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data-product-create

data-product-create is a skill for Claude Code, Codex from collibra/chip. It costs 58 tokens per session (4,263 once invoked), scanned A, original, Apache-2.0.

A guided Collibra workflow for packaging an existing physical database table as a Data Product, including a Port and Data Contract. A Data Product is a governed, reusable offering of data; a Port describes access, and a Data Contract records expectations between producers and users.

In plain words
What is it for?
Use it to create a governed Data Product from a table identified by name, UUID, or Collibra URL, with selected related tables, ownership, domain, service expectations, and contract details.
Why use it?
It discovers related tables, checks for overlap, gathers governance details, and presents the proposed result before making changes. This helps prevent accidental inclusion of data or creation of incorrect assets.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 37repo changed 28d ago A scan SkillSpector: pass 58 tokens original Apache-2.0

Good fit Use it to create a governed Data Product from a table identified by name, UUID, or Collibra URL, with selected related tables, ownership, domain, service expectations, and contract details.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/collibra/chip/data-product-create
View source ↗ collibra/chip
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 collibra/chip --skill data-product-create
Clone the repo
git clone --depth 1 https://github.com/collibra/chip

Made for: Claude Code, Codex.

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-product-create

README.md
[![agentmods](https://agentmods.dev/badge/skills/collibra/chip/data-product-create/github.svg)](https://agentmods.dev/skills/collibra/chip/data-product-create)
Your own site
<a href="https://agentmods.dev/skills/collibra/chip/data-product-create"><img src="https://agentmods.dev/badge/skills/collibra/chip/data-product-create/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-product-create

Your own site · 80×15
<a href="https://agentmods.dev/skills/collibra/chip/data-product-create"><img src="https://agentmods.dev/badge/skills/collibra/chip/data-product-create.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,263 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 pass 7 Sept 2026
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 $0.00058 $0.04263
Opus 5.5 $0.00023 $0.01705
Sonnet 5.5 $0.00012 $0.00853
Haiku 4.5 $0.00006 $0.00426

Measured 28d ago against content hash bd5a8172829c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-product-create 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 28d ago.

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.

pkg/skills/files/collibra/data-product-create/SKILL.md · 279 lines

How it starts

The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Creating a Data Product from a table

Packages an existing physical table — given as a name, DGC UUID, or Collibra URL — into a Collibra Data Product with one Port and a Data Contract. Phases 1–5 are read-only discovery and a proposal; Phase 6 is the single confirmation; Phase 7 writes. Generic tool mechanics — how to set attributes, write Markdown, handle each tool's success/error responses, resolve relation names, and recover from partial failures — live in collibra/asset-create and collibra/asset-edit; this skill is the DP-specific layer on top.

Hard rules

  1. Confirm once. Do discovery silently, gather every required choice (included tables, domain, owner, SLAs) in as few turns as possible and before the overview, present one complete overview (Phase 5), take a single explicit approval (Phase 6), then execute all writes (Phase 7) with no further prompts. Never ask field-by-field; never re-confirm individual writes.
  2. Nothing is auto-included or auto-created without that approval. Adding a table is an access decision: suggested tables default to not included and the user opts them in. Creating assets is significant and not auto-rolled-back — wrong assets must be deleted manually.
  3. One Port by default; more only if the user explicitly asks.
  4. Fill every applicable attribute — the reference tables are the source of truth for which attributes each asset has. Draft a proposed value for every attribute listed there; leave blank only the SLAs the user declined and Pricing without supplied cost. SLA values are discrete Data Contract attributes — never written into Description or the manifest.
  5. Existing coverage is a stop condition: if a Data Product/contract already covers substantially the same tables, stop and offer to extend it instead.
  6. Say where inferred values come from. When a value is drawn from another asset or otherwise inferred, tell the user in chat where it came from so they can judge it — the written attribute holds only the clean value, not provenance markers.
  7. Attribute writes (per collibra/asset-edit): set a not-yet-valued attribute with add_attribute, not update_attribute (which only changes an existing value). If any name doesn't resolve on this instance, follow the error's suggestion.

Read the full file on GitHub · 279 lines

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. 28d ago Changed bd5a8172829c
  2. 2mo ago First seen · 279 lines · 58 tokens per session scan A 325041f72ead

Subscribe to this mod's changes

data-product-create is a skill published in the GitHub repository collibra/chip (37 stars, last pushed yesterday), licensed Apache-2.0. It adds 58 tokens to every session and 4,263 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-product-create compared with skills from other repositories

data-product-create next to 6 skills from other repositories
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foundry-config-setupmicrosoft/agent-frameworkSkill15kyesterdayMITA65Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skillsSkill22kyesterdayApache-2.0A132Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleepSkill19k3d agoMITA35Claude Code
nemo-automodel-launcher-configNVIDIA/skillsSkill3.9kyesterdayApache-2.0A30Claude Code
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