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 personamanagmentlayer/pcl --skill stream-processing-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/stream-processing-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/stream-processing-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/stream-processing-expert.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 | $0.00090 | $0.02741 |
| Opus 5 | $0.00045 | $0.01371 |
| Sonnet 5 | $0.00018 | $0.00548 |
| Haiku 4.5 | $0.00009 | $0.00274 |
Grade A, and why
stream-processing-expert 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stream Processing Expert
Continuous computation over unbounded data. The hard parts are not throughput —
they are time, state and what happens when something is retried. Complements
kafka-expert (the transport) and airflow-expert (batch orchestration).
Core Concepts
Event Time, Not Processing Time
Every event carries the time it happened. The stream processor sees it later, out of order, sometimes much later. Windowing on arrival time produces results that change if you replay the same data — which makes them untestable and unreproducible.
| Time | Meaning | Use |
|---|---|---|
| Event time | When it happened, from the payload | Correctness; all business aggregation |
| Ingestion time | When the broker received it | Rough operational metrics |
| Processing time | When the operator ran | Latency monitoring only |
If the source has no reliable event timestamp, that is a defect in the producer. Fix it there.
Watermarks
A watermark is the processor's assertion that no event older than time T will arrive. It is how an unbounded stream produces a finite answer: when the watermark passes the end of a window, the window closes and emits.
The watermark is a bet on lateness. Too tight and correct data is dropped; too loose and every result waits.
# Flink: allow 30 seconds of out-of-orderness
watermark_strategy = (
WatermarkStrategy
.for_bounded_out_of_orderness(Duration.of_seconds(30))
.with_timestamp_assigner(lambda event, _: event["occurred_at_ms"])
.with_idleness(Duration.of_minutes(1)) # an idle partition must not stall time
)
with_idleness matters in production: a partition with no traffic holds the
watermark back for the whole job, and every window stops emitting. This is a
frequent and confusing outage.
What ships with it
1 file 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 · 310 lines · 90 tokens per session scan A cdadf455bdf3
stream-processing-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (41 stars, last pushed 2d ago), licensed Apache-2.0. It adds 90 tokens to every session and 2,741 once invoked, about $0.0005 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 2026-09-05.
Other skills, from other repositories
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
senior-data-engineer
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
data-formats
Working with diverse data formats: binary, text, structured, and custom.
developing-kafka-python-client
Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka…
developing-kafka-java-client
Use when the user wants to integrate a Kafka client into an existing application or scaffold a Java Kafka client project (Maven or Gradle based) for Confluent Cloud, local Docker, or WarpStream. Covers the Apache Kafka Java clients (KafkaProducer/KafkaConsumer/KafkaShareConsumer) with Avro, JSON Schema, or Protobuf…