interview-system-designer v1.0.0A

Immutable. This exact content is served forever at /api/v1/blob/a89aeae72a1e47fb.

---
name: interview-system-designer
description: >
  Design calibrated interview loops, competency-based question banks, and hiring calibration.
  Use when designing interview processes, creating hiring pipelines, generating scoring
  rubrics, analyzing interviewer bias, or building question banks.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: engineering
  updated: 2026-03-31
---
# Interview System Designer

The agent designs role-specific interview loops, generates competency-based question banks with scoring rubrics, and detects interviewer bias through statistical calibration analysis.

## Quick Start

```bash
# Design a complete interview loop for a senior software engineer role
python loop_designer.py --role "Senior Software Engineer" --level senior --team platform --output loops/

# Generate a question bank for a product manager position
python question_bank_generator.py --role "Product Manager" --level senior --competencies leadership,strategy,analytics --output questions/

# Analyze interview calibration across candidates and interviewers
python hiring_calibrator.py --input interview_data.json --output calibration_report.json --analysis-type full
```

---

## Core Workflows

### Workflow 1: Design an Interview Loop

1. Define role requirements (title, level, team, 3-5 critical competencies)
2. Run `loop_designer.py` with role parameters to generate rounds, time allocations, and scorecards
3. Review generated loop for competency coverage -- every required competency maps to at least one round
4. Customize interviewer skill requirements per round
5. **Validation checkpoint:** 100% competency coverage; no round exceeds 90 minutes; total loop under 6 hours

```bash
python loop_designer.py --role "Staff Data Scientist" --level staff \
  --competencies ml,statistics,leadership --format json --output loops/ds-staff.json
```

### Workflow 2: Generate a Question Bank

1. Identify target role and experience level
2. Select competency areas and question types (technical, behavioral, situational)
3. Run `question_bank_generator.py` to produce questions with scoring rubrics
4. Review for duplicate or overlapping questions across competency areas
5. **Validation checkpoint:** <15% duplicate rate; each competency has 3+ questions; calibration examples (poor/good/great) present for every question

```bash
python question_bank_generator.py --role "Frontend Engineer" \
  --competencies react,typescript,system-design --num-questions 30
```

### Workflow 3: Calibrate Hiring Bar

1. Collect interview results data (minimum 10 records for statistical significance)
2. Run `hiring_calibrator.py` with comprehensive analysis
3. Review interviewer deviation metrics -- flag anyone >0.5 standard deviations from team mean
4. Generate coaching recommendations for flagged interviewers
5. **Validation checkpoint:** Bias detection precision >80%; score distribution follows target (20/40/30/10 split)

```bash
python hiring_calibrator.py --input q1_interviews.json \
  --analysis-type comprehensive --trend-analysis --period quarterly
```

---

## Interview Loop Templates

### Software Engineering Loops

| Level | Duration | Rounds | Focus Areas |
|-------|----------|--------|-------------|
| Junior/Mid (2-4 yr) | 3-4 hours | 3-4 | Coding fundamentals, debugging, system basics, growth mindset |
| Senior (5-8 yr) | 4-5 hours | 4-5 | System design, technical leadership, mentoring, code quality |
| Staff+ (8+ yr) | 5-6 hours | 5-6 | Architecture vision, org impact, technical strategy, cross-functional leadership |

**Senior Software Engineer Example:**
1. Technical Phone Screen (45min) -- Advanced algorithms, optimization
2. System Design (60min) -- Scalability, trade-offs, architectural decisions
3. Coding Excellence (60min) -- Code quality, testing strategies, refactoring
4. Technical Leadership (45min) -- Mentoring, technical decisions, cross-team collaboration
5. Behavioral & Culture (30min) -- Leadership examples, conflict resolution

### Sample Questions by Level

**Junior:** "Implement a function to find the second largest element in an array"
**Senior:** "Design a real-time chat system supporting 1M concurrent users"
**Staff+:** "How would you evaluate and introduce a new programming language to the organization?"

**Behavioral (STAR Method):**
- "Tell me about a time you had to influence a decision without formal authority"
- "Walk me through a time when you had to make a decision with incomplete information"

---

## Scoring Rubric

### 4-Point Scale

| Score | Label | Description |
|-------|-------|-------------|
| 4 | Exceeds | Demonstrates mastery beyond required level |
| 3 | Meets | Solid performance meeting all requirements |
| 2 | Partial | Shows potential but has development areas |
| 1 | Does Not Meet | Significant gaps in required competencies |

### Calibration Benchmarks

- **Target distribution:** 20% (4s), 40% (3s), 30% (2s), 10% (1s)
- **Interviewer consistency:** <0.5 std dev from team average
- **Pass rate:** 15-25% for most roles
- **New hire correlation:** >0.6 between interview scores and 6-month performance

---

## Anti-Patterns

- **Unstandardized loops** -- different question sets per candidate prevent fair comparison; always use structured guides
- **Halo effect scoring** -- one strong answer inflates all dimensions; score each competency independently before debrief
- **Similarity bias** -- favoring candidates with similar backgrounds; require diverse panels and rotate assignments
- **Skipping calibration** -- interviewers drift over time without regular calibration sessions (monthly minimum)
- **Over-indexing on algorithms** -- testing LeetCode for a staff role that requires architecture and leadership; match round focus to actual job requirements
- **No debrief structure** -- unstructured debriefs lead to anchoring on the loudest voice; require independent score submission before group discussion

---

## Troubleshooting

| Problem | Cause | Solution |
|---------|-------|----------|
| Loop designer produces generic rounds with no role-specific focus | The `--competencies` flag was omitted, so the tool falls back to default competency mapping for the role family | Re-run with explicit `--competencies` listing the 3-5 most critical skills for the position |
| Question bank output has too many behavioral questions and too few technical ones | The `--question-types` flag was not provided, causing the generator to use a balanced default split | Supply `--question-types technical,system-design` (or whichever mix is needed) to control the ratio |
| Hiring calibrator reports "insufficient data" for bias detection | The input JSON contains fewer than 10 interview records, which is below the statistical minimum | Collect more interview data before running bias analysis; use `--analysis-type scoring` for small datasets |
| Calibrator trend analysis returns empty results | The input data lacks date fields or all records fall within a single period | Ensure each interview record has a valid date field and that the dataset spans multiple periods matching `--period` |
| Loop designer ignores the `--team` flag | The team value does not match any of the predefined team mappings in the tool | Check supported team names in the tool's `TEAM_CONFIGS` dictionary, or omit `--team` and rely on competency overrides |
| Score distribution chart shows all interviewers clustered at the same score | Interviewers are not applying the full 1-4 rubric scale (central tendency bias) | Run `--analysis-type calibration` to identify leniency/severity patterns and use the coaching recommendations |
| Question bank generates duplicate questions across competency areas | Overlapping competency keywords (e.g., "leadership" appears in both behavioral and technical mappings) | Use more specific competency terms or reduce `--num-questions` to avoid exhausting the unique question pool |

---

## Success Criteria

- **Interview loop coverage:** Every generated loop maps 100% of required competencies to at least one round with a dedicated scoring dimension.
- **Question bank diversity:** Generated banks contain no more than 15% duplicate or near-duplicate questions across competency areas.
- **Calibration detection accuracy:** Bias detection flags interviewer score deviation greater than 0.5 standard deviations from the team mean with at least 80% precision.
- **Time-to-design reduction:** Designing a complete interview loop (rounds, scorecards, question sets) takes under 10 minutes compared to the typical 2-4 hours of manual design.
- **Rubric consistency:** Generated scoring rubrics achieve inter-rater reliability (Cohen's kappa) of 0.7 or higher when tested with calibration panels.
- **Candidate experience alignment:** Loops designed with this tool target a candidate experience satisfaction score of 4.0/5.0 or above.
- **Hiring quality signal:** Organizations using the calibrator report a correlation of 0.6 or higher between interview scores and 6-month performance reviews.

---

## Scope & Limitations

**This skill covers:**
- Designing end-to-end interview loops for engineering, product, design, and data roles across all seniority levels (junior through principal)
- Generating competency-based question banks with structured scoring rubrics and calibration examples
- Detecting statistical bias and calibration drift across interviewers and time periods
- Producing scorecard templates, debrief guides, and interviewer assignment recommendations

**This skill does NOT cover:**
- Applicant tracking system (ATS) integration, job posting, or candidate sourcing pipeline management — see `hr-operations/talent-acquisition`
- Compensation benchmarking, offer negotiation strategy, or total rewards analysis — see `hr-operations/hr-business-partner`
- Workforce planning, headcount modeling, or organizational design — see `hr-operations/people-analytics`
- Post-hire onboarding program design or new-hire ramp-up tracking — see `engineering/codebase-onboarding`

---

## Integration Points

| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| `hr-operations/talent-acquisition` | Feed designed interview loops and scorecards into the talent acquisition pipeline for end-to-end hiring execution | Loop JSON output → talent acquisition workflow input |
| `hr-operations/people-analytics` | Supply calibration reports and interviewer performance data for workforce-level hiring analytics | Calibrator JSON reports → people analytics dashboards |
| `engineering/codebase-onboarding` | Hand off hired candidate profiles and assessed competency gaps to onboarding plan generation | Scorecard results → onboarding skill-gap inputs |
| `hr-operations/hr-business-partner` | Provide interview quality metrics and pass-rate data to support hiring bar discussions with HR leadership | Calibration trend data → HRBP quarterly reviews |
| `product-team` | Align PM interview loop competencies with the product team's competency frameworks and role leveling guides | Competency matrix → PM loop designer `--competencies` input |
| `engineering/pr-review-expert` | Use coding round evaluation criteria to inform code review standards for new hires during their ramp period | Scoring rubric technical criteria → PR review checklist alignment |

---

## Tool Reference

### loop_designer.py

**Purpose:** Generates calibrated interview loops tailored to specific roles, levels, and teams. Produces complete loops with rounds, focus areas, time allocation, interviewer skill requirements, and scorecard templates.

**Usage:**
```bash
python loop_designer.py --role "Senior Software Engineer" --level senior --team platform --output loops/
```

**Flags/Parameters:**

| Flag | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| `--role` | `str` | No | — | Job role title (e.g., "Senior Software Engineer") |
| `--level` | `str` | No | — | Experience level: `junior`, `mid`, `senior`, `staff`, `principal` |
| `--team` | `str` | No | — | Team or department name (optional context for loop customization) |
| `--competencies` | `str` | No | — | Comma-separated list of specific competencies to focus on |
| `--input` | `str` | No | — | Input JSON file with role definition |
| `--output` | `str` | No | — | Output directory or file path |
| `--format` | `str` | No | `both` | Output format: `json`, `text`, or `both` |

**Example:**
```bash
python loop_designer.py --role "Staff Data Scientist" --level staff --competencies ml,statistics,leadership --format json --output loops/ds-staff.json
```

**Output Formats:**
- **JSON:** Structured loop definition with rounds array, competency mappings, time allocations, and scorecard templates suitable for programmatic consumption.
- **Text:** Human-readable interview guide with formatted round descriptions, interviewer requirements, and evaluation criteria.
- **Both (default):** Writes both JSON and text outputs to the specified directory.

---

### question_bank_generator.py

**Purpose:** Generates comprehensive, competency-based interview questions with detailed scoring criteria, follow-up probes, and calibration examples organized by competency area.

**Usage:**
```bash
python question_bank_generator.py --role "Frontend Engineer" --competencies react,typescript,system-design --output questions/
```

**Flags/Parameters:**

| Flag | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| `--role` | `str` | No | — | Job role title (e.g., "Frontend Engineer") |
| `--level` | `str` | No | `senior` | Experience level: `junior`, `mid`, `senior`, `staff`, `principal` |
| `--competencies` | `str` | No | — | Comma-separated list of competencies to focus on |
| `--question-types` | `str` | No | — | Comma-separated list of question types: `technical`, `behavioral`, `situational` |
| `--num-questions` | `int` | No | `20` | Number of questions to generate |
| `--input` | `str` | No | — | Input JSON file with role requirements |
| `--output` | `str` | No | — | Output directory or file path |
| `--format` | `str` | No | `both` | Output format: `json`, `text`, or `both` |

**Example:**
```bash
python question_bank_generator.py --role "Product Manager" --level mid --question-types behavioral,situational --num-questions 30 --format text
```

**Output Formats:**
- **JSON:** Array of question objects each containing the question text, competency area, difficulty level, scoring rubric (1-4 scale), follow-up probes, and calibration examples (poor/good/great answers).
- **Text:** Formatted question bank grouped by competency with inline scoring guidance and example answers for interviewer reference.
- **Both (default):** Writes both JSON and text outputs to the specified directory.

---

### hiring_calibrator.py

**Purpose:** Analyzes interview scores from multiple candidates and interviewers to detect bias, calibration issues, and inconsistent rubric application. Generates calibration reports with recommendations for interviewer coaching and process improvements.

**Usage:**
```bash
python hiring_calibrator.py --input interview_results.json --analysis-type comprehensive --output report.json
```

**Flags/Parameters:**

| Flag | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| `--input` | `str` | **Yes** | — | Input JSON file with interview results data |
| `--analysis-type` | `str` | No | `comprehensive` | Analysis type: `comprehensive`, `bias`, `calibration`, `interviewer`, `scoring` |
| `--competencies` | `str` | No | — | Comma-separated list of competencies to focus on |
| `--trend-analysis` | flag | No | `false` | Enable trend analysis over time |
| `--period` | `str` | No | `monthly` | Trend period: `daily`, `weekly`, `monthly`, `quarterly` |
| `--output` | `str` | No | — | Output file path |
| `--format` | `str` | No | `both` | Output format: `json`, `text`, or `both` |

**Example:**
```bash
python hiring_calibrator.py --input q1_interviews.json --analysis-type bias --competencies technical,leadership --trend-analysis --period quarterly --format json --output calibration/q1_bias.json
```

**Output Formats:**
- **JSON:** Structured calibration report containing score distributions, interviewer deviation metrics, bias indicators, trend data (if enabled), and prioritized coaching recommendations.
- **Text:** Human-readable report with summary statistics, flagged interviewers, bias findings, and actionable improvement recommendations formatted for management review.
- **Both (default):** Writes both JSON and text outputs to the specified path.