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Design calibrated interview loops, competency-based question banks, and hiring calibration.
Passed 19 of 22 test cases with the skill, and 4 of 22 without it.
and 15 more in the full report
15 fixed4 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
We want to run a CLI script to create an interview loop for a Senior Software Engineer on the platform team and save the output files into the `loops/` directory. Which exact command line execution should we run?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies running python loop_designer.py --role "Senior Software Engineer" --level senior --team platform --output loops/. | Missed | Passed |
The instructions your model reads when the skill loads.
Design role-specific interview loops, generate competency-based question banks with scoring rubrics, and detect interviewer bias through statistical calibration analysis.
Before designing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
loop_designer.py vs question_bank_generator.py vs hiring_calibrator.py)--role/--level)--competencies and the question-bank scope)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
The Python tools live at the skill root (not in scripts/). All support --help, JSON/text output.
| Tool | Purpose | Command |
|---|---|---|
loop_designer.py | Generate a calibrated interview loop (rounds, time, scorecards) | python loop_designer.py --role "Senior Software Engineer" --level senior --team platform --output loops/ |
question_bank_generator.py | Generate competency-based questions with rubrics + calibration examples | python question_bank_generator.py --role "Frontend Engineer" --competencies react,typescript,system-design --num-questions 30 |
hiring_calibrator.py | Detect bias/calibration drift across interviewers and periods | python hiring_calibrator.py --input interview_data.json --analysis-type comprehensive --trend-analysis |
test cases passed, vs 4 / 22 without
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