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 superset-and-metrics-servinggit 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/superset-and-metrics-serving)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/superset-and-metrics-serving"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/superset-and-metrics-serving/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/superset-and-metrics-serving"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/superset-and-metrics-serving.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 | $0.00040 | $0.01093 |
| Opus 5 | $0.00020 | $0.00547 |
| Sonnet 5 | $0.00008 | $0.00219 |
| Haiku 4.5 | $0.00004 | $0.00109 |
Grade A, and why
superset-and-metrics-serving 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 8d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Superset And Metrics Serving
Overview
Use this skill when Apache Superset or a similar BI serving surface is the final consumer layer. It helps agents keep chart-ready data aligned with governed metrics, prevent dashboard drift, and manage the boundary between analytical modeling and visualization safely.
When to Use
- publishing governed datasets into
Supersetfor dashboard consumption - aligning dashboard metrics with centralized semantic definitions
- preventing BI-layer drift from governed metric definitions
- managing access control and row-level security for dashboard datasets
- designing chart-ready schemas that optimize Superset query performance
- operating Superset as part of a broader data platform
Do not use this when the BI tool is self-service only with no governance expectations, or when metrics are exploratory without shared definitions.
Workflow
-
Define the serving dataset and metric contract. Include:
- which governed datasets or models should be exposed in Superset
- metric definitions: calculation logic, grain, filters, and time dimensions
- who owns each dataset and metric in Superset (matching upstream ownership)
- freshness expectation: how stale can the data be before dashboards mislead?
- access requirements: who can see which datasets and rows?
-
Design chart-ready schemas for query performance.
- pre-aggregate where possible — Superset queries should not scan raw tables
- define time columns explicitly with consistent timezone handling
- use materialized views or dedicated serving tables for complex metrics
- minimize joins in Superset SQL Lab — push join logic into the modeling layer
- index or partition underlying tables to support common filter patterns
-
Keep dashboard metrics aligned with centralized definitions.
- metrics in Superset must match the source-of-truth definition (dbt metrics, semantic layer)
- avoid defining calculation logic directly in Superset that diverges from governed models
- use Superset's metric definition layer to reference pre-built aggregations
- when definitions change upstream, propagate changes to Superset metadata
- audit for drift: scheduled comparison between Superset metrics and source definitions
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.
- 8d ago First seen · 97 lines · 40 tokens per session scan A c32e17f996a7
superset-and-metrics-serving is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 1,093 once invoked, about $0.0002 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 2026-09-03.
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…