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 agentmods add skills/j4flmao/agent-skills/streamingnpx skills add j4flmao/agent-skills --skill 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/streaming)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/streaming"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/streaming.svg" alt="Measured on agentmods" 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 | $0.00112 | $0.06672 |
| Opus 5 | $0.00056 | $0.03336 |
| Sonnet 5 | $0.00022 | $0.01334 |
| Haiku 4.5 | $0.00011 | $0.00667 |
Grade A, and why
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 2d 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 — 563 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Streaming
Purpose
Design streaming data pipelines with Kafka topic architecture, Flink/Kafka Streams processing, schema evolution, and reliability guarantees.
Agent Protocol
Trigger
Exact user phrases: "streaming", "Kafka", "Flink", "Kinesis", "stream processing", "event stream", "real-time", "CDC", "change data capture", "message queue", "stream architecture", "Kafka topic", "Kafka consumer", "stream processing pipeline", "exactly-once", "schema registry", "Avro", "Debezium", "ksqlDB".
Input Context
Before activating, verify:
- Streaming platform (Kafka, Kinesis, Pulsar)
- Processing framework (Flink, Kafka Streams, ksqlDB, Spark Streaming)
- Source systems (database CDC, application events, IoT, logs)
- Target systems (warehouse, lake, search index, cache)
- Throughput and latency requirements
- Data volume and retention requirements
- Security and compliance needs
- Team expertise with streaming technologies
Output Artifact
Streaming pipeline design with topic model, processing logic, reliability config as YAML and SQL.
Response Format
# Topic topology
# Partition strategy
# Consumer group config
-- ksqlDB query
// Flink job skeleton
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Topic model defined with partition count and retention
- Schema Registry configured with evolution rules
- Stream processing job with exactly-once semantics
- CDC pipeline from source database configured
- Error handling with DLQ defined
- Monitoring and lag alerting configured
- Security configured (TLS, auth, ACLs)
- Data retention and compaction strategy defined
Max Response Length
300 lines of code and configuration.
Kafka Architecture
Brokers and Clusters
A Kafka cluster consists of multiple brokers. Each broker is a server that stores topic partitions and serves produce/consume requests. Brokers are identified by a unique ID. A cluster typically has 3-7 brokers for production. Controller broker handles partition leadership and cluster metadata. Brokers should have sufficient disk (RAID 10, SSDs) and network bandwidth for the expected throughput.
What ships with it
6 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.
- 2d ago First seen · 563 lines · 112 tokens per session scan A b6a32decce56
data-streaming is a skill published in the GitHub repository j4flmao/agent-skills (20 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 6,672 once invoked, about $0.0006 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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