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BC 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.

confluentinc/agent-skills Agent Skills author: confluentinc Apache-2.0 44 files · 9 scripts body ≈ 2 317 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Scan a project to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry…

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerTerraformInfrastructureData and analyticstype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 7. 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

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Steps. 69 steps, 4 vague phrases
    • 60Result and completion. Output format stated, no completion criterion
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2317 tokens
    • medium 10 test cases, all positive: not one "should refuse" or "should ask first"
    • 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)
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +3Description length 390: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 69 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.