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Use this skill when a user asks to analyze a folder or repo for Kafka usage, extract schemas, audit producer\u002Fconsumer configurations, or generate Terraform for Schema Registry.","confluentinc\u002Fagent-skills","confluentinc","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9439498?v=4","01M6DRHHXAPZ51S955GTK4NG5H","Confluent Inc.","01M6DRHJ1QGVS341XSPAW6G0QF",64,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fkafka-schema-registry\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"kafka schema registry","registry","kafka","schema","repo","project","scan","registration","models","migration","rollout","use","generate","analyze","applications","pii","data","audit","produce","report","2026-11-10 10:12:30",false,"---\nname: kafka-schema-registry\ndescription: 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\u002Fconsumer configurations, or generate Terraform for Schema Registry.\nmetadata:\n  version: \"1.0.0\"\n---\n\n# Kafka Schema Registry Skill\n\nScan a project to identify Kafka applications, extract schemas, generate Terraform for Schema Registry registration, and produce a comprehensive analysis report.\n\n## When to Use\n\nInvoke this skill when:\n- A user asks to analyze a project for Kafka usage in order to add event schemas or integrate Schema Registry\n- A user wants to extract schemas from Kafka producers\n- A user wants Terraform to register schemas to Schema Registry\n- A user wants to audit Kafka producer\u002Fconsumer configurations\n\n## Deliverables\n\nThis skill produces 3 outputs in the target project:\n\n1. **`schema-report.md`** — Full analysis report with findings, risks, and upgrade recommendations\n2. **`schemas\u002F`** — Extracted schema files (Avro, JSON Schema, Protobuf) with PII tagging\n3. **`terraform\u002F`** — Terraform configs using Confluent provider to register schemas\n\n### Optional: Code Migration Assistance\n\nIf the user asks for their application code to be updated to integrate Schema Registry, use the [Code Migration Reference](references\u002Fcode-migration.md) to update the code with proper Schema Registry integration patterns.\n\n---\n\n## High-Level Workflow\n\n### Phase 0: Initialize\n\n- Check for existing `schema.yaml` and `schemas\u002F` directory manually\n- Note any existing schema infrastructure in the report\n\n### Phase 1: Project Scan & Kafka Detection\n\n1. **Find build files** — Search for `pom.xml`, `build.gradle`, `requirements.txt`, `package.json`, etc.\n2. **Detect Kafka dependencies** — Look for `spring-kafka`, `confluent-kafka`, `kafkajs`, etc.\n3. **Find producers & consumers** — Grep for `KafkaTemplate`, `Producer(`, `producer.send`, etc.\n4. **Extract topic names** — From string literals, config properties, YAML files\n5. **Identify serializers** — Find `value.serializer`, `KafkaAvroSerializer`, custom serializers\n6. **Build app catalog** — Compile findings: app name, language, role, topics, serializer, category\n\n**Detailed patterns:** [Detection Patterns Reference](references\u002Fdetection-patterns.md)\n\n**App catalog structure:**\n```yaml\napp_name: module name\nlanguage: Java | Python | .NET | Go | Node\u002FTS\nrole: producer | consumer | both\ntopics: [list of topics]\nserializer_class: value.serializer used\ncustom_serializer: true | false\nschema_format: AVRO | JSON | PROTOBUF | UNKNOWN\nsr_integrated: true | false\ncategory: A | B | C | D | E  # REQUIRED\n```\n\n**Multi-schema topic detection:**\n- If multiple data models produce to the same topic, create a wrapper schema with `oneOf`\u002Funion\u002F`oneof`\n- Generate Terraform with `schema_reference` blocks\n- Flag prominently in report\n\n### Phase 2: Risk Detection\n\nSearch for:\n- **`auto.register.schemas=true`** — Uncontrolled schema evolution (Category C)\n- **`use.latest.version`** — Eases migration when set\n- **Custom serializers** — Bypass SR entirely (Category E)\n\nRecord file path, line number, and affected topics for each occurrence.\n\n**Patterns:** [Detection Patterns Reference](references\u002Fdetection-patterns.md#risk-detection)\n\n### Phase 3: Schema Inference\n\nFor each producer:\n1. **Check for existing schema files** — `**\u002F*.avsc`, `**\u002F*.proto`, `**\u002F*.schema.json`\n2. **Infer from data models** — Java classes, Pydantic models, TypeScript interfaces, Go structs\n3. **Infer from inline data** — HashMap, dict literals, map[string]any, plain objects, JSON strings\n4. **Convert to schemas** — Map language types to JSON Schema \u002F Avro \u002F Protobuf\n5. **Tag PII fields** — Scan field names for `email`, `ssn`, `phone`, `address`, etc.\n\n**PII tagging:** Add `confluent:tags` (`PII`, `PRIVATE`, `SENSITIVE`, `PHI`) to detected fields.\n\n**Detailed inference patterns:** [Schema Inference Reference](references\u002Fschema-inference.md)\n\n### Phase 4: Categorize Producers\n\nClassify each producer:\n\n| Category | Criteria |\n|----------|----------|\n| **A: Compliant** | Confluent serializer + SR + no auto.register |\n| **A→Header** | Already on SR, migrating to headers |\n| **B: Schema in code, no SR** | Data models exist, but no SR integration |\n| **C: Auto-register** | `auto.register.schemas=true` |\n| **D: No schema** | Raw strings\u002Fbytes, no data model |\n| **E: Custom serializer** | Custom `Serializer\u003CT>` or inline serialization without SR |\n\n**CRITICAL:** Use exact phrase \"Category X\" in:\n- App catalog field\n- Applications Discovered table\n- Report section headers\n- Terraform comments\n- Risk sections\n\n**Details:** [Categorization Reference](references\u002Fcategorization.md)\n\n### Phase 5: Create Schema Files\n\n**Directory structure:**\n```\nschemas\u002F\n├── avro\u002F\n│   └── {topic}-value.avsc\n├── json\u002F\n│   └── {topic}-value.json\n└── proto\u002F\n    └── {topic}-value.proto\n```\n\n**File naming:** MUST use **kebab-case** (lowercase with hyphens):\n- Value: `{topic}-value.{ext}`\n- Key: `{topic}-key.{ext}`\n- Examples: `order-events-value.avsc`, `user-notifications-value.json`\n\n**Initialize:** Create `schema.yaml`.\n\n**Validate:** Call `schema_lint(path: schemas\u002F, fix: true)` if available.\n\n### Phase 6: Generate Terraform\n\n**File structure (MANDATORY separate files):**\n```\nterraform\u002F\n├── providers.tf              # Provider config\n├── variables.tf              # Variable definitions\n├── tags.tf                   # confluent_tag resources (if PII exists)\n├── schemas.tf                # Active schemas (A, B, E)\n├── flagged-auto-register.tf  # Category C only (commented out)\n├── outputs.tf                # Output values\n└── import.sh                 # Import script\n```\n\n**CRITICAL:**\n- `schemas.tf` = Categories A, B, E — NOT commented out\n- `flagged-auto-register.tf` = Category C ONLY — MUST be commented out\n- `tags.tf` = MUST exist if ANY schema uses `confluent:tags`\n- Each schema resource MUST have comment block: Topic, App, Source, Category\n\n**Templates:** [Terraform Templates Reference](references\u002Fterraform-templates.md)\n\n### Phase 7: Generate Report\n\nCreate `schema-report.md` with:\n- Executive Summary (metrics + category breakdown)\n- **Applications Discovered table** (EXACT format, Category column MANDATORY)\n- RISKS (auto-register, custom serializers)\n- Producer Upgrade Recommendations (per app, with \"Category X\" in heading)\n- Migration Rollout Ordering (by category)\n- PII Fields Detected\n- Terraform Resources Generated\n- Next Steps checklist\n\n**CRITICAL formatting requirements:**\n1. Applications Discovered = markdown table, NOT narrative sections\n2. Every app section MUST say \"Category X\" explicitly\n3. Terraform comment blocks required for every resource\n\n**Template:** [Report Template Reference](references\u002Freport-template.md)\n\n---\n\n## Migration Rollout by Category\n\n- **Category B** (JSON, no SR): Producers first → consumers\n- **Category A→Header** (already on SR): Verify consumer versions → producers only\n- **Category C** (auto-register): Register via Terraform → disable auto-register → producers fetch latest\n- **Category E** (custom serializers): Consumers first (composite deserializer) → producers\n\n**Details:** [Categorization Reference](references\u002Fcategorization.md#migration-rollout-order-by-category)\n\n---\n\n## Edge Cases\n\n- **Monorepos:** Treat each service\u002Fmodule with Kafka deps as separate app\n- **Multi-topic producers:** Generate one schema resource per topic\n- **Shared schemas:** One schema file, multiple Terraform resources reference it\n- **No topic names:** If loaded from env vars, use placeholders with TODO\n- **Test code:** Skip test directories unless they contain only schema definitions\n- **Multiple serializers:** Create separate schema files per format\n\n---\n\n## Output Organization\n\n```\n{project_root}\u002F\n├── schema-report.md              # Analysis report\n├── schemas\u002F\n│   ├── schema.yaml               # Schema project config\n│   ├── avro\u002F\n│   │   └── {topic}-value.avsc\n│   ├── json\u002F\n│   │   └── {topic}-value.json\n│   └── proto\u002F\n│       └── {topic}-value.proto\n└── terraform\u002F\n    ├── providers.tf\n    ├── variables.tf\n    ├── tags.tf                    # PII\u002FPRIVATE\u002FSENSITIVE tags\n    ├── schemas.tf                 # Active schemas (depends_on tags)\n    ├── flagged-auto-register.tf   # Commented-out Category C\n    ├── outputs.tf\n    └── import.sh                  # Import existing schemas\n```\n\n---\n\n## Reference Documentation\n\n- [Detection Patterns](references\u002Fdetection-patterns.md) — Patterns for finding Kafka apps, dependencies, producers, consumers, serializers\n- [Schema Inference](references\u002Fschema-inference.md) — Extract schemas from data models, inline data, PII tagging\n- [Categorization](references\u002Fcategorization.md) — Category definitions, rollout order, client version requirements\n- [Terraform Templates](references\u002Fterraform-templates.md) — File structure, templates, naming conventions\n- [Report Template](references\u002Freport-template.md) — Required sections, formatting rules, validation checklist\n- [Code Migration](references\u002Fcode-migration.md) — Serializer\u002Fdeserializer implementation patterns for Python, Java, JavaScript, Go, and .NET\n\n---\n\n## Execution Approach\n\n1. Use **Glob** to find build files and schema files\n2. Use **Grep** for pattern detection (dependencies, producers, serializers, risks)\n3. Use **Read** to inspect source files and data models\n4. Use **Write** to create schema files, Terraform configs, and report\n\n**No need to use Agent tool** — this skill is self-contained and uses direct tool calls.\n","# Kafka Schema Registry Skill\n\nScan a project to identify Kafka applications, extract schemas, generate Terraform for Schema Registry registration, and produce a comprehensive analysis report.\n\n## When to Use\n\nInvoke this skill when:\n- A user asks to analyze a project for Kafka usage in order to add event schemas or integrate Schema Registry\n- A user wants to extract schemas from Kafka producers\n- A user wants Terraform to register schemas to Schema Registry\n- A user wants to audit Kafka producer\u002Fconsumer configurations\n\n## Deliverables\n\nThis skill produces 3 outputs in the target project:\n\n1. **`schema-report.md`** — Full analysis report with findings, risks, and upgrade recommendations\n2. **`schemas\u002F`** — Extracted schema files (Avro, JSON Schema, Protobuf) with PII tagging\n3. **`terraform\u002F`** — Terraform configs using Confluent provider to register schemas\n\n### Optional: Code Migration Assistance\n\nIf the user asks for their application code to be updated to integrate Schema Registry, use the [Code Migration Reference](references\u002Fcode-migration.md) to update the code with proper Schema Registry integration patterns.\n\n---\n\n## High-Level Workflow\n\n### Phase 0: Initialize\n\n- Check for existing `schema.yaml` and `schemas\u002F` directory manually\n- Note any existing schema infrastructure in the report\n\n### Phase 1: Project Scan & Kafka Detection\n\n1. **Find build files** — Search for `pom.xml`, `build.gradle`, `requirements.txt`, `package.json`, etc.\n2. **Detect Kafka dependencies** — Look for `spring-kafka`, `confluent-kafka`, `kafkajs`, etc.\n3. **Find producers & consumers** — Grep for `KafkaTemplate`, `Producer(`, `producer.send`, etc.\n4. **Extract topic names** — From string literals, config properties, YAML files\n5. **Identify serializers** — Find `value.serializer`, `KafkaAvroSerializer`, custom serializers\n6. **Build app catalog** — Compile findings: app name, language, role, topics, serializer, category\n\n**Detailed patterns:** [Detection Patterns Reference](references\u002Fdetection-patterns.md)\n\n**App catalog structure:**\n```yaml\napp_name: module name\nlanguage: Java | Python | .NET | Go | Node\u002FTS\nrole: producer | consumer | both\ntopics: [list of topics]\nserializer_class: value.serializer used\ncustom_serializer: true | false\nschema_format: AVRO | JSON | PROTOBUF | UNKNOWN\nsr_integrated: true | false\ncategory: A | B | C | D | E  # REQUIRED\n```\n\n**Multi-schema topic detection:**\n- If multiple data models produce to the same topic, create a wrapper schema with `oneOf`\u002Funion\u002F`oneof`\n- Generate Terraform with `schema_reference` blocks\n- Flag prominently in report\n\n### Phase 2: Risk Detection\n\nSearch for:\n- **`auto.register.schemas=true`** — Uncontrolled schema evolution (Category C)\n- **`use.latest.version`** — Eases migration when set\n- **Custom serializers** — Bypass SR entirely (Category E)\n\nRecord file path, line number, and affected topics for each occurrence.\n\n**Patterns:** [Detection Patterns Reference](references\u002Fdetection-patterns.md#risk-detection)\n\n### Phase 3: Schema Inference\n\nFor each producer:\n1. **Check for existing schema files** — `**\u002F*.avsc`, `**\u002F*.proto`, `**\u002F*.schema.json`\n2. **Infer from data models** — Java classes, Pydantic models, TypeScript interfaces, Go structs\n3. **Infer from inline data** — HashMap, dict literals, map[string]any, plain objects, JSON strings\n4. **Convert to schemas** — Map language types to JSON Schema \u002F Avro \u002F Protobuf\n5. **Tag PII fields** — Scan field names for `email`, `ssn`, `phone`, `address`, etc.\n\n**PII tagging:** Add `confluent:tags` (`PII`, `PRIVATE`, `SENSITIVE`, `PHI`) to detected fields.\n\n**Detailed inference patterns:** [Schema Inference Reference](references\u002Fschema-inference.md)\n\n### Phase 4: Categorize Producers\n\nClassify each producer:\n\n| Category | Criteria |\n|----------|----------|\n| **A: Compliant** | Confluent serializer + SR + no auto.register |\n| **A→Header** | Already on SR, migrating to headers |\n| **B: Schema in code, no SR** | Data models exist, but no SR integration |\n| **C: Auto-register** | `auto.register.schemas=true` |\n| **D: No schema** | Raw strings\u002Fbytes, no data model |\n| **E: Custom serializer** | Custom `Serializer\u003CT>` or inline serialization without SR |\n\n**CRITICAL:** Use exact phrase \"Category X\" in:\n- App catalog field\n- Applications Discovered table\n- Report section headers\n- Terraform comments\n- Risk sections\n\n**Details:** [Categorization Reference](references\u002Fcategorization.md)\n\n### Phase 5: Create Schema Files\n\n**Directory structure:**\n```\nschemas\u002F\n├── avro\u002F\n│   └── {topic}-value.avsc\n├── json\u002F\n│   └── {topic}-value.json\n└── proto\u002F\n    └── {topic}-value.proto\n```\n\n**File naming:** MUST use **kebab-case** (lowercase with hyphens):\n- Value: `{topic}-value.{ext}`\n- Key: `{topic}-key.{ext}`\n- Examples: `order-events-value.avsc`, `user-notifications-value.json`\n\n**Initialize:** Create `schema.yaml`.\n\n**Validate:** Call `schema_lint(path: schemas\u002F, fix: true)` if available.\n\n### Phase 6: Generate Terraform\n\n**File structure (MANDATORY separate files):**\n```\nterraform\u002F\n├── providers.tf              # Provider config\n├── variables.tf              # Variable definitions\n├── tags.tf                   # confluent_tag resources (if PII exists)\n├── schemas.tf                # Active schemas (A, B, E)\n├── flagged-auto-register.tf  # Category C only (commented out)\n├── outputs.tf                # Output values\n└── import.sh                 # Import script\n```\n\n**CRITICAL:**\n- `schemas.tf` = Categories A, B, E — NOT commented out\n- `flagged-auto-register.tf` = Category C ONLY — MUST be commented out\n- `tags.tf` = MUST exist if ANY schema uses `confluent:tags`\n- Each schema resource MUST have comment block: Topic, App, Source, Category\n\n**Templates:** [Terraform Templates Reference](references\u002Fterraform-templates.md)\n\n### Phase 7: Generate Report\n\nCreate `schema-report.md` with:\n- Executive Summary (metrics + category breakdown)\n- **Applications Discovered table** (EXACT format, Category column MANDATORY)\n- RISKS (auto-register, custom serializers)\n- Producer Upgrade Recommendations (per app, with \"Category X\" in heading)\n- Migration Rollout Ordering (by category)\n- PII Fields Detected\n- Terraform Resources Generated\n- Next Steps checklist\n\n**CRITICAL formatting requirements:**\n1. Applications Discovered = markdown table, NOT narrative sections\n2. Every app section MUST say \"Category X\" explicitly\n3. Terraform comment blocks required for every resource\n\n**Template:** [Report Template Reference](references\u002Freport-template.md)\n\n---\n\n## Migration Rollout by Category\n\n- **Category B** (JSON, no SR): Producers first → consumers\n- **Category A→Header** (already on SR): Verify consumer versions → producers only\n- **Category C** (auto-register): Register via Terraform → disable auto-register → producers fetch latest\n- **Category E** (custom serializers): Consumers first (composite deserializer) → producers\n\n**Details:** [Categorization Reference](references\u002Fcategorization.md#migration-rollout-order-by-category)\n\n---\n\n## Edge Cases\n\n- **Monorepos:** Treat each service\u002Fmodule with Kafka deps as separate app\n- **Multi-topic producers:** Generate one schema resource per topic\n- **Shared schemas:** One schema file, multiple Terraform resources reference it\n- **No topic names:** If loaded from env vars, use placeholders with TODO\n- **Test code:** Skip test directories unless they contain only schema definitions\n- **Multiple serializers:** Create separate schema files per format\n\n---\n\n## Output Organization\n\n```\n{project_root}\u002F\n├── schema-report.md              # Analysis report\n├── schemas\u002F\n│   ├── schema.yaml               # Schema project config\n│   ├── avro\u002F\n│   │   └── {topic}-value.avsc\n│   ├── json\u002F\n│   │   └── {topic}-value.json\n│   └── proto\u002F\n│       └── {topic}-value.proto\n└── terraform\u002F\n    ├── providers.tf\n    ├── variables.tf\n    ├── tags.tf                    # PII\u002FPRIVATE\u002FSENSITIVE tags\n    ├── schemas.tf                 # Active schemas (depends_on tags)\n    ├── flagged-auto-register.tf   # Commented-out Category C\n    ├── outputs.tf\n    └── import.sh                  # Import existing schemas\n```\n\n---\n\n## Reference Documentation\n\n- [Detection Patterns](references\u002Fdetection-patterns.md) — Patterns for finding Kafka apps, dependencies, producers, consumers, serializers\n- [Schema Inference](references\u002Fschema-inference.md) — Extract schemas from data models, inline data, PII tagging\n- [Categorization](references\u002Fcategorization.md) — Category definitions, rollout order, client version requirements\n- [Terraform Templates](references\u002Fterraform-templates.md) — File structure, templates, naming conventions\n- [Report Template](references\u002Freport-template.md) — Required sections, formatting rules, validation checklist\n- [Code Migration](references\u002Fcode-migration.md) — Serializer\u002Fdeserializer implementation patterns for Python, Java, JavaScript, Go, and .NET\n\n---\n\n## Execution Approach\n\n1. Use **Glob** to find build files and schema files\n2. Use **Grep** for pattern detection (dependencies, producers, serializers, risks)\n3. Use **Read** to inspect source files and data models\n4. 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this skill to assess and plan a migration from AWS MSK (Managed Streaming for Apache Kafka) to Confluent Cloud. Triggers on user intent like \"migrate MSK to Confluent Cloud\", \"move off MSK\", \"MSK to CC cutover\", \"Zero-Cut migration from MSK\", \"kcp scan my MSK\", or any discussion of MSK-to-CC assessment, planning, cluster sizing, Cluster Linking setup, Gateway-based switchover, or post-cutover validation. Do NOT trigger for non-MSK Kafka sources (open-source Kafka, Aiven, Confluent Platform, Redpanda) — this skill is MSK-only. Do NOT trigger for greenfield Confluent Cloud projects with no existing Kafka source. Do NOT trigger for general Kafka programming questions (producer\u002Fconsumer code, Kafka Streams) unrelated to migration.","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmsk-migration\u002FSKILL.md",[285,286],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":287,"categoryName":288,"taxonomyType":289,"taxonomyTypeDesc":290},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[36,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310],"msk migration","assessment","code","apache","streaming","gateway","open","migrate","plan","projects","only","unrelated","intent","cc","do","cut","confluent","confluent cloud","kafka streams",{"skillNo":312,"name":313,"nameZh":6,"description":314,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":315,"categories":316,"keywords":318,"publishedAt":47},"01M6DRM48N8ZCGTA0MSNADJXJ9","kafka-streams-programming","Architect, build, and debug Kafka Streams apps (JVM-embedded stream processing). Use when user mentions KStream, KTable, topology, TopologyTestDriver, StreamsBuilder, interactive queries, GlobalKTable, joins\u002Fwindows\u002Faggregations, or debugging issues (rebalancing, state stores, lag, deserialization errors). Also use when user wants to optimize Kafka Streams for WarpStream or tune Kafka Streams client configuration for WarpStream. Do NOT trigger for Flink, connectors, CDC, or plain producer\u002Fconsumer.","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fkafka-streams-programming\u002FSKILL.md",[317],{"categoryNo":287,"categoryName":288,"taxonomyType":289,"taxonomyTypeDesc":290},[29,319,310,320,321,322,323,324,325,326,327,328,329,330,38,306,331,332,333,334],"programming","kafka streams programming","windows","debug","connectors","debugging","issues","stream processing","cdc","topology","architect","interactive","optimize","jvm","trigger","apps",{"skillNo":336,"name":337,"nameZh":6,"description":338,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":339,"categories":340,"keywords":346,"publishedAt":47},"01M6DRKR2J9MFM4HSSPJBX26SD","flink-udf","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\u002Fconsumer code.","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fflink-udf\u002FSKILL.md",[341,344,345],{"categoryNo":342,"categoryName":343,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":287,"categoryName":288,"taxonomyType":289,"taxonomyTypeDesc":290},[347,348,349,350,351,294,295,352,326,296,29,353,319,38,354,355,356,306,357,333],"flink","flink udf","docker","development","create","java","api","Connector","general","functions","local",{"skillNo":359,"name":360,"nameZh":6,"description":361,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":362,"categories":363,"keywords":365,"publishedAt":47},"01M6DRKJ5YMXKVD7BG52K68NK6","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 client configuration for WarpStream.","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fdeveloping-kafka-python-client\u002FSKILL.md",[364],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[366,29,367,349,294,28,368,369,299,370,371,38,30,372,373,331,357,32,308,309],"python","developing kafka python client","serialization","cloud","json","user","build","configuration",{"skillNo":375,"name":376,"nameZh":6,"description":377,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":378,"categories":379,"keywords":381,"publishedAt":47},"01M6DRKCFWR3TXQDYCNBPBPTCK","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\u002FKafkaConsumer\u002FKafkaShareConsumer) with Avro, JSON Schema, or Protobuf serializers. Also use when the user wants to optimize Java Kafka client configuration for WarpStream. Do NOT trigger for Kafka Streams apps (use kafka-streams-programming), Flink, connectors, or Kafka Python client (use developing-kafka-python-client).","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fdeveloping-kafka-java-client\u002FSKILL.md",[380],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[352,29,382,366,349,383,384,295,385,386,306,331,357,32,387,333,334,308,309,310],"developing kafka java client","json schema","protobuf","maven","integrate","gradle",{"skillNo":389,"name":390,"nameZh":6,"description":391,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":392,"categories":393,"keywords":397,"publishedAt":47},"01M6DRK641W03RKB050P9YDNJQ","confluent-tuning-advisor","Advisory-only Kafka tuning for Confluent Cloud or Confluent Platform by priority: latency, throughput, availability, or durability. Use when asked to size a planned topic or tune an existing cluster, topic, producer, or consumer for a quantified target such as lower p99 latency or no lost acknowledged writes. Inspects configuration read-only and recommends changes, trade-offs, and validation; the administrator applies them. Do NOT trigger for: WarpStream or Apache Kafka OSS-only clusters; Flink SQL or UDF authoring, tuning, or debugging (use confluent-cloud-flink-sql or flink-udf); Kafka Connect tuning; new Java\u002FPython client implementation (use developing-kafka-java-client or developing-kafka-python-client); Kafka Streams\u002FKStream\u002FKTable development (use kafka-streams-programming); or CDC, Tableflow, or Iceberg pipelines (use confluent-cloud-cdc-tableflow).","https:\u002F\u002Fgithub.com\u002Fconfluentinc\u002Fagent-skills\u002Fblob\u002Fmain\u002Fskills\u002Fconfluent-tuning-advisor\u002FSKILL.md",[394,395,396],{"categoryNo":342,"categoryName":343,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":287,"categoryName":288,"taxonomyType":289,"taxonomyTypeDesc":290},[398,399,308,400,401,402,403,404,405,324,406,295,407,408,353,409,410,302,357,411],"tuning","advisor","confluent tuning advisor","testing","MCP","cli","mcp server","sql","connect","iceberg","server","access","read","scripts"]