# Mock Assessing

> Simulates technical assessments calibrated to real AI engineer interview patterns from 100+ analyzed take-home assignments and hiring processes. Triggers on /assess, test me, or mock interview. Three modes -- quick (15 min), milestone (45 min), and interview (4-round simulation). Scoring is ruthless. 8/10 means hireable.

- Skill: `aman-bhandari/mock-assessing` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add aman-bhandari/mock-assessing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/aman-bhandari/mock-assessing/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: aman-bhandari (https://skillmd.com/u/aman-bhandari)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/aman-bhandari/mock-assessing

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# Mock Assessment -- Interview-Calibrated Testing

Tests calibrated to what companies actually ask. Based on analysis of 100+ take-home assignments and interview rounds.

## Trigger

- `/assess placement` -- placement test to skip mastered topics
- `/assess quick` -- 15-minute concept check
- `/assess milestone` -- 45-minute milestone-calibrated assessment
- `/assess interview` -- 4-round full simulation

## Assessment Modes

### Placement (20-30 min per topic)

Determines if a student can skip a topic they already know. **Use before starting the curriculum or when an experienced developer joins.**

**Process:**
1. Student declares which topic(s) they want to test out of.
2. For each topic, ask 5 questions covering the full exercise range:
   - 2 concept recall (from memory, no lookup)
   - 1 design question (architect a solution using that topic's patterns)
   - 1 debugging question (here's broken code/config, what's wrong?)
   - 1 production question (how does this work at scale / in production?)
3. Score using the standard rubric.

**Skip criteria:**
- **8/10+:** Skip the topic entirely. Record in PROGRESS.md: `"Topic N: PLACED OUT (score X/10, {date})"`
- **6-7/10:** Skip exercises 1-5 (fundamentals), start at exercise 6+ (intermediate). Record partial placement.
- **5/10 or below:** Start from exercise 1. No skip.

**Rules:**
- Placement is topic-level, not exercise-level. You can't skip individual exercises within a topic (except with 6-7 partial placement).
- Questions must cover the FULL topic range -- don't just test the easy exercises.
- Be ruthless. A senior Python developer who can't explain `asyncio.gather` vs `asyncio.wait` doesn't skip Topic 2.
- Record all placement results in `assessments/{date}-placement-topic-{N}.md`.
- If the student barely passes (exactly 8/10), warn them: "You passed, but the knowledge is thin. Consider doing the milestone project anyway."

### Quick (15 min)

3 concept recall questions + 1 design question. No code.

### Milestone (45 min)

Mirrors real take-home assignments. See [ASSIGNMENTS.md](ASSIGNMENTS.md) for topic-specific assessment details.

### Interview (4 rounds)

Full simulation matching real hiring:
1. **Technical screen:** Python fundamentals + LLM basics
2. **System design:** Design a RAG/agent system for a use case
3. **Code review:** Review provided code, find issues, suggest fixes
4. **Behavioral:** "Tell me about a system you built" (prior agentic AI projects)

## Execution

1. Select assessment type (or ask the student).
2. Generate questions calibrated to current topic + cumulative knowledge.
3. One question at a time. Note time but don't hard-enforce.
4. Evaluate against rubric in [RUBRIC.md](RUBRIC.md).
5. Output results:
   ```
   ASSESSMENT -- {type} -- {date}

   Score: X/10
   Strengths: [specific]
   Gaps: [specific, actionable]
   Market readiness: [honest vs. job requirements]
   Next focus: [what to work on]
   ```
6. Save to `assessments/{date}-{type}.md` for tracking.

## Rules

- 8/10 from this system means "would pass a real interview round."
- Don't soften feedback. "You would not pass this round" is valid.
- Connect every question to real market requirements.
- Track scores over time -- they should trend up.
- Cross-reference with spaced-review deck to find systematic gaps.

## Outcome Tracking (Calibration)

Our scoring is only useful if it predicts real-world outcomes. After any real interview, take-home, CCA exam, or job offer:

1. Record the outcome in `assessments/OUTCOME-TRACKING.md`
2. Compare against our last assessment score for that topic
3. After 5+ entries, recalibrate if prediction accuracy drops below 80%

See [OUTCOME-TRACKING.md](../../assessments/OUTCOME-TRACKING.md) for the full calibration protocol.