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BB confluent-tuning-advisor

Advisory-only Kafka tuning for Confluent Cloud or Confluent Platform by priority: latency, throughput, availability, or durability. Use when asked to size a planned topic or tune an existing cluster, topic, producer, or consumer for a quantified target such as lower p99 latency or no lost acknowledged writes. Inspects configuration read-only and recommends changes, trade-offs, and validation; the administrator applies them. Do NOT trigger for: WarpStream or Apache Kafka OSS-only clusters; Flink SQL or UDF authoring, tuning, or debugging (use confluent-cloud-flink-sql or flink-udf); Kafka Connect tuning; new Java/Python client implementation (use developing-kafka-java-client or developing-kafka-python-client); Kafka Streams/KStream/KTable development (use kafka-streams-programming); or CDC, Tableflow, or Iceberg pipelines (use confluent-cloud-cdc-tableflow).

confluentinc/agent-skills Agent Skills author: confluentinc Apache-2.0 6 files body ≈ 4 857 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Advisory-only Kafka tuning for Confluent Cloud or Confluent Platform by priority: latency, throughput, availability, or durability.

As a process B 68/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureSoftware 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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: 5. 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 68/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4857 tokens
    • 85Steps. 34 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 7 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 top-level sections: this looks like several domains in one skill

    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)
    • +3Description length 869: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.