kafka-stream-processor
Kafka Stream Processor - Auto-activating skill for Data Pipelines. Triggers on: kafka stream processor, kafka stream processor Part of the Data Pipelines skill category.
36
3%
Does it follow best practices?
Impact
96%
1.00xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/11-data-pipelines/kafka-stream-processor/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is essentially a title and category label with no substantive content. It fails to describe any concrete capabilities, lacks meaningful trigger terms beyond the skill name repeated, and provides no guidance on when Claude should select this skill. It reads like auto-generated boilerplate rather than a useful skill description.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates Kafka consumer/producer configurations, designs stream processing topologies, implements message serialization/deserialization, and troubleshoots Kafka cluster issues.'
Add an explicit 'Use when...' clause with natural trigger scenarios, e.g., 'Use when the user mentions Kafka, stream processing, message queues, event streaming, consumer groups, topics, or real-time data pipelines.'
Include natural keyword variations users would actually say, such as 'Kafka consumer', 'Kafka producer', 'message broker', 'event-driven architecture', 'Kafka Connect', 'stream topology', '.avro', '.parquet'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names 'Kafka Stream Processor' and 'Data Pipelines' but provides no concrete actions. There is no indication of what the skill actually does—no verbs describing capabilities like 'processes messages', 'transforms streams', 'manages consumer groups', etc. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond naming itself, and the 'when' clause is essentially just the skill name repeated as a trigger. There is no explicit 'Use when...' guidance with meaningful trigger scenarios. | 1 / 3 |
Trigger Term Quality | The trigger terms are just 'kafka stream processor' repeated twice. Missing natural variations users would say like 'kafka consumer', 'kafka producer', 'stream processing', 'kafka topics', 'message queue', 'event streaming', etc. | 1 / 3 |
Distinctiveness Conflict Risk | The mention of 'Kafka' provides some domain specificity that distinguishes it from generic data processing skills. However, 'Data Pipelines' is broad and could overlap with other ETL or streaming skills, and the lack of specific capabilities makes it harder to distinguish from other Kafka-related skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a placeholder with no substantive content. It contains only meta-descriptions of what the skill claims to do without any actual Kafka stream processing guidance, code examples, configuration templates, or workflow steps. It provides no value beyond what Claude already knows about Kafka.
Suggestions
Add concrete, executable code examples for common Kafka Streams patterns (e.g., KStream/KTable creation, topology building, SerDes configuration) with specific library imports and copy-paste ready snippets.
Define a clear multi-step workflow for building a Kafka stream processor, including steps like topic setup, topology definition, state store configuration, deployment, and validation/monitoring checkpoints.
Replace all generic meta-descriptions ('Provides step-by-step guidance', 'Follows industry best practices') with actual technical content — specific configuration properties, common pitfalls, and concrete patterns.
Add references to supporting files for advanced topics like exactly-once semantics, windowed aggregations, or error handling strategies, rather than trying to cover everything in one file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is almost entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content about Kafka stream processing. Phrases like 'Provides step-by-step guidance' and 'Follows industry best practices' are empty padding. | 1 / 3 |
Actionability | There is zero concrete, executable guidance — no code, no commands, no configuration examples, no specific Kafka APIs or patterns. The entire skill describes rather than instructs, offering only vague promises like 'Generates production-ready code and configurations.' | 1 / 3 |
Workflow Clarity | No workflow steps are defined at all. There is no sequence of operations, no validation checkpoints, and no error recovery guidance for any Kafka stream processing task. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of generic descriptions with no references to supporting files, no structured navigation, and no separation of overview from detailed content. There are no bundle files to reference either, but the content itself doesn't even attempt to organize information progressively. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
ba8561d
- Reviewed
Table of Contents
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