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 streaming-and-messaging-systemsgit 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/streaming-and-messaging-systems)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/streaming-and-messaging-systems"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/streaming-and-messaging-systems/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/streaming-and-messaging-systems"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/streaming-and-messaging-systems.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 | $0.00050 | $0.00650 |
| Opus 5 | $0.00025 | $0.00325 |
| Sonnet 5 | $0.00010 | $0.00130 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
streaming-and-messaging-systems 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Streaming And Messaging Systems
Overview
Use this skill for continuous data movement and real-time processing. It helps agents design safe event pipelines around brokers and stream processors such as Kafka, Kinesis, and Flink, with attention to ordering, state, replay, schema evolution, and delivery guarantees.
When to Use
- designing or modifying
Kafkatopics, consumers, or stream processors - using
Kinesisfor event ingestion or streaming delivery - implementing
Flinkjobs or stateful stream transformations - defining near-real-time pipelines, windows, watermarks, and replay behavior
- handling schemas and contracts for event-driven systems
Do not use this when the workload is purely batch and does not require streaming semantics.
Workflow
-
Define the event contract. Include:
- event key
- schema and versioning
- ordering expectations
- delivery guarantees
- retention and replay policy
-
Choose the right platform role.
Kafka: durable event backbone and broad ecosystemKinesis: AWS-native streaming ingestion and transportFlink: stateful stream processing and event-time logic
-
Design for state and late data. Account for:
- watermarks
- windows
- deduplication
- exactly-once or at-least-once semantics
- checkpointing and recovery
-
Make consumer behavior observable. Lag, failed checkpoints, poison messages, schema drift, and dead-letter handling should be planned, not discovered in production. For production Kafka hardening, load
kafka-resilience-and-schema-evolution. For live lag or broker metadata before changes, loadmcp-data-observability-integration. -
Treat replay as a first-class operation. Reprocessing events should not depend on manual guesswork.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "Streaming just means faster batch." | Streaming introduces ordering, state, replay, and time semantics that batch systems do not have. |
| "We can ignore schema evolution because events are small." | Event size does not reduce contract risk; schema drift can break many downstream consumers. |
| "Exactly-once is automatic." | Delivery semantics depend on end-to-end design, state handling, sinks, and replay behavior. |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 75 lines · 50 tokens per session scan A f03df2bb2cfc
streaming-and-messaging-systems is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 650 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.
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