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.
npx skills add vaquarkhan/data-engineering-agent-skills --skill data-catalog-and-discoverygit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-catalog-and-discovery)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-catalog-and-discovery"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-catalog-and-discovery/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.
<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-catalog-and-discovery"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-catalog-and-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00468 |
| Opus 5 | $0.00024 | $0.00234 |
| Sonnet 5 | $0.00010 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
data-catalog-and-discovery 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 12d 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.
This is a copy
88% identical to data-catalog-and-discovery — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Catalog And Discovery
Overview
Use this skill when the challenge is not only building data, but making it understandable and discoverable. It helps agents treat metadata, ownership, lineage, and usage context as delivery artifacts instead of afterthoughts.
When to Use
- publishing a new shared dataset
- improving catalog metadata quality
- curating lineage, tags, or ownership information
- reducing duplicate datasets created because teams cannot find trusted ones
Do not stop at filling in a title and description. Discovery quality requires operational context too.
Workflow
-
Define the discovery contract. Include:
- owner
- business description
- technical description
- grain
- freshness expectation
- intended consumers
-
Link the asset to its lineage. Show upstream sources, transformation layers, and major downstream uses where possible.
-
Add trust signals. Typical signals:
- quality status
- SLA or freshness status
- certification or review state
- deprecation state
-
Tag for real discovery, not taxonomy theater.
-
Revisit metadata when the contract changes.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The table name is descriptive enough." | Names alone do not explain grain, trust, or ownership. |
| "We can catalog it after people start using it." | Poor discovery usually leads to duplicate local copies first. |
| "Lineage is a platform problem, not a delivery problem." | Producers know the business meaning and must help make lineage useful. |
Red Flags
- shared datasets have no owner or description
- certified and experimental assets are indistinguishable
- metadata is copied from schema names without business meaning
- deprecation state is absent for old assets
Verification
- Ownership, description, grain, and freshness are documented
- Lineage or source context is attached
- Trust signals exist for consumers
- Discovery metadata is updated when the contract 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.
- 12d ago First seen · 67 lines · 48 tokens per session scan A 92aedf4d08ea
data-catalog-and-discovery is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 468 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to data-catalog-and-discovery, differing in 12 lines, and is treated as a copy.
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