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data-catalog

data-catalog is a skill for Claude Code from microsoft/hve-core. It costs 54 tokens per session (1,218 once invoked), scanned A, original, MIT.

A workflow for creating a durable data catalog in a defined Markdown format. A data catalog describes business entities, their sources and properties, and how they relate, while preserving uncertainty and privacy details.

In plain words
What is it for?
Use it to inventory engagement data, document entities and relationships, prepare an ERD, and validate the catalog with the supplied script.
Why use it?
It gives teams a consistent record of available data and prevents guessed relationships from being presented as confirmed facts.

Skill for Claude Code ✓ vendor

Written for Claude Code: user-invocable in frontmatter.

not rated 1.6krepo +8 changed 2d ago A scan SkillSpector: warn 54 tokens original MIT

Good fit Use it to inventory engagement data, document entities and relationships, prepare an ERD, and validate the catalog with the supplied script.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/microsoft/hve-core/data-catalog
View source ↗ microsoft/hve-core
About the project

HVE Core is a collection of agents, prompts, coding instructions, and skills for building repeatable software-development workflows with GitHub Copilot. It is intended for individuals and teams that want structured AI-assisted research, planning, implementation, and review, while the catalogue entries provide many of its reusable workflow components.

microsoft/hve-core · 1,606 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add microsoft/hve-core --skill data-catalog
Clone the repo
git clone --depth 1 https://github.com/microsoft/hve-core

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for data-catalog

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/hve-core/data-catalog/github.svg)](https://agentmods.dev/skills/microsoft/hve-core/data-catalog)
Your own site
<a href="https://agentmods.dev/skills/microsoft/hve-core/data-catalog"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/data-catalog/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for data-catalog

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/hve-core/data-catalog"><img src="https://agentmods.dev/badge/skills/microsoft/hve-core/data-catalog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,218 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Prompt Injection · line 65
    Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.
    Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.01218
Opus 5.5 $0.00022 $0.00487
Sonnet 5.5 $0.00011 $0.00244
Haiku 4.5 $0.00005 $0.00122

Measured 2d ago against content hash e61c3c0f4fcf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-catalog scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/validate_catalog.py, tests/fuzz_harness.py, tests/test_validate_catalog.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.github/skills/data-science-engineering/data-catalog/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Catalog Workflow

Goal

Produce a customer-readable Markdown catalog whose YAML frontmatter is a valid DS_CATALOG_V1 machine contract. Preserve uncertainty explicitly so inferred or assumed relationships never appear confirmed.

Flow

  1. Confirm the engagement name and the caller-approved durable output path.
  2. Inventory entities at business grain. Record source access, tier, volume, profile pointer, classification, lineage, and open questions without copying column-level profile data.
  3. Assign every relationship a stable rel-* identifier. Record endpoints, maximum cardinality, both endpoint minimums, one or more paired join-key fields, confidence, and evidence basis.
  4. Reconcile coverage counts with the entity and relationship records.
  5. Render the human-readable sections from the YAML facts, ending with the canonical Data Science and Engineering Coaching disclaimer footer. Narrative can explain facts but cannot redefine them.
  6. Validate the artifact with scripts/validate_catalog.py before treating it as ready for review.

Inputs

  • Engagement context and a caller-approved output path
  • Data source inventory and access status
  • Business entity names, grain, and declared relationships
  • Existing per-dataset profile paths, when available
  • Privacy classifications or standards citations produced by the owning privacy workflow

Success criteria

  • The frontmatter declares exactly catalog_version: DS_CATALOG_V1 and validates against assets/ds-catalog-v1.schema.json.
  • Entity IDs and relationship IDs are unique and stable. Every endpoint and lineage reference resolves.
  • Every relationship declares cardinality as its maximum multiplicity plus from_minimum and to_minimum as zero or one.
  • Join keys use one string on both sides or paired arrays of equal length, and record field names only without primary-key, foreign-key, or uniqueness roles.
  • Relationship confidence is one of confirmed, inferred, or assumed, and every relationship records its basis.
  • Classification uses the privacy-standards citation-field names. The catalog does not invent standards identifiers.
  • Column statistics and feature metadata remain behind profile_ref rather than being copied into the catalog.
  • Every customer-facing catalog ends with the canonical Data Science and Engineering Coaching disclaimer footer.

Read the full file on GitHub · 80 lines

Files

What ships with it

17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago Changed e61c3c0f4fcf
  2. 2mo ago First seen · 80 lines · 54 tokens per session scan A 6089072adc31

Subscribe to this mod's changes

data-catalog is a skill published in the GitHub repository microsoft/hve-core (1,606 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 1,218 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AI
Related

data-catalog compared with skills from other repositories

data-catalog next to 6 skills from other repositories
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