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Claude Skills by Manoj-11-Dahal
github.com/Manoj-11-Dahal15,860 skills0 installs0 views
- Nosql Expert Failure Triage RecordUse when a reproducible NoSQL data modeling symptom needs bounded diagnosis before any corrective change. Produce a failure-triage record for NoSQL data modeling with symptom, environment, evidence, hypothesis, and next safe check. Success means the diagnosis distinguishes observation from inference, each proposed check is reversible and scoped, and unresolved causes remain open. The review is bounded to access-pattern evidence, key design, consistency assumptions, index cost, and hot-key ris...Votes: 0GitHub stars: 2
- Nosql Expert Input Output ContractUse when the accepted inputs or produced outputs for NoSQL data modeling need a checkable boundary. Produce an input/output contract for NoSQL data modeling with types, required fields, exclusions, and representative approved fixtures. Success means the contract names its source and version, boundary cases are visible, and no private or unapproved payload is needed to explain the result. The review is bounded to access-pattern evidence, key design, consistency assumptions, index cost, and hot...Votes: 0GitHub stars: 2
- Nosql Expert Integration Parity TraceUse when NoSQL data modeling exchanges state or data with another component and the handoff needs to be verified. Produce an integration-parity trace for NoSQL data modeling showing source, transformation, destination, and observed result. Success means each material field or state transition has a traceable mapping and observed mismatches are separated from assumptions. The review is bounded to access-pattern evidence, key design, consistency assumptions, index cost, and hot-key risk. Use on...Votes: 0GitHub stars: 2
- Nosql Expert Performance EnvelopeUse when NoSQL data modeling must be assessed against a user- or owner-defined latency, memory, throughput, size, or cost budget. Produce a performance-envelope note for NoSQL data modeling with test conditions, baseline, observed range, and limitations. Success means conditions and measurements are reproducible, the comparison uses the stated budget, and limitations or resource costs are visible. The review is bounded to access-pattern evidence, key design, consistency assumptions, index cos...Votes: 0GitHub stars: 2
- Nosql Expert Permission Boundary ReviewUse when NoSQL data modeling may read, write, transmit, publish, spend, or administer data or resources. Produce a permission-boundary map for NoSQL data modeling showing identity, requested capability, data scope, and approval gate. Success means every sensitive capability has a named authorization boundary, unnecessary data and privileges are called out, and no action is treated as approved by implication. The review is bounded to access-pattern evidence, key design, consistency assumptions...Votes: 0GitHub stars: 2
- Nosql Expert Release Handoff RecordUse when a NoSQL data modeling review is ready to be handed to a maintainer or accountable operator. Produce a release-handoff record for NoSQL data modeling with version, evidence, open issues, approval state, and safe next step. Success means the receiver can distinguish verified facts, proposals, approvals, and unknowns, and no publication, deployment, write, or contact is claimed unless independently confirmed. The review is bounded to access-pattern evidence, key design, consistency assu...Votes: 0GitHub stars: 2
- Nosql Expert Reproducibility Fixture PlanUse when a NoSQL data modeling result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for NoSQL data modeling defining fixture identity, environment, expected observations, and cleanup boundary. Success means another reviewer can identify the fixture and environment, distinguish expected from observed behavior, and repeat the check without an unapproved external effect. The review is bounded to access-pattern evidence, ...Votes: 0GitHub stars: 2
- Nosql Expert Version CompatibilityUse when a NoSQL data modeling integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for NoSQL data modeling with exact versions, supported combinations, and open questions. Success means the tested version tuple is explicit, each compatibility claim has a dated source or local result, and unsupported combinations are not presented as working. The review is bounded to access-pattern evidence, key design, consistency...Votes: 0GitHub stars: 2
- Snowflake Development Capability Surface MapUse when the actual capability boundary of Snowflake development needs to be distinguished from assumptions. Produce a capability map for Snowflake development covering the requested behavior, verified support, exclusions, and unknowns. Success means each in-scope capability is tied to local configuration, a version-matched authoritative reference, or an observed non-production result, and all unknowns remain explicit. The review is bounded to object and role scope, query behavior, task depen...Votes: 0GitHub stars: 2
- Snowflake Development Change Impact AssessmentUse when a proposed Snowflake development version, setting, schema, model, or integration change may affect existing consumers. Produce a change-impact record for Snowflake development with baseline, affected dependencies, validation needs, and recovery boundary. Success means affected dependencies and compatibility assumptions are evidenced, unknown consumers are named as unknown, and no migration or rollout is implied. The review is bounded to object and role scope, query behavior, task dep...Votes: 0GitHub stars: 2
- Snowflake Development Failure Triage RecordUse when a reproducible Snowflake development symptom needs bounded diagnosis before any corrective change. Produce a failure-triage record for Snowflake development with symptom, environment, evidence, hypothesis, and next safe check. Success means the diagnosis distinguishes observation from inference, each proposed check is reversible and scoped, and unresolved causes remain open. The review is bounded to object and role scope, query behavior, task dependencies, and warehouse cost boundary...Votes: 0GitHub stars: 2
- Snowflake Development Input Output ContractUse when the accepted inputs or produced outputs for Snowflake development need a checkable boundary. Produce an input/output contract for Snowflake development with types, required fields, exclusions, and representative approved fixtures. Success means the contract names its source and version, boundary cases are visible, and no private or unapproved payload is needed to explain the result. The review is bounded to object and role scope, query behavior, task dependencies, and warehouse cost ...Votes: 0GitHub stars: 2
- Snowflake Development Integration Parity TraceUse when Snowflake development exchanges state or data with another component and the handoff needs to be verified. Produce an integration-parity trace for Snowflake development showing source, transformation, destination, and observed result. Success means each material field or state transition has a traceable mapping and observed mismatches are separated from assumptions. The review is bounded to object and role scope, query behavior, task dependencies, and warehouse cost boundary. Use onl...Votes: 0GitHub stars: 2
- Snowflake Development Performance EnvelopeUse when Snowflake development must be assessed against a user- or owner-defined latency, memory, throughput, size, or cost budget. Produce a performance-envelope note for Snowflake development with test conditions, baseline, observed range, and limitations. Success means conditions and measurements are reproducible, the comparison uses the stated budget, and limitations or resource costs are visible. The review is bounded to object and role scope, query behavior, task dependencies, and wareh...Votes: 0GitHub stars: 2
- Snowflake Development Permission Boundary ReviewUse when Snowflake development may read, write, transmit, publish, spend, or administer data or resources. Produce a permission-boundary map for Snowflake development showing identity, requested capability, data scope, and approval gate. Success means every sensitive capability has a named authorization boundary, unnecessary data and privileges are called out, and no action is treated as approved by implication. The review is bounded to object and role scope, query behavior, task dependencies...Votes: 0GitHub stars: 2
- Snowflake Development Release Handoff RecordUse when a Snowflake development review is ready to be handed to a maintainer or accountable operator. Produce a release-handoff record for Snowflake development with version, evidence, open issues, approval state, and safe next step. Success means the receiver can distinguish verified facts, proposals, approvals, and unknowns, and no publication, deployment, write, or contact is claimed unless independently confirmed. The review is bounded to object and role scope, query behavior, task depen...Votes: 0GitHub stars: 2
- Snowflake Development Reproducibility Fixture PlanUse when a Snowflake development result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for Snowflake development defining fixture identity, environment, expected observations, and cleanup boundary. Success means another reviewer can identify the fixture and environment, distinguish expected from observed behavior, and repeat the check without an unapproved external effect. The review is bounded to object and role scope...Votes: 0GitHub stars: 2
- Snowflake Development Version CompatibilityUse when a Snowflake development integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for Snowflake development with exact versions, supported combinations, and open questions. Success means the tested version tuple is explicit, each compatibility claim has a dated source or local result, and unsupported combinations are not presented as working. The review is bounded to object and role scope, query behavior, task ...Votes: 0GitHub stars: 2
- Sql Optimization Result Hash ProofUse when a query is rewritten for latency, cost, or plan quality to create or update a benchmark record with query version, result hash, and timing distribution. Use the configured search, fetch, read, browser, and test capabilities only when available and authorized. Success criterion: normalized result hashes match and the performance difference exceeds noise. Preserve evidence and a run record, limit rework to three meaningful passes, and obtain approval before externally visible or irreve...Votes: 0GitHub stars: 2
- Vector Index Tuning Capability Surface MapUse when the actual capability boundary of Vector index tuning needs to be distinguished from assumptions. Produce a capability map for Vector index tuning covering the requested behavior, verified support, exclusions, and unknowns. Success means each in-scope capability is tied to local configuration, a version-matched authoritative reference, or an observed non-production result, and all unknowns remain explicit. The review is bounded to corpus snapshot, embedding identity, index parameters...Votes: 0GitHub stars: 2
- Vector Index Tuning Change Impact AssessmentUse when a proposed Vector index tuning version, setting, schema, model, or integration change may affect existing consumers. Produce a change-impact record for Vector index tuning with baseline, affected dependencies, validation needs, and recovery boundary. Success means affected dependencies and compatibility assumptions are evidenced, unknown consumers are named as unknown, and no migration or rollout is implied. The review is bounded to corpus snapshot, embedding identity, index paramete...Votes: 0GitHub stars: 2
- Vector Index Tuning Failure Triage RecordUse when a reproducible Vector index tuning symptom needs bounded diagnosis before any corrective change. Produce a failure-triage record for Vector index tuning with symptom, environment, evidence, hypothesis, and next safe check. Success means the diagnosis distinguishes observation from inference, each proposed check is reversible and scoped, and unresolved causes remain open. The review is bounded to corpus snapshot, embedding identity, index parameters, recall measure, and latency budget...Votes: 0GitHub stars: 2
- Vector Index Tuning Input Output ContractUse when the accepted inputs or produced outputs for Vector index tuning need a checkable boundary. Produce an input/output contract for Vector index tuning with types, required fields, exclusions, and representative approved fixtures. Success means the contract names its source and version, boundary cases are visible, and no private or unapproved payload is needed to explain the result. The review is bounded to corpus snapshot, embedding identity, index parameters, recall measure, and latenc...Votes: 0GitHub stars: 2
- Vector Index Tuning Integration Parity TraceUse when Vector index tuning exchanges state or data with another component and the handoff needs to be verified. Produce an integration-parity trace for Vector index tuning showing source, transformation, destination, and observed result. Success means each material field or state transition has a traceable mapping and observed mismatches are separated from assumptions. The review is bounded to corpus snapshot, embedding identity, index parameters, recall measure, and latency budget. Use onl...Votes: 0GitHub stars: 2
- Vector Index Tuning Performance EnvelopeUse when Vector index tuning must be assessed against a user- or owner-defined latency, memory, throughput, size, or cost budget. Produce a performance-envelope note for Vector index tuning with test conditions, baseline, observed range, and limitations. Success means conditions and measurements are reproducible, the comparison uses the stated budget, and limitations or resource costs are visible. The review is bounded to corpus snapshot, embedding identity, index parameters, recall measure, ...Votes: 0GitHub stars: 2
- Vector Index Tuning Permission Boundary ReviewUse when Vector index tuning may read, write, transmit, publish, spend, or administer data or resources. Produce a permission-boundary map for Vector index tuning showing identity, requested capability, data scope, and approval gate. Success means every sensitive capability has a named authorization boundary, unnecessary data and privileges are called out, and no action is treated as approved by implication. The review is bounded to corpus snapshot, embedding identity, index parameters, recal...Votes: 0GitHub stars: 2
- Vector Index Tuning Release Handoff RecordUse when a Vector index tuning review is ready to be handed to a maintainer or accountable operator. Produce a release-handoff record for Vector index tuning with version, evidence, open issues, approval state, and safe next step. Success means the receiver can distinguish verified facts, proposals, approvals, and unknowns, and no publication, deployment, write, or contact is claimed unless independently confirmed. The review is bounded to corpus snapshot, embedding identity, index parameters...Votes: 0GitHub stars: 2
- Vector Index Tuning Reproducibility Fixture PlanUse when a Vector index tuning result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for Vector index tuning defining fixture identity, environment, expected observations, and cleanup boundary. Success means another reviewer can identify the fixture and environment, distinguish expected from observed behavior, and repeat the check without an unapproved external effect. The review is bounded to corpus snapshot, embeddin...Votes: 0GitHub stars: 2
- Vector Index Tuning Version CompatibilityUse when a Vector index tuning integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for Vector index tuning with exact versions, supported combinations, and open questions. Success means the tested version tuple is explicit, each compatibility claim has a dated source or local result, and unsupported combinations are not presented as working. The review is bounded to corpus snapshot, embedding identity, index param...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Dataset Shift 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 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, and with...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Dataset Shift Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection 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 before bui...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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, one testa...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 compared w...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Dataset Shift 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 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 uncertaint...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 measura...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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. Use permis...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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, permis...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Dataset Shift Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection 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 permissio...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 reproduc...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection External Validation 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 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, reviewable, and...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection External Validation Design BlueprintUse when a solution needs a coherent structure before implementation or production for Pattern recognition and applied machine learning: anomaly detection 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 testable befor...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 observations, one ...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 is compa...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection External Validation 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 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 with uncer...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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, and me...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 explicit. Use p...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 hazards, p...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection External Validation Source Research And Provenance LedgerUse when a decision depends on current, source-grounded information for Pattern recognition and applied machine learning: anomaly detection 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. Use perm...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection 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: anomaly detection 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 to a rep...Votes: 0GitHub stars: 2
- Patternlab Anomaly Detection Measurement Uncertainty 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 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, reviewa...Votes: 0GitHub stars: 2