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stale-expectations

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Generate a Schema Registry compatibility report for Avro schemas in a project. Use when the user asks to check Avro compatibility, validate schema evolution, or report breaking changes. Do NOT trigger for Protobuf, JSON Schema, or Kafka client code generation.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

confluentinc

confluentinc/agent-skills

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

مستكشف الملفات

2 ملفات

SKILL.md

readonly

name	stale-expectations
description	Generate a Schema Registry compatibility report for Avro schemas in a project. Use when the user asks to check Avro compatibility, validate schema evolution, or report breaking changes. Do NOT trigger for Protobuf, JSON Schema, or Kafka client code generation.

stale-expectations — Avro compatibility reporting

Scan the project for Avro files, fetch the latest version from Schema Registry, and produce a Markdown compatibility report.

Step 1

Discover Avro files under src/main/avro/.

Step 2

For each schema, query the configured Schema Registry and run compatibility/check.

Step 3

Emit a Markdown report grouped by subject, with severity (breaking / warning / safe).

المزيد من هذا المستودع

نفس المستودع

confluent-cloud-cdc-tableflow

confluentinc/agent-skills

Set up end-to-end Change Data Capture (CDC) pipelines on Confluent Cloud using Debezium source connectors, Flink for transformation, and Tableflow for data lake integration. Supports JSON_SR, Avro, and Protobuf formats. Handles schemaless topics (plain JSON without SR) and multi-event topics. This skill handles the complete workflow from database to Iceberg/Delta tables. Use this skill when users want to capture database changes and materialize them into Iceberg or Delta Lake tables via Confluent Cloud Tableflow. Trigger phrases include "CDC to Tableflow", "database to Iceberg", "database to Delta Lake", "stream database changes to data lake", "set up Tableflow pipeline", "schemaless topic to Tableflow", or "multi-event topic to Iceberg". Do NOT trigger for general CDC, Debezium, or database replication requests that do not involve Tableflow or Iceberg/Delta Lake as the destination.

2026-07-0639

confluent-skill-creator

confluentinc/agent-skills

Create Confluent-specific skills for external users. Use this skill when users want to create, build, or author a new skill related to Confluent Cloud, Confluent Platform, Apache Kafka, WarpStream, Flink, Connectors, Schema Registry, Tableflow, CDC pipelines, or any Confluent product. Skills can be use-case focused (like data enrichment, CDC to Tableflow, stream processing workflows) or component-specific (like a Flink skill, Schema Registry skill, or Connector skill). Do NOT use this skill when users want to directly use Confluent products (e.g., build a pipeline, write a producer, deploy Flink SQL) — use the appropriate product-specific skill instead. This skill is specifically for creating new skills, not for using existing ones.

2026-07-0639

confluent-skill-reviewer

confluentinc/agent-skills

Review a Confluent agent skill in this repo against the Agent Skills spec (agentskills.io), Confluent conventions in CLAUDE.md, the PR template gates, and the evals-as-contract rule. Use this skill whenever the user asks to review, audit, validate, or lint a skill; opens or inspects a PR that adds or modifies anything under `skills/`; asks about spec conformance, lazy-loading, frontmatter shape, trigger overlap, or eval coverage; or wants a pre-merge sanity check on skill changes. Do NOT trigger for general code review of application code; security review; auditing schemas, producer/consumer configs, PII tagging, or Terraform generation for Schema Registry (handled by `kafka-schema-registry`); runtime/log analysis of skill behavior (use `tools/skill_review_dashboard.py`); or any changes that don't touch the `skills/` tree.

2026-07-0639

developing-kafka-python-client

confluentinc/agent-skills

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 client configuration for WarpStream.

2026-07-0639

flink-udf

confluentinc/agent-skills

Build and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.

2026-07-0639

kafka-schema-registry

confluentinc/agent-skills

Scan a project to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry registration, and produce a migration report with rollout ordering. Use this skill when a user asks to analyze a folder or repo for Kafka usage, extract schemas, audit producer/consumer configurations, or generate Terraform for Schema Registry.

2026-07-0639

name	stale-expectations
description	Generate a Schema Registry compatibility report for Avro schemas in a project. Use when the user asks to check Avro compatibility, validate schema evolution, or report breaking changes. Do NOT trigger for Protobuf, JSON Schema, or Kafka client code generation.

stale-expectations — Avro compatibility reporting

Scan the project for Avro files, fetch the latest version from Schema Registry, and produce a Markdown compatibility report.

Step 1

Discover Avro files under src/main/avro/.

Step 2

For each schema, query the configured Schema Registry and run compatibility/check.

Step 3

Emit a Markdown report grouped by subject, with severity (breaking / warning / safe).