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 reverse-etl-and-operational-data-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/reverse-etl-and-operational-data-serving)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/reverse-etl-and-operational-data-serving"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/reverse-etl-and-operational-data-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/reverse-etl-and-operational-data-serving"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/reverse-etl-and-operational-data-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 | $0.00051 | $0.00506 |
| Opus 5 | $0.00026 | $0.00253 |
| Sonnet 5 | $0.00010 | $0.00101 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
reverse-etl-and-operational-data-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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse ETL And Operational Data Serving
Overview
Use this skill when trusted analytical data needs to move back into operational systems. It helps agents design stable outbound contracts, idempotent syncs, destination-aware quality rules, and failure-safe activation pipelines.
When to Use
- syncing warehouse outputs into SaaS tools
- publishing segments, scores, or entity attributes to operational systems
- designing outbound APIs or activation datasets
- changing operationally consumed data contracts
Do not treat reverse ETL like a simple export job. Operational destinations have side effects and user-facing impact.
Workflow
-
Define the outbound contract. Include:
- destination system
- key mapping
- sync cadence
- field semantics
- deletion or unsync behavior
-
Understand the destination constraints. Consider:
- rate limits
- API semantics
- idempotency behavior
- partial update rules
- rollback limitations
-
Make sync behavior explicit. Decide how the system handles:
- upserts
- deletes
- deduplication
- failures
- replay
-
Validate business risk before publish. A bad reverse-ETL sync can affect campaigns, sales workflows, or customer experience directly.
-
Monitor delivery and divergence.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It is just another export." | Operational destinations can create real downstream actions and customer-facing side effects. |
| "We can rerun if something goes wrong." | Replays may duplicate writes or retrigger actions in external tools. |
| "The warehouse model is already trusted." | Destination systems still need contract, mapping, and side-effect safety review. |
Red Flags
- no destination-aware key mapping exists
- deletes or unsync behavior are undefined
- replay logic ignores external side effects
- sync failures are only visible after business users complain
Verification
- Destination constraints and contract are explicit
- Sync behavior for inserts, updates, deletes, and retries is defined
- Business side effects and rollback limits are considered
- Delivery success and divergence are observable
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 · 73 lines · 51 tokens per session scan A 3919fcc28b70
reverse-etl-and-operational-data-serving is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 506 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 2026-09-03.
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-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…
kafka-schema-review
Review Kafka schema changes (Avro, Protobuf, JSON Schema) for compatibility and evolution best practices using the Lenses MCP server. Detects breaking changes, missing defaults, schema drift and naming issues. Use when user says "review schema changes", "check schema compatibility", "will this schema break consumers"…