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 j4flmao/agent-skills --skill data-streaminggit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/data-streaming)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-streaming"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-streaming/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/j4flmao/agent-skills/data-streaming"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-streaming.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 | $0.00145 | $0.02116 |
| Opus 5.5 | $0.00058 | $0.00846 |
| Sonnet 5.5 | $0.00029 | $0.00423 |
| Haiku 4.5 | $0.00015 | $0.00212 |
Grade A, and why
backend-data-streaming 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 today.
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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Streaming Architecture
Purpose
Build pipelines that process continuous event streams with predictable latency and correct results under failures, late data and redelivery. Covers Kafka topic design, consumer scaling, processing semantics, and stateful stream processing with Flink or Kafka Streams.
Agent Protocol
Trigger
Exact user phrases: "data streaming", "kafka architecture", "stream processing", "flink job", "kafka streams", "exactly once", "watermarks", "windowed aggregation", "consumer lag", "log compaction".
Input Context
- Event sources, volume (events/s, bytes/s), and latency target.
- Ordering and correctness needs (approximate OK vs exact counts, money).
- Processing engine in use or allowed (Flink, Kafka Streams, Spark Structured Streaming, plain consumers).
- Sinks (DB, search index, another topic, warehouse).
Output Artifact
Pipeline diagram, topic/partition plan, producer/consumer/processor config, and processing code. No files unless requested.
Response Format
Pipeline: {source} -> {topic(partitions, RF)} -> {processor} -> {sink}
Key: {partition key} Ordering: per key
Semantics: {at-least-once + idempotent sink | exactly-once (transactions)}
Time: {event time, watermark = max_ts - {lateness}} Window: {tumbling/sliding/session, size}
State: {store/backend, TTL} Checkpoint: {interval}
Kafka Fundamentals That Drive Design
| Concept | Rule |
|---|---|
| Partition | Unit of ordering and parallelism. Order is only guaranteed within a partition |
| Key | Same key -> same partition (default hash partitioner), so key by the entity whose order matters |
| Consumer group | Each partition is consumed by at most one member of a group; members beyond the partition count sit idle |
| Partition count | Can be increased but not decreased; increasing remaps keys, breaking per-key order for in-flight data. Size for future peak |
| Retention | Time/size-based delete, or cleanup.policy=compact to keep the latest value per key (changelogs, state rebuild). Tombstone = null value deletes a key |
| Replication | replication.factor=3 with min.insync.replicas=2 and producer acks=all survives one broker loss without data loss |
| Metadata | Kafka 4.x runs only in KRaft mode; ZooKeeper is gone |
What ships with it
7 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.
- today Changed · +137 lines · +145 tokens per session ee2377a615ef
- 1mo ago First seen · 23 lines · 0 tokens per session scan A 3b39c6aa7ec9
backend-data-streaming is a skill published in the GitHub repository j4flmao/agent-skills (34 stars, last pushed today), licensed MIT. It adds 145 tokens to every session and 2,116 once invoked, about $0.0006 per session on Opus 5.5. 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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