ME

Standardizes dbt metadata to ensure assets are searchable, governed, and correctly routed by Nova.

Install

mkdir -p .claude/skills/meta-authoring && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/17966" && unzip -o skill.zip -d .claude/skills/meta-authoring && rm skill.zip

Installs to .claude/skills/meta-authoring

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Builds and reviews high-signal `meta.nova` and curated domain references for dbt resources. Use when adding Nova metadata, choosing canonical datasets or indicators, defining model-bound measures or metric templates, annotating columns, de-ranking helper models, authoring domain reference docs, or improving Nova MCP search and metadata scores.
345 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • Author or review `meta.nova` for dbt resources.
  • Classify entities as canonical dataset, helper, metric template, or source.
  • Use entity metadata for stable routing and governance signals.
  • Use column metadata for identifiers, time fields, and high-signal dimensions.
  • Verify referenced fields exist in `meta.nova`.

How it works

The skill guides the authoring and review of `meta.nova` fields by classifying entities, defining business concepts, and applying specific rules for metadata placement and content.

Inputs & outputs

You give it
A request to add or review `meta.nova` for dbt resources or author domain references.
You get back
A final handoff including classification, intended Nova surface, metadata changes, domain reference changes, canonicality evidence, validation results, and sear

When to use meta-authoring

  • Add nova metadata to dbt models
  • Annotate dbt columns
  • Define canonical datasets
  • Review metadata consistency

About this skill

Meta Authoring Skill (dbt-nova)

Mission

Author or review meta.nova so Nova can route agents to the right dbt resource, business definition, grain, and governance signal without adding noisy metadata.

Good metadata is:

  • structurally valid
  • stable business intent, not one-off report logic
  • attached to the execution entity that owns the data or KPI
  • discoverable through Nova search
  • minimal enough to stay maintainable

Required Frame

Before editing, state:

  • entity classification: canonical dataset, helper/staging/intermediate, metric template, source, or column annotation
  • business concept: dataset, measure, metric, dimension, identifier, time field, or governance signal
  • existing surface: current indicators, columns, overlapping entities, and canonical definitions
  • intended surface: entity fields, measures, metric / metrics, column meta.nova, or search.candidates
  • validation path: local schema/audit checks if editing files, then Nova search/score checks after manifest refresh

Do not author a new canonical measure, metric, or dataset until the repeated-concept surface has been checked.

Tool Strategy

Prefer Nova MCP for evidence:

  • show_metadata to confirm the loaded project and manifest timestamp
  • search_indicator and indicator_inventory for measures and metrics
  • search_columns and column_inventory for column semantics
  • search, get_entity, get_context, and get_columns for current contracts
  • compare_grains, find_entity_overlap, or modelling_consistency_report when placement is unclear
  • get_metadata_score and get_undocumented for review and scoring evidence

Use the CLI only when you are validating local file edits or a local manifest build. A hosted MCP endpoint reflects its deployed manifest; it cannot prove uncompiled local YAML changes and should not be reloaded from this skill.

Authoring Rules

  • Use entity metadata for stable routing: canonical, domains, use_cases, synonyms, tier, grain, and governance.
  • Use measures when an aggregation belongs on the execution model.
  • Use metric or metrics for reusable KPI templates; never set both on the same entity.
  • Use column metadata only for identifiers, time fields, high-signal dimensions, measure columns, and genuine semantic disambiguation.
  • Use search.candidates.analyst: false only for helper, ops, staging, or intermediate resources that should remain searchable but not rank first for analysts.
  • Use canonical flags sparingly. Repeated business terms should have one preferred definition at the relevant level.
  • Keep synonyms concise: 2-8 business terms is usually enough.
  • Verify referenced fields exist: grain.time_field, grain.dimensions, measures[].field, and recommended_filters[].field.
  • Treat PII and compliance as first-class governance signals. Prefer entity-level governance for dataset sensitivity and column-level governance only where a specific exposed column needs it.
  • Use curated domain references for narrative guidance that does not belong in meta.nova: business context, scope and exclusions, standard hygiene filters, canonical-entity rationale, gotchas, and cross-domain handoffs. Domain references must point back to Nova metadata rather than replacing it.
  • Do not use raw query corpora, notebook archives, or dashboard history as the source of authority for domain references. They can be supporting evidence only after the governed metadata and eval evidence are clear.

Workflow

  1. Read references/workflow.md.
  2. Read one transport reference: references/transport-mcp.md for live Nova evidence, or references/transport-cli.md for local validation.
  3. Load references/decision-rules.md when choosing a Nova surface or canonical placement.
  4. Load references/patterns.md only when writing YAML.
  5. Load references/domain-reference-template.md when authoring or reviewing a domain reference document. Use references/domain-reference-example.md only when a concrete synthetic example is useful.
  6. Load references/review-checklist.md before final handoff.

Output Standard

Final handoffs must include:

  • classification and intended Nova surface
  • metadata added or changed
  • domain reference sections added or changed, if any
  • repeated-concept and canonicality evidence
  • validation result
  • search or score verification result
  • remaining caveats or follow-up cleanup

Do not dump full YAML unless the user asks for it or the review requires exact patch text.

When not to use it

  • When the task does not involve adding or reviewing Nova metadata or domain references.
  • When the user wants to use raw query corpora, notebook archives, or dashboard history as the source of authority for domain references.
  • When the user wants to dump full YAML without explicit request or review necessity.

Limitations

  • The skill focuses on `meta.nova` and curated domain references for dbt resources.
  • The skill requires structural validity and stable business intent for metadata.
  • The skill uses Nova MCP for evidence gathering.

How it compares

This skill provides a structured approach to authoring high-signal `meta.nova` and curated domain references, ensuring discoverability and maintainability, unlike ad-hoc metadata additions.

Compared to similar skills

meta-authoring side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
meta-authoring (this skill)020dNo flagsAdvanced
database-documentation-gen210dReviewIntermediate
project-logger04moReviewBeginner
domain-model-documentation04moNo flagsIntermediate

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