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 glue-data-catalog-and-lake-formation-governancegit 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/glue-data-catalog-and-lake-formation-governance)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/glue-data-catalog-and-lake-formation-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/glue-data-catalog-and-lake-formation-governance/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/glue-data-catalog-and-lake-formation-governance"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/glue-data-catalog-and-lake-formation-governance.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 | $0.00065 | $0.00627 |
| Opus 5 | $0.00032 | $0.00313 |
| Sonnet 5 | $0.00013 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
glue-data-catalog-and-lake-formation-governance 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Glue Data Catalog And Lake Formation Governance
Overview
Use this skill when AWS governance is anchored in Glue Data Catalog and Lake Formation, not only in generic metadata tools. It helps agents design catalog structure, access controls, governed sharing, and publish-safe dataset access across lake and warehouse workflows.
When to Use
- designing
Glue Data Catalogdatabase or table organization - defining
Lake Formationpermissions, tag-based access, or sharing boundaries - reviewing governed access for
S3,Athena,Glue,EMR, orRedshift - improving metadata quality for AWS-native data discovery
- aligning platform-native governance with regulated-data and publish controls
Do not treat Lake Formation and the catalog as only platform-admin setup. They are part of delivery design.
Workflow
-
Define the governed asset boundary. Clarify which datasets, tables, zones, and consumers need AWS-native governance.
-
Design the catalog structure. Decide:
- database boundaries
- table naming and ownership
- metadata quality expectations
- partition and location conventions
-
Define the access model. Include:
- principals and roles
- tag-based access where appropriate
- row or column restrictions when required
- cross-account or consumer sharing behavior
-
Align publish behavior with governance. Require:
- certified versus raw asset distinctions
- explicit publish approval or validation gates where needed
- lineage and ownership visibility for shared assets
-
Validate operational behavior. Check how permissions, schema evolution, new partitions, and cross-service access behave under real delivery conditions.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "IAM alone is enough." | Dataset governance often needs finer-grained sharing, tagging, and lake access patterns than broad service-level IAM. |
| "We can clean up catalog metadata later." | Poor metadata and unclear ownership make governed data hard to discover and trust. |
| "Lake Formation is only for the platform team." | Producers still need to design publish boundaries and access assumptions around it. |
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 · 73 lines · 65 tokens per session scan A 2e8c694dd753
glue-data-catalog-and-lake-formation-governance is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 65 tokens to every session and 627 once invoked, about $0.0003 per session on Opus 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.
Other skills, from other repositories
kafka-shadowtraffic
Generate a ShadowTraffic configuration to populate a Kafka topic with realistic synthetic data. Discovers the target topic, its key and value schemas, and the correct serializers from the live cluster via any attached Kafka MCP server, then writes a ready-to-run shadowtraffic-config.json and Docker command. Use when…
kafka-shadowtraffic-java
Generate a TestContainers Java test class that spins up ShadowTraffic in-process to populate a Kafka topic with synthetic data during tests. Invokes the kafka-shadowtraffic skill to build the ShadowTraffic config, then adapts it for a containerized test network and scaffolds a JUnit 5 test class with Kafka, optional…
kafka-connector-review
Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect…
kafka-consumer-lag
Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress.…
kafka-dlq-review
Review dead letter queue implementations for completeness using the Lenses MCP server. Checks DLQ topic existence, configuration, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. Use when user says "review dead letter queues", "check DLQ setup", "DLQ audit" or asks about…
kafka-perf-review
Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching…