SKILLEMALL.ai
GitHub ↗ RU Sign in

CC developing-kafka-python-client

Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed serialization, or scaffold a confluent-kafka-python project for Confluent Cloud, local Docker, or WarpStream. Also use when user wants to optimize Python Kafka client configuration for WarpStream.

confluentinc/agent-skills Agent Skills author: confluentinc Apache-2.0 19 files · 11 scripts body ≈ 9 592 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Use when the user wants to build a Python Kafka producer or consumer, add Schema Registry to existing Python code, migrate from raw JSON to schema-backed…

As a process C 53/100 · Has gaps — References files that are not bundled: references/value.avsc

GeneratorDockerSoftware developmentInfrastructuretype 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
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
53/100
Has gaps
References files that are not bundled: references/value.avsc
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. The text references files that are not there: add them or drop the references.
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: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9592 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/value.avsc

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: references/value.avsc
  • 0Tools and files. 1 referenced file(s) missing: references/value.avsc
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 40Execution cost. Instruction body is 9592 tokens: crowds the task out of the window
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 77 steps, 1 vague phrases
  • 100Failures and branches. 22 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 350: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 77 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

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