Data output and reporting

Skill wfprieto/wcbs-build-kit/skills/data-output-and-reporting

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.From its SKILL.md

Install

npx -y skills add wfprieto/wcbs-build-kit --skill data-output-and-reporting

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.0 KB, as published. Nobody here has run it

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

Read 40_knowledge/AUTOMATION_AND_REPORTING_PATTERNS.md.
Classify the output:
- live dashboard;
- scheduled report;
- downloadable export;
- API endpoint;
- notification summary;
- audit/compliance evidence;
- ad hoc artifact.
Define data contract, freshness, permissions, privacy controls, and reconciliation checks.
Decide whether scheduling/automation or external API guidance also applies.
Decide whether the output is task-local, audit evidence, release evidence, or reusable learning.
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.
Connected skills: scheduling for refresh, external API if billing provider data is pulled, TDD for transformation logic.
Evidence: sample export, reconciliation total, permission check, failed-run behavior, and retention location.
Compound learning: capture a lesson only if the export reveals a reusable reporting rule, such as timezone-boundary reconciliation or permission checks for generated files.
APIVR verdict: PASS only when accuracy, access control, freshness, and recovery are Verified.

Closeout

Report output format, source of truth, data checks, permission checks, evidence state, reusable-learning decision, and APIVR verdict.

Workflow

Define the decision and audience, validate source data and transformations, then publish a traceable report with its uncertainty and refresh path.