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Claude Skills by Manoj-11-Dahal
github.com/Manoj-11-Dahal15,860 skills0 installs0 views
- Data Engineering Data Pipeline Reproducibility Fixture PlanUse when a Data pipeline engineering result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for Data pipeline engineering 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 source contra...Votes: 0GitHub stars: 2
- Data Engineering Data Pipeline Version CompatibilityUse when a Data pipeline engineering integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for Data pipeline engineering 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 source contract, transformation sequ...Votes: 0GitHub stars: 2
- Polars Capability Surface MapUse when the actual capability boundary of Polars data processing needs to be distinguished from assumptions. Produce a capability map for Polars data processing 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 schema inference, null behavior, lazy plans, me...Votes: 0GitHub stars: 2
- Polars Change Impact AssessmentUse when a proposed Polars data processing version, setting, schema, model, or integration change may affect existing consumers. Produce a change-impact record for Polars data processing 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 schema inference, null behavior, lazy plans, ...Votes: 0GitHub stars: 2
- Polars Failure Triage RecordUse when a reproducible Polars data processing symptom needs bounded diagnosis before any corrective change. Produce a failure-triage record for Polars data processing 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 schema inference, null behavior, lazy plans, memory use, and output parity. Use onl...Votes: 0GitHub stars: 2
- Polars Input Output ContractUse when the accepted inputs or produced outputs for Polars data processing need a checkable boundary. Produce an input/output contract for Polars data processing 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 schema inference, null behavior, lazy plans, memory use, and output parity....Votes: 0GitHub stars: 2
- Polars Integration Parity TraceUse when Polars data processing exchanges state or data with another component and the handoff needs to be verified. Produce an integration-parity trace for Polars data processing 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 schema inference, null behavior, lazy plans, memory use, and output parity. Use only authori...Votes: 0GitHub stars: 2
- Polars Performance EnvelopeUse when Polars data processing must be assessed against a user- or owner-defined latency, memory, throughput, size, or cost budget. Produce a performance-envelope note for Polars data processing 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 schema inference, null behavior, lazy plans, memory use, and outp...Votes: 0GitHub stars: 2
- Polars Permission Boundary ReviewUse when Polars data processing may read, write, transmit, publish, spend, or administer data or resources. Produce a permission-boundary map for Polars data processing 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 schema inference, null behavior, lazy plans, memory us...Votes: 0GitHub stars: 2
- Polars Release Handoff RecordUse when a Polars data processing review is ready to be handed to a maintainer or accountable operator. Produce a release-handoff record for Polars data processing 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 schema inference, null behavior, lazy plans, me...Votes: 0GitHub stars: 2
- Polars Reproducibility Fixture PlanUse when a Polars data processing result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for Polars data processing 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 schema inference, n...Votes: 0GitHub stars: 2
- Polars Version CompatibilityUse when a Polars data processing integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for Polars data processing 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 schema inference, null behavior, lazy plan...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Approval Evidence PacketUse when a feature store lineage and point-in-time parity result is ready for a human owner to approve, reject, or redirect to produce a decision packet for the feature lineage and point-in-time check containing options, evidence, risks, and open questions. Success means the decision owner, requested decision, source evidence, alternatives, uncertainty, and consequence of no action are all visible; training and serving use the intended as-of values without future leakage. Use configured searc...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Change Impact TraceUse when a version, rule, source, or stakeholder change may affect feature store lineage and point-in-time parity to produce a before/after change record and impact map for the feature lineage and point-in-time check. Success means each material difference is tied to affected dependencies, consumers, owners, and a test or explicitly unknown impact; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browser, test, and write capabiliti...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Closeout Handoff LedgerUse when a feature store lineage and point-in-time parity work item is nearing completion, pause, or transfer to another owner to produce a closeout ledger for the feature lineage and point-in-time check with status, evidence, owner, and retention state. Success means all deliverables, unresolved items, approvals, and next owners are recorded, and the stop reason is explicit; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browser...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Completeness ReconciliationUse when the feature store lineage and point-in-time parity workflow receives records that must agree across sources, periods, or statuses to produce a reconciliation worksheet for the feature lineage and point-in-time check with matched, unmatched, and unresolved rows. Success means counts and key fields reconcile or every variance is quantified, sourced, and left unresolved for an owner; training and serving use the intended as-of values without future leakage. Use configured search, fetch,...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Exception Triage QueueUse when the feature store lineage and point-in-time parity workflow has exceptions, missing evidence, or competing priorities to produce a prioritized exception queue for the feature lineage and point-in-time check with evidence, owner, and next action. Success means every exception has a severity rationale, source, owner or explicit unassigned state, and a bounded next step; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browse...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Recovery Readiness DrillUse when the feature store lineage and point-in-time parity workflow needs a safe recovery, rollback, replay, or continuity check to produce a recovery-drill record for the feature lineage and point-in-time check with starting state, test, and observed outcome. Success means the dry-run or approved non-production drill meets the predeclared recovery target and leaves the baseline intact; training and serving use the intended as-of values without future leakage. Use configured search, fetch, r...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Scenario Sensitivity MatrixUse when the feature store lineage and point-in-time parity team needs to compare options under changing assumptions to produce a baseline-plus-scenarios matrix for the feature lineage and point-in-time check with assumptions and ranges. Success means the baseline is reproducible, scenario inputs are explicit, comparable units are used, and conclusions state uncertainty; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browser, tes...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Scope Intake GateUse when a new feature store lineage and point-in-time parity request needs a bounded work scope to produce a scoped intake card for the feature lineage and point-in-time check. Success means owner, objective, permitted sources, acceptance condition, exclusions, and deadline are explicit; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browser, test, and write capabilities only when relevant and authorized. Record evidence, limit ...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Source Provenance LedgerUse when a decision about feature store lineage and point-in-time parity depends on facts from several records or public sources to produce a source-to-claim provenance ledger for the feature lineage and point-in-time check. Success means each material claim has a source, version/date, location, and confidence note; training and serving use the intended as-of values without future leakage. Use configured search, fetch, read, browser, test, and write capabilities only when relevant and authori...Votes: 0GitHub stars: 2
- Aas Data Feature Store Lineage Threshold Rule CheckUse when a feature store lineage and point-in-time parity decision depends on a limit, eligibility rule, policy, or target to produce a rule-check record for the feature lineage and point-in-time check showing source, units, boundary case, and outcome. Success means the rule source and effective date are verified, units and scope match, and borderline cases are flagged rather than auto-approved; training and serving use the intended as-of values without future leakage. Use configured search, ...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Approval Evidence PacketUse when a semi-structured schema evolution result is ready for a human owner to approve, reject, or redirect to produce a decision packet for the JSON schema compatibility table containing options, evidence, risks, and open questions. Success means the decision owner, requested decision, source evidence, alternatives, uncertainty, and consequence of no action are all visible; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Change Impact TraceUse when a version, rule, source, or stakeholder change may affect semi-structured schema evolution to produce a before/after change record and impact map for the JSON schema compatibility table. Success means each material difference is tied to affected dependencies, consumers, owners, and a test or explicitly unknown impact; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test, and write capabilities only ...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Closeout Handoff LedgerUse when a semi-structured schema evolution work item is nearing completion, pause, or transfer to another owner to produce a closeout ledger for the JSON schema compatibility table with status, evidence, owner, and retention state. Success means all deliverables, unresolved items, approvals, and next owners are recorded, and the stop reason is explicit; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test, ...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Completeness ReconciliationUse when the semi-structured schema evolution workflow receives records that must agree across sources, periods, or statuses to produce a reconciliation worksheet for the JSON schema compatibility table with matched, unmatched, and unresolved rows. Success means counts and key fields reconcile or every variance is quantified, sourced, and left unresolved for an owner; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, b...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Exception Triage QueueUse when the semi-structured schema evolution workflow has exceptions, missing evidence, or competing priorities to produce a prioritized exception queue for the JSON schema compatibility table with evidence, owner, and next action. Success means every exception has a severity rationale, source, owner or explicit unassigned state, and a bounded next step; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test,...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Recovery Readiness DrillUse when the semi-structured schema evolution workflow needs a safe recovery, rollback, replay, or continuity check to produce a recovery-drill record for the JSON schema compatibility table with starting state, test, and observed outcome. Success means the dry-run or approved non-production drill meets the predeclared recovery target and leaves the baseline intact; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, bro...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Scenario Sensitivity MatrixUse when the semi-structured schema evolution team needs to compare options under changing assumptions to produce a baseline-plus-scenarios matrix for the JSON schema compatibility table with assumptions and ranges. Success means the baseline is reproducible, scenario inputs are explicit, comparable units are used, and conclusions state uncertainty; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test, and w...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Scope Intake GateUse when a new semi-structured schema evolution request needs a bounded work scope to produce a scoped intake card for the JSON schema compatibility table. Success means owner, objective, permitted sources, acceptance condition, exclusions, and deadline are explicit; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test, and write capabilities only when relevant and authorized. Record evidence, limit refineme...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Source Provenance LedgerUse when a decision about semi-structured schema evolution depends on facts from several records or public sources to produce a source-to-claim provenance ledger for the JSON schema compatibility table. Success means each material claim has a source, version/date, location, and confidence note; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, read, browser, test, and write capabilities only when relevant and authorized. Rec...Votes: 0GitHub stars: 2
- Aas Data Json Schema Evolution Threshold Rule CheckUse when a semi-structured schema evolution decision depends on a limit, eligibility rule, policy, or target to produce a rule-check record for the JSON schema compatibility table showing source, units, boundary case, and outcome. Success means the rule source and effective date are verified, units and scope match, and borderline cases are flagged rather than auto-approved; compatible changes remain readable and breaking changes have a versioned migration path. Use configured search, fetch, r...Votes: 0GitHub stars: 2
- Data Export Row Count Total ReconciliationUse when a report or export is produced from a filtered dataset to create or update a reconciliation table with row counts, keys, and selected totals. Use the configured search, fetch, read, browser, and test capabilities only when available and authorized. Success criterion: source and export counts reconcile under the documented filters. Preserve evidence and a run record, limit rework to three meaningful passes, and obtain approval before externally visible or irreversible writes.Votes: 0GitHub stars: 2
- Data Quality Frameworks Capability Surface MapUse when the actual capability boundary of Data quality frameworks needs to be distinguished from assumptions. Produce a capability map for Data quality frameworks 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 rule definitions, fixture coverage, exception...Votes: 0GitHub stars: 2
- Data Quality Frameworks Change Impact AssessmentUse when a proposed Data quality frameworks version, setting, schema, model, or integration change may affect existing consumers. Produce a change-impact record for Data quality frameworks 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 rule definitions, fixture coverage, excepti...Votes: 0GitHub stars: 2
- Data Quality Frameworks Failure Triage RecordUse when a reproducible Data quality frameworks symptom needs bounded diagnosis before any corrective change. Produce a failure-triage record for Data quality frameworks 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 rule definitions, fixture coverage, exception semantics, and false-positive handl...Votes: 0GitHub stars: 2
- Data Quality Frameworks Input Output ContractUse when the accepted inputs or produced outputs for Data quality frameworks need a checkable boundary. Produce an input/output contract for Data quality frameworks 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 rule definitions, fixture coverage, exception semantics, and false-positi...Votes: 0GitHub stars: 2
- Data Quality Frameworks Integration Parity TraceUse when Data quality frameworks exchanges state or data with another component and the handoff needs to be verified. Produce an integration-parity trace for Data quality frameworks 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 rule definitions, fixture coverage, exception semantics, and false-positive handling. Use ...Votes: 0GitHub stars: 2
- Data Quality Frameworks Performance EnvelopeUse when Data quality frameworks must be assessed against a user- or owner-defined latency, memory, throughput, size, or cost budget. Produce a performance-envelope note for Data quality frameworks 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 rule definitions, fixture coverage, exception semantics, and fa...Votes: 0GitHub stars: 2
- Data Quality Frameworks Permission Boundary ReviewUse when Data quality frameworks may read, write, transmit, publish, spend, or administer data or resources. Produce a permission-boundary map for Data quality frameworks 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 rule definitions, fixture coverage, exception semant...Votes: 0GitHub stars: 2
- Data Quality Frameworks Release Handoff RecordUse when a Data quality frameworks review is ready to be handed to a maintainer or accountable operator. Produce a release-handoff record for Data quality frameworks 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 rule definitions, fixture coverage, exception...Votes: 0GitHub stars: 2
- Data Quality Frameworks Reproducibility Fixture PlanUse when a Data quality frameworks result must be independently repeated or compared without relying on an uncontrolled live action. Produce a reproducibility plan for Data quality frameworks 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 rule definitions,...Votes: 0GitHub stars: 2
- Data Quality Frameworks Version CompatibilityUse when a Data quality frameworks integration or upgrade depends on compatible runtime, client, service, or artifact versions. Produce a version-compatibility record for Data quality frameworks 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 rule definitions, fixture coverage, exce...Votes: 0GitHub stars: 2
- Duplicate Record Review Bucket ClosureUse when an import or reconciliation process reports likely duplicate rows to create or update a dedupe ledger with matched fields and disposition per candidate pair. Use the configured search, fetch, read, browser, and test capabilities only when available and authorized. Success criterion: every pair is merged, retained, or escalated under explicit match rules. Preserve evidence and a run record, limit rework to three meaningful passes, and obtain approval before externally visible or irrev...Votes: 0GitHub stars: 2
- Geo Ops Field Schema Change Impact TraceUse when a version, rule, source, or stakeholder change may affect field-survey schema and collection quality to produce a before/after change record and impact map for the field collection schema review. Success means each material difference is tied to affected dependencies, consumers, owners, and a test or explicitly unknown impact; records contain the needed fields without collecting unnecessary personal or sensitive data. Use configured search, fetch, read, browser, test, and write capab...Votes: 0GitHub stars: 2
- Ml Data Ops Feature Parity Approval Evidence PacketUse when a feature transformation parity result is ready for a human owner to approve, reject, or redirect to produce a decision packet for the feature-pipeline parity report containing options, evidence, risks, and open questions. Success means the decision owner, requested decision, source evidence, alternatives, uncertainty, and consequence of no action are all visible; the same approved fixture produces equivalent features across intended paths. Use configured search, fetch, read, browser...Votes: 0GitHub stars: 2
- Ml Data Ops Feature Parity Change Impact TraceUse when a version, rule, source, or stakeholder change may affect feature transformation parity to produce a before/after change record and impact map for the feature-pipeline parity report. Success means each material difference is tied to affected dependencies, consumers, owners, and a test or explicitly unknown impact; the same approved fixture produces equivalent features across intended paths. Use configured search, fetch, read, browser, test, and write capabilities only when relevant a...Votes: 0GitHub stars: 2
- Ml Data Ops Feature Parity Closeout Handoff LedgerUse when a feature transformation parity work item is nearing completion, pause, or transfer to another owner to produce a closeout ledger for the feature-pipeline parity report with status, evidence, owner, and retention state. Success means all deliverables, unresolved items, approvals, and next owners are recorded, and the stop reason is explicit; the same approved fixture produces equivalent features across intended paths. Use configured search, fetch, read, browser, test, and write capab...Votes: 0GitHub stars: 2
- Ml Data Ops Feature Parity Completeness ReconciliationUse when the feature transformation parity workflow receives records that must agree across sources, periods, or statuses to produce a reconciliation worksheet for the feature-pipeline parity report with matched, unmatched, and unresolved rows. Success means counts and key fields reconcile or every variance is quantified, sourced, and left unresolved for an owner; the same approved fixture produces equivalent features across intended paths. Use configured search, fetch, read, browser, test, a...Votes: 0GitHub stars: 2
- Ml Data Ops Feature Parity Exception Triage QueueUse when the feature transformation parity workflow has exceptions, missing evidence, or competing priorities to produce a prioritized exception queue for the feature-pipeline parity report with evidence, owner, and next action. Success means every exception has a severity rationale, source, owner or explicit unassigned state, and a bounded next step; the same approved fixture produces equivalent features across intended paths. Use configured search, fetch, read, browser, test, and write capa...Votes: 0GitHub stars: 2