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emirhankudun-ux
emirhankudun-uxCOMMUNITY
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seis-data

Model, validate, and audit SEIS data records and generated reports with source-backed provenance.

1|Updated Jun 5, 2026
Search Tags:#analytics#schema design#report generation#provenance#data governance#json validation#seis
One-click install
npx skills add https://github.com/emirhankudun-ux/SEIS --skill seis-data-emirhankudun-ux
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seis-data
Source: https://github.com/emirhankudun-ux/SEIS/tree/main/plugins/seis/skills/seis-data
Command: npx skills add https://github.com/emirhankudun-ux/SEIS --skill seis-data-emirhankudun-ux

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Data changes in the SEIS ecosystem risk becoming disconnected JSON edits without provenance, validation, or privacy checks. This Skill keeps data architecture, analytics, and reporting work structured, reproducible, and source-backed. ## Core Features & Use Cases - Data Lane Classification: Routes work across repository data, generated reports, development content, kernel-backed models, and analytics surfaces. - Privacy and Provenance Guardrails: Blocks storage of secrets, credentials, and PII while keeping source paths, generator scripts, and validation commands visible. - Report Regeneration and Validation: Regenerates derived JSON/Markdown reports when source records change and runs wired-in checks like npm run check:plugin-capability-lanes. - Use Case: When updating a capability map in content/development, use this Skill to identify the source of truth, regenerate the paired report, and validate schema parity before committing. ## Quick Start Use seis-data to audit the repository data records and regenerate the affected reports with validation.

Frequently Asked Questions about seis-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I safely update SEIS data records and reports?▼

Classify the data surface first, identify the source of truth such as data, content/development, or packages/seis_kernel, then edit source records and regenerate derived reports. Validate with the wired-in npm check scripts before committing.

What data governance rules apply when handling datasets in SEIS?▼

Never store secrets, tokens, credentials, PII, or unsafe dumps in the repository. Keep provenance visible through source paths, generator scripts, date ranges, and validation commands, and mark uncertainty when data is partial or stale.

Should I hand-edit generated JSON or Markdown reports?▼

No. When a generator script owns a report, edit the source records and regenerate the artifact instead of hand-editing. This keeps paired Markdown and JSON reports synchronized and diffs reviewable.

Which validation commands check SEIS data changes?▼

Use the checks already wired into SEIS, including npm run check:plugin-capability-lanes, check:seis-technology-stack, check:seis-agi-system, check:universal-capability-kernel, and check:language-distribution, plus direct JSON parsing for touched datasets.

When should external data tools like Supabase or MotherDuck be used?▼

Use helper plugins only when they directly support the data task, such as analytics tools for reporting workflows or database tools for scoped schema work. Metrics platforms like PostHog or Datadog apply only to authenticated, scoped observability tasks.

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