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Claude Skills by Manoj-11-Dahal
github.com/Manoj-11-Dahal15,860 skills0 installs0 views
- Patternlab Anomaly Detection Small Samples Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means interfaces, dependencies, constraints, and review points are testable before build. Use pe...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Failure Triage And RecoveryUse when an unexpected result needs diagnosis without widening risk or losing evidence for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means the failure is bounded by observations, one testable hypoth...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Measured Improvement And HandoffUse when a measured result or recurring failure suggests a change worth testing and handing to an owner for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means the proposed change is compared with a base...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Option Comparison MatrixUse when several tools, designs, methods, or workflows could satisfy the same need for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means options are compared on user-approved criteria with uncertainty and trad...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Requirements And Decision BriefUse when a request must be converted into testable constraints and an owned decision for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means requirements have priorities, evidence, owners, and measurable accept...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Scope And Baseline RecordUse when the work needs a verified goal, authorized boundary, and starting state for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means the owner, constraints, baseline, and acceptance signal are explicit. Use permissioned inp...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Security Rights And Safety ReviewUse when the workflow could affect people, systems, confidential data, intellectual property, or physical outcomes for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means the review names relevant hazards, permissions, con...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means material claims are linked to current primary sources with dates and limitations. Use permissioned inputs...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples Verification And Acceptance Test PlanUse when a result must be checked against explicit behavior, safety, or quality conditions for Pattern recognition and applied machine learning: anomaly detection for small samples. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Report sample count, unit of analysis, uncertainty range, and the limits of generalizing from a small sample. Success means each critical requirement maps to a reproducible check...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Build Configuration RunbookUse when an authorized implementation needs repeatable steps and a visible change boundary for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means the prepared result is reproducible, reviewable, and wit...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means interfaces, dependencies, constraints, and review points are testable before bu...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Failure Triage And RecoveryUse when an unexpected result needs diagnosis without widening risk or losing evidence for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means the failure is bounded by observations, one test...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Measured Improvement And HandoffUse when a measured result or recurring failure suggests a change worth testing and handing to an owner for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means the proposed change is compared ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Option Comparison MatrixUse when several tools, designs, methods, or workflows could satisfy the same need for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means options are compared on user-approved criteria with uncertain...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Requirements And Decision BriefUse when a request must be converted into testable constraints and an owned decision for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means requirements have priorities, evidence, owners, and measur...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Scope And Baseline RecordUse when the work needs a verified goal, authorized boundary, and starting state for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means the owner, constraints, baseline, and acceptance signal are explicit. Use permi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Security Rights And Safety ReviewUse when the workflow could affect people, systems, confidential data, intellectual property, or physical outcomes for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means the review names relevant hazards, permi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means material claims are linked to current primary sources with dates and limitations. Use permissi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Source Disagreement Verification And Acceptance Test PlanUse when a result must be checked against explicit behavior, safety, or quality conditions for Pattern recognition and applied machine learning: anomaly detection for source disagreement. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Compare labels, collection procedures, and subgroup composition before interpreting disagreement as a real effect. Success means each critical requirement maps to a reprodu...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Build Configuration RunbookUse when an authorized implementation needs repeatable steps and a visible change boundary for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means the prepared result is reproducible, reviewable, ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means interfaces, dependencies, constraints, and review points are testable be...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Failure Triage And RecoveryUse when an unexpected result needs diagnosis without widening risk or losing evidence for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means the failure is bounded by observations, o...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Measured Improvement And HandoffUse when a measured result or recurring failure suggests a change worth testing and handing to an owner for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means the proposed change is co...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Option Comparison MatrixUse when several tools, designs, methods, or workflows could satisfy the same need for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means options are compared on user-approved criteria with un...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Requirements And Decision BriefUse when a request must be converted into testable constraints and an owned decision for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means requirements have priorities, evidence, owners, and...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Scope And Baseline RecordUse when the work needs a verified goal, authorized boundary, and starting state for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means the owner, constraints, baseline, and acceptance signal are explicit. Us...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Security Rights And Safety ReviewUse when the workflow could affect people, systems, confidential data, intellectual property, or physical outcomes for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means the review names relevant hazards...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means material claims are linked to current primary sources with dates and limitations. Use p...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Dataset Shift Verification And Acceptance Test PlanUse when a result must be checked against explicit behavior, safety, or quality conditions for Pattern recognition and applied machine learning: classification evaluation for dataset shift. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Separate training and evaluation by time/source where appropriate and test for drift against the baseline distribution. Success means each critical requirement maps to a ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Build Configuration RunbookUse when an authorized implementation needs repeatable steps and a visible change boundary for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means the prepared result is reproducible, reviewa...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means interfaces, dependencies, constraints, and review points are testab...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Failure Triage And RecoveryUse when an unexpected result needs diagnosis without widening risk or losing evidence for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means the failure is bounded by observatio...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Measured Improvement And HandoffUse when a measured result or recurring failure suggests a change worth testing and handing to an owner for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means the proposed change ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Option Comparison MatrixUse when several tools, designs, methods, or workflows could satisfy the same need for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means options are compared on user-approved criteria wi...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Requirements And Decision BriefUse when a request must be converted into testable constraints and an owned decision for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means requirements have priorities, evidence, owners...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Scope And Baseline RecordUse when the work needs a verified goal, authorized boundary, and starting state for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means the owner, constraints, baseline, and acceptance signal are explici...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Security Rights And Safety ReviewUse when the workflow could affect people, systems, confidential data, intellectual property, or physical outcomes for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means the review names relevant ha...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means material claims are linked to current primary sources with dates and limitations. ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation External Validation Verification And Acceptance Test PlanUse when a result must be checked against explicit behavior, safety, or quality conditions for Pattern recognition and applied machine learning: classification evaluation for external validation. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Hold out an independent source or sample before evaluation and report performance variability, not just a point score. Success means each critical requirement maps ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Build Configuration RunbookUse when an authorized implementation needs repeatable steps and a visible change boundary for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means the prepared result is reproducible,...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means interfaces, dependencies, constraints, and review points ar...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Failure Triage And RecoveryUse when an unexpected result needs diagnosis without widening risk or losing evidence for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means the failure is bounded by ob...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Option Comparison MatrixUse when several tools, designs, methods, or workflows could satisfy the same need for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means options are compared on user-approved cri...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Requirements And Decision BriefUse when a request must be converted into testable constraints and an owned decision for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means requirements have priorities, evidence...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Scope And Baseline RecordUse when the work needs a verified goal, authorized boundary, and starting state for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means the owner, constraints, baseline, and acceptance signal are...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Security Rights And Safety ReviewUse when the workflow could affect people, systems, confidential data, intellectual property, or physical outcomes for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means the review names rel...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means material claims are linked to current primary sources with dates and limit...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Measurement Uncertainty Verification And Acceptance Test PlanUse when a result must be checked against explicit behavior, safety, or quality conditions for Pattern recognition and applied machine learning: classification evaluation for measurement uncertainty. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Carry sensor, annotation, sampling, and preprocessing error into the interpretation rather than reporting false precision. Success means each critical requireme...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Missing Observations Build Configuration RunbookUse when an authorized implementation needs repeatable steps and a visible change boundary for Pattern recognition and applied machine learning: classification evaluation for missing observations. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Describe missingness by field/group and show sensitivity to treatment; do not impute silently or label absence as a signal. Success means the prepared result is reproducible, r...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Missing Observations Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation for missing observations. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Describe missingness by field/group and show sensitivity to treatment; do not impute silently or label absence as a signal. Success means interfaces, dependencies, constraints, and review points are ...Votes: 0GitHub stars: 2