AC data-engineering-interview-coach
An interactive data engineering interview coach that drills senior-level data engineering knowledge through a coaching-style mock interview — one question at a time, waits for the answer, then teaches through feedback. Covers SQL (advanced), data modeling, data pipelines, batch vs streaming, dbt, Apache Spark, Airflow, Kafka, data warehouse design, lake house architecture, data quality, observability, and performance optimization. Designed for senior software engineers transitioning into or leveling up for data engineering roles. Trigger for requests like "interview me on data engineering", "quiz me on SQL", "test my pipeline knowledge", "data engineering mock interview", "ask me dbt questions", or "drill me on Spark".
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 3. 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 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1484 tokens
- 100Progress reporting. Reports progress
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 728: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.