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Apache Kafka best practices for event streaming, topic design, and producer-consumer patterns
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Basierend auf der SOC-Berufsklassifikation
Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline
Full Self Driving — autonomous release loop that processes all auto-dev-eligible GitHub issues until none remain, by repeatedly running /pipeline auto-dev then /homework.
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
| name | kafka-best-practices |
| description | Apache Kafka best practices for event streaming, topic design, and producer-consumer patterns |
| scope | core |
| user-invocable | false |
enable.idempotence=trueacks=all, retries > 0, max.in.flight.requests.per.connection <= 5initTransactions(), beginTransaction(), commitTransaction()linger.ms (wait for batch to fill)compression.type=snappy or lz4batch.size: 16KB default, tune based on message sizeenable.auto.commit=true (at-least-once)commitSync() or commitAsync() (better control)session.timeout.ms and heartbeat.interval.ms tuningretention.msretention.bytescleanup.policy=compact)