Borrowing it
Nothing to install: this file belongs to aviraldua93/architect-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aviraldua93/architect-ai/main/.claude/commands/exam.mdgit clone --depth 1 https://github.com/aviraldua93/architect-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/aviraldua93/architect-ai/exam)<a href="https://agentmods.dev/commands/aviraldua93/architect-ai/exam"><img src="https://agentmods.dev/badge/commands/aviraldua93/architect-ai/exam.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00396 |
| Opus 5 | $0.00000 | $0.00198 |
| Sonnet 5 | $0.00000 | $0.00079 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
exam scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Mock Exam
Simulate a full Claude Certified Architect (CCA-F) exam.
Usage
/exam — Full 60-question timed exam (120 min)
/exam --no-timer — Practice mode without countdown
/exam -q 10 — Quick practice exam with 10 questions
What It Does
- Selects questions weighted by official CCA-F domain percentages:
- Domain 1 (Agentic Architecture): 27% → 16 questions
- Domain 2 (Tool Design & MCP): 18% → 11 questions
- Domain 3 (Claude Code Config): 20% → 12 questions
- Domain 4 (Prompt Engineering): 20% → 12 questions
- Domain 5 (Context & Reliability): 15% → 9 questions
- Starts a 120-minute countdown timer (unless
--no-timer) - Shows only ✓/✗ after each answer — no explanations until the end
- At completion (or timeout), shows:
- Scaled score out of 1000
- PASS/FAIL verdict (threshold: 720/1000)
- Per-domain breakdown with bar charts
- Time used vs time allowed
- Weakest domain recommendation
- Saves results to
~/.architect-ai/exams/
Running Directly
npx tsx src/cli/exam.ts # Full exam
npx tsx src/cli/exam.ts --no-timer # No timer
npx tsx src/cli/exam.ts --no-timer --questions 5 # Quick 5-question practice
npx tsx src/cli/index.ts exam --no-timer -q 10 # Via CLI entry point
Passing Score
720/1000 (approximately 72%)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 43 lines · 0 tokens per session scan A 3d44a908fd2d
exam is a command published in the GitHub repository aviraldua93/architect-ai (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 396 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
audit-quiz-coverage
Find quiz coverage gaps from recent guide/CHANGELOG/CC-releases changes and propose new questions.
learn
Learn new capabilities through experimentation, then codify into the system.
ccc-compound
Post-task learning capture — extract patterns, corrections, and decisions to compound productivity.
explain
Explain the subject in depth using the fixed four-part structure: What, Why, Example, Consequences. Treat the subject as the thing to be explained, not as a literal question.
interview
Prepare for the interview from the filed application — predicted questions, STAR answers from real evidence, and gap-defence.
learn
Capture a reusable learning from this session — with a dedup + Save/Absorb/Drop gate so the knowledge base stays clean.