Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ancoleman/ai-design-componentsnpx agentmods add skills/ancoleman/ai-design-components/streaming-dataWrote 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/ancoleman/ai-design-components/streaming-data)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/streaming-data"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/streaming-data/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/ancoleman/ai-design-components/streaming-data"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/streaming-data.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 | $0.00068 | $0.02553 |
| Opus 5 | $0.00034 | $0.01277 |
| Sonnet 5 | $0.00014 | $0.00511 |
| Haiku 4.5 | $0.00007 | $0.00255 |
Grade A, and why
streaming-data 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 7d 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Streaming Data Processing
Build production-ready event streaming systems and real-time data pipelines using modern message brokers and stream processors.
When to Use This Skill
Use this skill when:
- Building event-driven architectures and microservices communication
- Processing real-time analytics, monitoring, or alerting systems
- Implementing data integration pipelines (CDC, ETL/ELT)
- Creating log or metrics aggregation systems
- Developing IoT platforms or high-frequency trading systems
Core Concepts
Message Brokers vs Stream Processors
Message Brokers (Kafka, Pulsar, Redpanda):
- Store and distribute event streams
- Provide durability, replay capability, partitioning
- Handle producer/consumer coordination
Stream Processors (Flink, Spark, Kafka Streams):
- Transform and aggregate streaming data
- Provide windowing, joins, stateful operations
- Execute complex event processing (CEP)
Delivery Guarantees
At-Most-Once:
- Messages may be lost, no duplicates
- Lowest overhead
- Use for: Metrics, logs where loss is acceptable
At-Least-Once:
- Messages never lost, may have duplicates
- Moderate overhead, requires idempotent consumers
- Use for: Most applications (default choice)
Exactly-Once:
- Messages never lost or duplicated
- Highest overhead, requires transactional processing
- Use for: Financial transactions, critical state updates
Quick Start Guide
Step 1: Choose a Message Broker
See references/broker-selection.md for detailed comparison.
Quick decision:
- Apache Kafka: Mature ecosystem, enterprise features, event sourcing
- Redpanda: Low latency, Kafka-compatible, simpler operations (no ZooKeeper)
- Apache Pulsar: Multi-tenancy, geo-replication, tiered storage
- RabbitMQ: Traditional message queues, RPC patterns
Step 2: Choose a Stream Processor (if needed)
See references/processor-selection.md for detailed comparison.
Quick decision:
- Apache Flink: Millisecond latency, real-time analytics, CEP
- Apache Spark: Batch + stream hybrid, ML integration, analytics
- Kafka Streams: Embedded in microservices, no separate cluster
- ksqlDB: SQL interface for stream processing
What ships with it
15 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.
- examples/python/basic_consumer.py 3.7 KB runs code
- examples/typescript/basic-producer.ts 2.5 KB runs code
- outputs.yaml 13 KB
- references/broker-selection.md 12 KB
- references/cdc-patterns.md 7.8 KB
- references/delivery-guarantees.md 9.0 KB
- references/error-handling.md 8.6 KB
- references/event-sourcing.md 7.4 KB
- references/exactly-once.md 8.1 KB
- references/go-patterns.md 8.1 KB
- references/java-patterns.md 9.1 KB
- references/performance-tuning.md 3.1 KB
- references/processor-selection.md 16 KB
- references/python-patterns.md 11 KB
- references/typescript-patterns.md 18 KB
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.
- 7d ago First seen · 362 lines · 68 tokens per session scan A a932e0b4be68
streaming-data is a skill published in the GitHub repository ancoleman/ai-design-components (517 stars, last pushed 9mo ago), licensed MIT. It adds 68 tokens to every session and 2,553 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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