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 vasilyu1983/AI-Agents-public --skill data-streaminggit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/data-streaming)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/data-streaming"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/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/vasilyu1983/ai-agents-public/data-streaming"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/data-streaming.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.04145 |
| Opus 5 | $0.00020 | $0.02073 |
| Sonnet 5 | $0.00008 | $0.00829 |
| Haiku 4.5 | $0.00004 | $0.00415 |
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 9d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Streaming
Modern Best Practices: choose the event backbone and stream processor separately, treat schemas and replay as product interfaces, default to event-time processing for stateful analytics, and verify managed-service behavior from primary docs before making vendor-specific recommendations.
Primary sources live in data/sources.json. Refresh time-sensitive claims against official docs before making definitive recommendations about managed services, version-specific features, limits, or pricing.
This skill covers the data platform side of streaming: event backbones, CDC, stateful processing, schema governance, and real-time delivery into lakes, warehouses, search, or serving systems.
When to Use
- Choose between Kafka, Redpanda, Pulsar, Kinesis, or managed Kafka offerings
- Design topic strategy, partitioning, retention, replay, and ordering guarantees
- Build or fix CDC pipelines with Debezium, Flink CDC, or managed database-streaming tools
- Choose between Flink, Kafka Streams, Spark Structured Streaming, or lighter transformation paths
- Define schema registry, compatibility, contract, and tombstone handling rules
- Deliver streams into Iceberg, Hudi, Delta, ClickHouse, warehouses, caches, or search systems
- Review streaming SLOs, lag, checkpointing, reprocessing, and operational failure modes
When NOT to Use
- Lakehouse storage formats, catalogs, or medallion architecture -> Use data-lake-platform
- OLTP schema tuning or transactional query optimization -> Use data-sql-optimization
- Event-driven application architecture, CQRS, or domain event design -> Use software-architecture-design
- BI dashboard automation and Metabase APIs -> Use data-metabase
- Product instrumentation and attribution strategy -> Use
marketing-product-analytics
Triage Questions
- What is the real requirement: operational events, CDC, analytical enrichment, or customer-facing low-latency delivery?
- What matters most: portability, managed simplicity, geo-replication, cost, or end-to-end latency?
- Where must ordering hold: globally, per key, or only within a local processing step?
- What is the replay model: full retention, compacted snapshots, time-bounded backfills, or one-shot delivery?
- Which guarantees are required: at-most-once, at-least-once, or business-level exactly-once with idempotent sinks?
- Which downstream systems consume the stream: lakehouse tables, warehouses, search, caches, APIs, or ML features?
- What is the operational baseline: small team, platform team, managed service, or self-hosted multi-region cluster?
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.
- agents/openai.yaml 326 B
- assets/cdc-rollout-checklist.md 1.0 KB
- assets/streaming-platform-scorecard.md 818 B
- assets/topic-contract-template.md 860 B
- data/sources.json 12 KB
- learnings.consolidated.md 590 B
- learnings.md 296 B
- references/cdc-and-schema-governance.md 2.3 KB
- references/control-theory-applied.md 36 KB
- references/distributed-systems-applied.md 42 KB
- references/engine-table-format-state.md 3.9 KB
- references/operations-and-slos.md 3.4 KB
- references/platform-selection.md 5.9 KB
- references/queueing-theory-applied.md 40 KB
- references/stream-processing-patterns.md 3.5 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.
- 9d ago First seen · 259 lines · 41 tokens per session scan A 82ff7c472dd6
data-streaming is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 7d ago), licensed MIT. It adds 41 tokens to every session and 4,145 once invoked, about $0.0002 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 2026-08-30.
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