Reusable AI capability
Reconstruct Broken Data Lineage
Apply a repeatable incident method to locate the first data-lineage divergence, bound affected outputs and decisions, and gate repair and reprocessing.
This Skill is a reusable set of instructions based on the linked Amo.ng Prompt or Workflow. It tells you what to provide, how to apply the method and how to know when the task is complete. It does not run automatically.
# Reconstruct Broken Data Lineage Skill ID: AMO-S-000025 Skill URL: https://amo.ng/skills/reconstruct-broken-data-lineage Purpose: Give data, analytics, service, and decision owners a reusable capability for investigating lineage failures across pipelines, metrics, models, dashboards, and downstream decisions. Required inputs: - Exact affected data product, output, metric, model, report, or decision - Authoritative sources, schemas, transformations, queries or code, lineage metadata, orchestration and quality checks - Known-good and affected samples, change and incident timeline, runtime logs, and owner records - Repair, rollback, reprocessing, retention, audit, and decision-suspension constraints How to use: When to use: - A downstream value or decision no longer reconciles to its authoritative source. - A schema, transformation, mapping, orchestration, or quality-control change may have broken lineage. When not to use: - General data catalog design with no observed divergence. - Repairing or reprocessing production data without owner authorization. Reusable method: 1. Freeze the affected outputs, decision window, expected lineage, authoritative source, and known-good comparison. 2. Trace source, extraction, transformation, mapping, storage, aggregation, semantic, and consumption edges with versions and evidence. 3. Compare values and contracts at each boundary to locate the first supported divergence. 4. Bound affected periods, entities, outputs, models, reports, and decisions; distinguish confirmed from possible exposure. 5. Compare smallest repair, rollback, targeted reprocessing, full reprocessing, and continue-investigation paths. 6. Define preservation, validation, reconciliation, downstream notification, and decision-reassessment gates. Expected output: A lineage graph, evidence ledger, first-divergence finding, impact and decision register, repair and reprocessing options, validation plan, unresolved gaps, and owner decisions. Boundaries: Do not invent data, lineage, code, queries, runs, or repair results. Data and service owners authorize changes and reprocessing; analytics and model owners validate downstream artifacts; the accountable decision owner decides reassessment. Source: AMO-P-000318. Applicable Workflow: Rebuild Confidence in an Analytics Decision. Based on Prompt: Data Lineage Break Investigation Source ID: AMO-P-000318 https://amo.ng/prompts/data-lineage-break-investigation Completion criteria: Complete when the first supported divergence and all affected downstream edges are evidence-linked; impact is bounded; a repair and reprocessing disposition has owner, preservation, rollback, and validation gates; and affected decisions are marked usable, limited, suspended, or pending reassessment. Use this Amo.ng Skill with your preferred AI tool. Supply the required inputs and follow the usage instructions. # Reconstruct Broken Data Lineage Skill ID: AMO-S-000025 Skill URL: https://amo.ng/skills/reconstruct-broken-data-lineage Purpose: Give data, analytics, service, and decision owners a reusable capability for investigating lineage failures across pipelines, metrics, models, dashboards, and downstream decisions. Required inputs: - Exact affected data product, output, metric, model, report, or decision - Authoritative sources, schemas, transformations, queries or code, lineage metadata, orchestration and quality checks - Known-good and affected samples, change and incident timeline, runtime logs, and owner records - Repair, rollback, reprocessing, retention, audit, and decision-suspension constraints How to use: When to use: - A downstream value or decision no longer reconciles to its authoritative source. - A schema, transformation, mapping, orchestration, or quality-control change may have broken lineage. When not to use: - General data catalog design with no observed divergence. - Repairing or reprocessing production data without owner authorization. Reusable method: 1. Freeze the affected outputs, decision window, expected lineage, authoritative source, and known-good comparison. 2. Trace source, extraction, transformation, mapping, storage, aggregation, semantic, and consumption edges with versions and evidence. 3. Compare values and contracts at each boundary to locate the first supported divergence. 4. Bound affected periods, entities, outputs, models, reports, and decisions; distinguish confirmed from possible exposure. 5. Compare smallest repair, rollback, targeted reprocessing, full reprocessing, and continue-investigation paths. 6. Define preservation, validation, reconciliation, downstream notification, and decision-reassessment gates. Expected output: A lineage graph, evidence ledger, first-divergence finding, impact and decision register, repair and reprocessing options, validation plan, unresolved gaps, and owner decisions. Boundaries: Do not invent data, lineage, code, queries, runs, or repair results. Data and service owners authorize changes and reprocessing; analytics and model owners validate downstream artifacts; the accountable decision owner decides reassessment. Source: AMO-P-000318. Applicable Workflow: Rebuild Confidence in an Analytics Decision. Based on Prompt: Data Lineage Break Investigation Source ID: AMO-P-000318 https://amo.ng/prompts/data-lineage-break-investigation Completion criteria: Complete when the first supported divergence and all affected downstream edges are evidence-linked; impact is bounded; a repair and reprocessing disposition has owner, preservation, rollback, and validation gates; and affected decisions are marked usable, limited, suspended, or pending reassessment.Copy Skill copies the full Skill instructions. Use with AI copies those instructions with a short guide for your chosen AI tool. Both actions only copy text—they do not send anything to an AI provider or run the Skill.
Purpose
Give data, analytics, service, and decision owners a reusable capability for investigating lineage failures across pipelines, metrics, models, dashboards, and downstream decisions.
Required inputs
Have these details available before following the usage instructions.
- Exact affected data product, output, metric, model, report, or decision
- Authoritative sources, schemas, transformations, queries or code, lineage metadata, orchestration and quality checks
- Known-good and affected samples, change and incident timeline, runtime logs, and owner records
- Repair, rollback, reprocessing, retention, audit, and decision-suspension constraints
How to use this Skill
When to use:
- A downstream value or decision no longer reconciles to its authoritative source.
- A schema, transformation, mapping, orchestration, or quality-control change may have broken lineage.
When not to use:
- General data catalog design with no observed divergence.
- Repairing or reprocessing production data without owner authorization.
Reusable method:
1. Freeze the affected outputs, decision window, expected lineage, authoritative source, and known-good comparison.
2. Trace source, extraction, transformation, mapping, storage, aggregation, semantic, and consumption edges with versions and evidence.
3. Compare values and contracts at each boundary to locate the first supported divergence.
4. Bound affected periods, entities, outputs, models, reports, and decisions; distinguish confirmed from possible exposure.
5. Compare smallest repair, rollback, targeted reprocessing, full reprocessing, and continue-investigation paths.
6. Define preservation, validation, reconciliation, downstream notification, and decision-reassessment gates.
Expected output:
A lineage graph, evidence ledger, first-divergence finding, impact and decision register, repair and reprocessing options, validation plan, unresolved gaps, and owner decisions.
Boundaries:
Do not invent data, lineage, code, queries, runs, or repair results. Data and service owners authorize changes and reprocessing; analytics and model owners validate downstream artifacts; the accountable decision owner decides reassessment. Source: AMO-P-000318. Applicable Workflow: Rebuild Confidence in an Analytics Decision.
Based on an Amo.ng Prompt
Data Lineage Break Investigation
Open the linked Prompt to read the instructions this Skill is based on.
Completion criteria
Complete when the first supported divergence and all affected downstream edges are evidence-linked; impact is bounded; a repair and reprocessing disposition has owner, preservation, rollback, and validation gates; and affected decisions are marked usable, limited, suspended, or pending reassessment.
Was this useful?
Explore related Workflows
Browse WorkflowsRebuild Confidence in an Analytics Decision
Reconstruct broken data lineage, challenge forecast assumptions and drift, and test conclusion sensitivity before relying on a consequential analytical decision.
Related Prompts
Browse PromptsReview an Audit Sampling and Control-Test Design
Challenge a proposed audit sampling and control-test design for population coverage, selection logic, evidence requirements, exception handling and defensible inference before testing begins.
Automate KPI Reporting from Approved Metric Definitions
Implement a repeatable KPI-reporting process from approved metric definitions, with traceable calculations, authoritative reconciliation, failure and rerun tests, disabled delivery, and recovery evidence.
Analytical Conclusion Sensitivity Review
Test whether a consequential analytical conclusion survives plausible changes to data, cohort, definitions, assumptions, model choices, and missing-information treatment.
Forecast Assumption and Model Drift Challenge
Challenge forecast assumptions, structural stability, backtest evidence, scenario sensitivity, and decision thresholds before relying on projected outcomes.
Knowledge Entitlement Drift Review
Reconcile authoritative access policy with effective permissions across source, ingestion, index, cache, retrieval, citation, and response layers.
AI-Generated SQL Result Verification and Reconciliation
Validate AI-generated SQL and its reported results before they are used for a consequential decision.