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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.

76

Quality

95%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-engineered skill body: mode detection with three clearly sequenced workflows, embedded validation checkpoints and error-recovery loops, exact commands/config keys, and an aggressive progressive-disclosure structure whose references all resolve. The only weakness is minor duplication in the Build Step 6 environment branches and the repeated lazy-load injunctions.

Suggestions

In Build Mode Step 6, merge the Confluent Cloud and WarpStream branches into one shared 'remote cluster without creds' procedure with a short WarpStream-specific note (longer RUNNING transition, ws_az reminder) instead of repeating the full handoff block twice.

Tighten the ⚠️ lazy-load section to a single injunction plus the intent→file mapping table; the current 'Do NOT read all reference files upfront' and 'Never read multiple files preemptively' lines say the same thing twice.

The preamble says 'not all 10' reference files but the bundle contains 11 — update the count or drop the numeral.

DimensionReasoningScore

Conciseness

Largely lean — tables, imperative checklists, specific config keys, and no explanations of concepts Claude already knows (the single framing line 'JVM-embedded stream processing library with no separate cluster' is non-obvious and earns its place). However, the Confluent Cloud and WarpStream branches of Build Step 6 near-duplicate the same 'do not fabricate a successful run' handoff block, and the ⚠️ lazy-load section repeats its injunction twice ('Do NOT read all reference files upfront... Never read multiple files preemptively'), so a little tightening is possible.

4 / 5

Actionability

Fully executable instruction-level guidance: exact commands (`docker compose up -d`, `./gradlew run`, `gradle wrapper --gradle-version 8.12`, `./create-topics.sh --cloud`), exact config properties (`group.protocol=streams`, `statestore.cache.max.bytes=0`, `ws_az=<az>`), and exact success markers (`State transition from REBALANCING to RUNNING` within ~30s). As an instruction-only skill with templates delegated to real reference files, no code absence is penalized.

5 / 5

Workflow Clarity

Three modes selected via an explicit intent table, numbered step sequences per mode, and a mandatory run-before-handoff step with explicit validation checkpoints and a feedback loop ('If you don't see it, read the actual stack trace, diagnose via references/debugging.md § Startup Failures, fix, restart, re-verify'), plus honest-reporting instructions covering each environment branch including the can't-run cases.

5 / 5

Progressive Disclosure

Exemplary on-demand structure: an explicit lazy-load policy mapping user intents to specific reference sections, per-task read triggers in Build mode, and a one-level-deep reference index. All 11 referenced files exist, and every cited section (§ Joins Decision Tree, § Startup Failures, § Thread Failures, § Memory Issues, § Kafka Streams Specific, § Assignment Strategy, § Java type mapping) verified present in the bundle.

5 / 5

Total

19

/

20

Passed

Description

95%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: concrete domain-scoped actions, an explicit 'Use when' clause with natural API-class and symptom trigger terms, a WarpStream extension clause, and explicit exclusion triggers for adjacent tools. The only weakness is that the top-level verbs (architect/build/debug) are less concretely enumerated than the best anchor examples.

DimensionReasoningScore

Specificity

Lists several specific actions ("Architect, build, and debug Kafka Streams apps", "optimize Kafka Streams for WarpStream or tune Kafka Streams client configuration") grounded in a named domain, but the primary verbs are higher-level than anchor 5's fully enumerated concrete operations (e.g. extract text, fill forms, merge documents), leaving minor gaps in what 'build' concretely covers.

4 / 5

Completeness

Explicitly answers both 'what' ("Architect, build, and debug Kafka Streams apps") and 'when' ("Use when user mentions KStream, KTable, topology...") with concrete trigger phrases and a second use-when clause for WarpStream optimization.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms users would actually say: KStream, KTable, topology, TopologyTestDriver, StreamsBuilder, interactive queries, GlobalKTable, joins/windows/aggregations, rebalancing, state stores, lag, deserialization errors, WarpStream — covering API class names, concepts, and symptom vocabulary with no notable synonyms missing for this domain.

5 / 5

Distinctiveness Conflict Risk

Clear niche (JVM-embedded Kafka Streams) with explicit negative triggers ("Do NOT trigger for Flink, connectors, CDC, or plain producer/consumer") that sharply separate it from adjacent stream-processing and Kafka-client skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
confluentinc/agent-skills
Reviewed

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