data-output-and-reporting

name	data-output-and-reporting
description	Use when designing, auditing, or implementing dashboards, exports, recurring reports, analytics outputs, CSV/PDF/spreadsheet artifacts, API reporting endpoints, notification reports, audit ledgers, compliance reports, or business outcome reporting.
activation	Activate when the description trigger applies to the current task.
required_inputs	Task request, relevant repository context, constraints, and authority dependencies.
required_outputs	Skill-specific artifact, verification evidence, canonical verdict, and next action.
authority_dependencies	00_start_here/SOURCE_OF_TRUTH.md; 10_governance/APIVR_EXECUTION_LIFECYCLE.md; 10_governance/source_of_truth/Elite_Build_Goals_v3.md.
evidence_requirements	Executed checks or an honest Unknown, Not Run, or Blocked state for every material claim.

Data Output And Reporting

Use this skill to choose the safest and most useful reporting/output format.

Required Inputs

• Audience, decision the report supports, and required freshness.
• Source data, transformations, filters, permissions, and retention.
• Output channel: dashboard, export, email, file artifact, API endpoint, or audit/compliance record.
• Accuracy threshold and verification horizon.

Routing Workflow

1. Read 40_knowledge/AUTOMATION_AND_REPORTING_PATTERNS.md.
2. Classify the output:
   • live dashboard;
   • scheduled report;
   • downloadable export;
   • API endpoint;
   • notification summary;
   • audit/compliance evidence;
   • ad hoc artifact.
3. Define data contract, freshness, permissions, privacy controls, and reconciliation checks.
4. Decide whether scheduling/automation or external API guidance also applies.
5. Decide whether the output is task-local, audit evidence, release evidence, or reusable learning.
6. Verify data accuracy, completeness, permissions, and business usefulness.

Decision Graph

flowchart TD
  A["Data output needed"] --> B{"Who consumes it?"}
  B -- "Human user" --> C["Dashboard/report with clear labels"]
  B -- "External system" --> D["Export/API contract"]
  B -- "Audit/compliance" --> E["Evidence artifact with provenance"]
  C --> F{"Freshness requirement?"}
  D --> F
  E --> F
  F -- "Real-time/near-real-time" --> G["Event or query-backed output"]
  F -- "Periodic" --> H["Scheduled refresh"]
  F -- "One-time" --> I["Generated artifact"]
  G --> J{"Reusable lesson or recurring report pattern?"}
  H --> J
  I --> J
  J -- "Yes" --> K["Route to compound learning after evidence"]
  J -- "No" --> L["Keep as task-local evidence"]
  K --> M["Validate accuracy, permissions, and failure state"]
  L --> M

Guardrails

• Do not expose private or sensitive data without permission checks.
• Do not report derived metrics without source definition and reconciliation.
• Do not claim business outcome success from a generated report alone.
• Do not create a second source of truth without migration and ownership.
• Do not turn every report into durable learning; capture only reusable, evidence-backed lessons.
• Do not include private data, secrets, raw customer records, or sensitive operational details in solved-problem learning.

Worked Example

Scenario: Add a monthly revenue export.

• Route: scheduled export with immutable evidence artifact.