القائمة
Apache Kafka best practices for event streaming, topic design, and producer-consumer patterns
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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)