantifications. This skill guides you through data validation, statistical analysis, variography, block model construction, grade interpolation using kriging or inverse distance methods, uncertainty assessment, and resource classification to JORC, NI-43-101, or CRIRSCO standards. You'll produce reproducible, stakeholder-ready estimates that support engineering decisions and regulatory filings.
Features
check drill hole quality, assay integrity, and spatial distribution before estimation begins
evaluate kriging (ordinary, simple, indicator), inverse distance weighting, and simulation approaches for your deposit type
construct and interpret experimental variograms to define grade continuity and inform block model decisions
determine appropriate cell size, domain boundaries, and search parameters based on data density and deposit geology
implement and compare estimation methods with cross-validation and bias diagnostics
assess estimation variance, classify confidence in predictions, and flag high-risk zones
apply measured/indicated/inferred categories with transparent confidence drivers and regulatory justification
structure estimates and documentation to meet JORC, NI-43-101, or CRIRSCO disclosure requirements
Example Output
Example 1: Block Model Validation Report
- Data quality check: 1,247 drill holes reviewed; 3 holes flagged for assay outliers
- Variogram recommendation: Spherical model with 250m range (E-W) and 180m range (N-S)
- Kriging search: 40-sample maximum, minimum 5 samples, 300m search radius
- Classification result: 45% measured (±15% kriging variance), 38% indicated (±25%), 17% inferred
Example 2: Estimation Troubleshooting Summary
- Issue: 12% positive kriging bias in central block cluster
- Root cause: Clustered drilling near high-grade intersection; declustering weights applied
- Solution: Ordinary kriging with range reduction to 200m; validation: bias reduced to −2%
- Outcome: 250,000 tonnes reclassified from indicated to measured after bias correction
Example 3: Resource Statement (Summary)
- Measured: 5.2M tonnes @ 1.85 g/t Au = 308,000 oz
- Indicated: 3.8M tonnes @ 1.62 g/t Au = 197,000 oz
- Inferred: 1.1M tonnes @ 1.41 g/t Au = 50,000 oz
What's Included
- SKILL.md instruction file: detailed workflow and method guidance
- Data validation checklist: drill hole QA, assay integrity, spatial analysis steps
- Variography worksheet: template for lag interval selection, sill/range fitting, and anisotropy analysis
- Block model design template: cell size, domain definition, and search parameter worksheet
- Grade interpolation comparison table: kriging vs. IDW decision matrix with bias/variance trade-offs
- Resource classification framework: confidence criteria and measured/indicated/inferred decision tree
- Regulatory compliance checklist: JORC/NI-43-101/CRIRSCO documentation requirements
Who It's For
- Mining geologists and resource estimation specialists conducting greenfield or brownfield estimates
- Exploration managers preparing resource statements for board reporting or regulatory filings
- Ore reserve engineers validating block models and grade control strategies
- Mining consultants supporting feasibility studies and technical assessments
- Regulatory and compliance officers preparing JORC/NI-43-101 technical reports
Best For
- Designing and QA-ing block model geometry, interpolation strategy, and search parameters
- Classifying resources into measured/indicated/inferred categories with transparent confidence drivers
- Troubleshooting estimation anomalies (bias, variance, poor kriging performance, outlier impacts)
- Selecting appropriate geostatistical methods (kriging, IDW, simulation) for deposit geology and data density
- Preparing technical reports, management summaries, and regulatory submissions with defensible methodology
You might also like
Structure and validate insurance risk models in minutes, not weeks. Generate regulatory-compliant documentation, compliance frameworks, and model validation reports that underwriters and compliance teams can immediately use. Ensure all risk factors, parameters, and assumptions align with insurance regulations and internal governance standards.
Analyze medical histories and applicant health data to generate compliant underwriting assessments with documented risk stratification. Claude reviews medical records, identifies clinical risk factors, and produces standardized underwriting decisions with complete audit trails for regulatory compliance.
This skill analyzes medical histories, diagnoses, and clinical data to generate risk assessments and coverage recommendations for health insurance underwriting. You can streamline initial reviews, evaluate pre-existing conditions, and document coverage decisions with regulatory compliance checks built in. The skill helps you process medical documentation faster while maintaining consistency and accuracy in your risk evaluation process.
Transform complex commercial applications, loss histories, and operational data into professional risk assessments and coverage recommendations. You'll receive structured underwriting documentation with detailed risk analysis, exposure mapping, and loss pattern evaluation that meet regulatory standards. Streamline your underwriting workflow with data-driven, consistent assessments tailored to each client's unique risk profile.
You can systematically evaluate whether your insurance reserves are sufficient to cover incurred but not reported (IBNR) claims and identify reserve deficiencies. This skill analyzes claims development patterns, projects future claim emergence, and applies actuarial methods to assess reserve adequacy across product lines and underwriting years.
Transform unstructured property observations into comprehensive risk assessments with quantified hazard scores and prioritized control recommendations. Claude analyzes property details, identifies potential hazards across categories, calculates risk levels based on likelihood and impact, and delivers actionable remediation strategies tailored to your property type and use case.
Analyze your insurance portfolio to identify concentration risk across perils, geographies, and underwriting assumptions. Validate catastrophe model outputs against real exposure data and flagged data gaps. Generate concise executive risk summaries that highlight key vulnerabilities and inform underwriting decisions.
This skill helps P&C underwriters evaluate risk profiles, recommend policy actions, and justify underwriting decisions through systematic analysis of applicant data, claims history, and exposure factors. You provide application details and historical information; Claude generates risk scores, compliance-checked recommendations, and documentation ready for underwriting files.