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Claude Skills by Manoj-11-Dahal
github.com/Manoj-11-Dahal15,860 skills0 installs0 views
- Patternlab Anomaly Detection Measurement Uncertainty Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection 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 are testab...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 observatio...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Measurement Uncertainty 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 measurement uncertainty. Produce a versioned improvement proposal and handoff packet 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 proposed change ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Measurement Uncertainty 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 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 criteria wi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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, owners...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 explici...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 relevant ha...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Measurement Uncertainty Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection 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 limitations. ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 requirement maps ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 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, reviewabl...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection 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 testable...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a failure-triage record and reversible recovery plan 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 failure is bounded by observations...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a versioned improvement proposal and handoff packet 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 proposed change is...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce an evidence-backed option-comparison matrix 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 options are compared on user-approved criteria with...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a reviewable requirements and decision brief 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 requirements have priorities, evidence, owners, ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a scope-and-baseline record 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 owner, constraints, baseline, and acceptance signal are explicit....Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a scoped risk-and-control review 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 review names relevant haza...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for missing observations. Produce a source-and-claim ledger 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 material claims are linked to current primary sources with dates and limitations. Us...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Missing Observations 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 missing observations. Produce a risk-weighted verification plan with saved evidence 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 each critical requirement maps to...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means the prepared result is reproducible, reviewable, and withi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection for multiple comparisons. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means interfaces, dependencies, constraints, and review points are testable before buil...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means the failure is bounded by observations, one testab...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means the proposed change is compared wi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means options are compared on user-approved criteria with uncertainty...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means requirements have priorities, evidence, owners, and measurab...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means the owner, constraints, baseline, and acceptance signal are explicit. Use permiss...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means the review names relevant hazards, permiss...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for multiple comparisons. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means material claims are linked to current primary sources with dates and limitations. Use permission...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Multiple Comparisons 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 multiple comparisons. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Predeclare comparisons or adjust for multiplicity; distinguish exploratory patterns from confirmatory evidence. Success means each critical requirement maps to a reproduci...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means the prepared result is reproducible, reviewable, and wi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection for privacy constraints. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means interfaces, dependencies, constraints, and review points are testable before b...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means the failure is bounded by observations, one tes...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means the proposed change is compared...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means options are compared on user-approved criteria with uncertai...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means requirements have priorities, evidence, owners, and measu...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means the owner, constraints, baseline, and acceptance signal are explicit. Use perm...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means the review names relevant hazards, perm...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for privacy constraints. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means material claims are linked to current primary sources with dates and limitations. Use permiss...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Privacy Constraints 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 privacy constraints. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Minimize personal data, document lawful/approved access, and report subgroup results only where disclosure is safe. Success means each critical requirement maps to a reprod...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a bounded build or configuration runbook with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means the prepared result is reproducible, reviewable, and within...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection for reproducibility. Produce a staged design blueprint with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means interfaces, dependencies, constraints, and review points are testable before build...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a failure-triage record and reversible recovery plan with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means the failure is bounded by observations, one testabl...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a versioned improvement proposal and handoff packet with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means the proposed change is compared wit...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce an evidence-backed option-comparison matrix with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means options are compared on user-approved criteria with uncertainty ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a reviewable requirements and decision brief with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means requirements have priorities, evidence, owners, and measurabl...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a scope-and-baseline record with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means the owner, constraints, baseline, and acceptance signal are explicit. Use permissi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a scoped risk-and-control review with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means the review names relevant hazards, permissi...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection for reproducibility. Produce a source-and-claim ledger with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means material claims are linked to current primary sources with dates and limitations. Use permissione...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Reproducibility 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 reproducibility. Produce a risk-weighted verification plan with saved evidence with scope, versions, decisions, and evidence for this task-specific gate: Record data version, feature steps, parameters, random state, software version, and reproducible evaluation method. Success means each critical requirement maps to a reproducib...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Small Samples 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 small samples. Produce a bounded build or configuration runbook 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 prepared result is reproducible, reviewable, and within the app...Votes: 0GitHub stars: 2