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Claude Skills by Manoj-11-Dahal
github.com/Manoj-11-Dahal15,860 skills0 installs0 views
- Patternlab Classification Evaluation 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: classification evaluation 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 obse...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 c...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Missing Observations 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 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 crite...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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, ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 e...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 relev...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Missing Observations Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation 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 limitat...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Multiple Comparisons 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 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, a...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Multiple Comparisons Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation 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 bef...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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, on...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 com...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Multiple Comparisons 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 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 unc...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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,...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Multiple Comparisons Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation 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 pe...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 r...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Privacy Constraints 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 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Privacy Constraints Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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,...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Privacy Constraints 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 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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, a...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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. ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 hazar...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Privacy Constraints Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility 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 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, an...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation 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 befo...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility 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 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 comp...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility 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 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 unce...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 m...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility 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 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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, ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Reproducibility Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation 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 per...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 re...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Small Samples 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 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Small Samples Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation 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...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 testabl...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 wit...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Small Samples 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 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 ...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 measurabl...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 permissi...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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, permissi...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Small Samples Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: classification evaluation 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 permissione...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation 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: classification evaluation 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 reproducib...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Source Disagreement 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 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,...Votes: 0GitHub stars: 2
- Patternlab Classification Evaluation Source Disagreement Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: classification evaluation 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 b...Votes: 0GitHub stars: 2