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BC 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/windows/aggregations, 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/consumer.

confluentinc/agent-skills Agent Skills author: confluentinc Apache-2.0 17 files · 4 scripts body ≈ 3 756 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Architect, build, and debug Kafka Streams apps (JVM-embedded stream processing).

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions

GeneratorDockerKubernetesSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration read-dotenv scripts/create-topics.sh:32
      Reads a .env file
      source .env

    Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Steps. 57 steps, 5 vague phrases
    • 100Failures and branches. 11 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3756 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low No test case covers injection arriving through data

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 503: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 57 items
    • +4Reference files are cited in the instructions (11 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.