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kafka-schema-registry

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.

66

Quality

83%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

65%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-structured multi-phase skill with genuinely actionable detection patterns, an exact output layout, and excellent one-level-deep progressive disclosure to six real reference files. Its weaknesses are duplication (the directory tree and deliverables appear twice), and a conditional-only validation step for a batch file-generation workflow.

Suggestions

Make validation unconditional in Phase 5/7: replace 'Call schema_lint(...) if available' with a mandatory validate-and-fix loop (lint, fix errors, re-lint before generating Terraform), which lifts workflow_clarity on a batch-generating skill.

Remove the duplicated output directory tree — keep the 'Output Organization' section or the Phase 5/6 trees, not both — and drop the redundant restatement of deliverables in the intro.

Specify the required contents of schema.yaml (or point to a template in references/) so the 'Initialize: Create schema.yaml' step is executable rather than a name-only instruction.

DimensionReasoningScore

Conciseness

The body is mostly lean imperative bullets with no concept explanations, but it repeats itself: the full output directory tree appears twice (Phase 5/6 and 'Output Organization'), the capability summary is stated in the intro and again in 'Deliverables', and 'CRITICAL' emphasis blocks restate rules already given inline. Not 2: there is no explanation of things Claude already knows; not 4: the duplicated tree and repeated emphasis are more than minor trimming opportunities.

3 / 5

Actionability

Concrete throughout: exact grep targets ('KafkaTemplate', 'Producer(', 'value.serializer'), exact Terraform file layout, mandatory naming conventions with examples ('order-events-value.avsc'), and a lint command 'schema_lint(path: schemas/, fix: true)'. Not 5: key details are deferred without specification — e.g., 'Initialize: Create schema.yaml' never says what schema.yaml must contain, and the YAML app catalog is a placeholder skeleton rather than a filled example.

4 / 5

Workflow Clarity

Phases 0–7 are clearly numbered and ordered with a per-category rollout sequence and an Edge Cases section, but the only validation checkpoint is conditional: 'Validate: Call schema_lint(path: schemas/, fix: true) if available'. For a workflow that batch-generates schemas, Terraform, and a report across a project, there is no unconditional validate-and-fix loop, capping this at 3 per the batch-operations guideline. Not 2: the sequence itself is coherent and detailed with explicit ordering rules, far beyond a rough outline.

3 / 5

Progressive Disclosure

The body is an overview that pushes all detail to six one-level-deep reference files, each clearly signaled inline ('Detailed patterns:', 'Templates:', 'Details:') with descriptive link text, plus a consolidated 'Reference Documentation' section annotating each file's purpose. All six referenced paths (detection-patterns, schema-inference, categorization, terraform-templates, report-template, code-migration) exist in the bundle. Not 4: there are no organization gaps — no nested references, no buried pointers, no content that belongs in a reference inlined in the body.

5 / 5

Total

15

/

20

Passed

Description

92%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 that concretely enumerates five capabilities and provides an explicit, multi-trigger 'Use this skill when...' clause in third person. Its only weakness is modest synonym coverage (Avro, Protobuf, schema-registration phrasing) in the trigger terms.

DimensionReasoningScore

Specificity

The description lists five concrete, distinct actions — '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' — comprehensive coverage with no vague padding. Not 4: there are no gaps in the action inventory; not below 5 since every capability is stated concretely rather than generically.

5 / 5

Completeness

Explicitly answers both questions: the 'what' is the five-action capability list, and the 'when' is the concrete trigger clause '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'. Matches the anchor-5 example structure exactly; not 4 because the 'when' is already fully explicit with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural-phrase coverage: 'analyze a folder or repo for Kafka usage', 'audit producer/consumer configurations', 'generate Terraform for Schema Registry' are phrasings users would actually say. Not 5: a few natural synonyms and artifacts are missing — users would also say 'Avro', 'Protobuf', 'register schemas', or mention .avsc/.proto files, none of which appear.

4 / 5

Distinctiveness Conflict Risk

Clear niche at the intersection of Kafka, Confluent Schema Registry, and Terraform — triggers like 'Kafka usage', 'Schema Registry', 'producer/consumer configurations' are unlikely to fire for any other skill. Not 4: the trigger terms are domain-unique with no meaningful overlap with generic Terraform or Kafka-admin 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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