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AD senior-data-engineer

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.

ClawHub Agent Skills author: wu-uk v0.1.0 MIT-0 14 files body ≈ 5 367 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureDockerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
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.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • 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: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5367 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "domain"
  • note frontmatter-key unknown frontmatter key "subdomain"
  • note frontmatter-key unknown frontmatter key "difficulty"
  • note frontmatter-key unknown frontmatter key "time-saved"
  • note frontmatter-key unknown frontmatter key "frequency"
  • note frontmatter-key unknown frontmatter key "use-cases"
  • note frontmatter-key unknown frontmatter key "related-agents"
  • note frontmatter-key unknown frontmatter key "related-skills"
  • note frontmatter-key unknown frontmatter key "related-commands"
  • note frontmatter-key unknown frontmatter key "orchestrated-by"
  • note frontmatter-key unknown frontmatter key "dependencies"
  • note frontmatter-key unknown frontmatter key "tech-stack"
  • note frontmatter-key unknown frontmatter key "examples"
  • note frontmatter-key unknown frontmatter key "stats"
  • note frontmatter-key unknown frontmatter key "contributors"
  • note frontmatter-key unknown frontmatter key "created"
  • note frontmatter-key unknown frontmatter key "updated"
  • note frontmatter-key unknown frontmatter key "featured"
  • note frontmatter-key unknown frontmatter key "verified"

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 40Consistency. Frontmatter name (senior-data-engineer) differs from the folder (flink-query-senior-data-engineer)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5367 tokens
  • 85Steps. 177 steps, 1 vague phrases
  • 100Running it twice. Mutating operations check current state
  • 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 476: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 177 items
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 6 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.

External checks

ClawHub: suspicious
This data-engineering skill is not clearly malicious, but it needs review because some production-style monitoring and security templates are under-scoped or misleading.
LLM: suspicious (high) · VirusTotal: · 29 May 2026