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BB confluent-cloud-flink-sql

Write and debug Flink SQL that runs on Confluent Cloud, enforcing the CC-vs-Apache-Flink (OSS) dialect boundary. Use when the working directory is a Confluent Cloud Flink workspace, when a Flink SQL statement needs checking before it runs on a CC compute pool, or when the user mentions CC Flink, Confluent Cloud Flink SQL, the `confluent flink` CLI, a CFU compute pool, `CREATE CONNECTION`, or asks to check or debug Flink SQL whose runtime is Confluent Cloud. Also trigger when a Flink SQL question is posed and nothing establishes an Apache Flink OSS runtime. Do NOT trigger for: building or deploying Flink UDFs in Java (UDF/UDTF/PTF — use flink-udf); a full CDC pipeline from a database through Flink into Tableflow/Iceberg/Delta Lake (use confluent-cloud-cdc-tableflow); Kafka Streams topology work (use kafka-streams-programming); or Flink SQL confirmed to run on Apache Flink OSS, not Confluent Cloud.

confluentinc/agent-skills Agent Skills author: confluentinc Apache-2.0 8 files body ≈ 3 464 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Write and debug Flink SQL that runs on Confluent Cloud, enforcing the CC-vs-Apache-Flink (OSS) dialect boundary.

As a process B 72/100 · Nearly there — weak spots: result and completion

GeneratorTerraformSoftware 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
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
B
72/100
Nearly there
Result and completion w 14
0
Tools and files w 18
60
When it triggers w 12
70
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 72/100

    • 0Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 109 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3464 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (9 tags): a typed call is more reliable
    • 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)
    • +3Description length 909: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 109 items
    • +4Reference files are cited in the instructions (6 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.