Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jamestorrevillas/dev-skills --skill interview-prepgit clone --depth 1 https://github.com/jamestorrevillas/dev-skillsWrote 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/skills/jamestorrevillas/dev-skills/interview-prep)<a href="https://agentmods.dev/skills/jamestorrevillas/dev-skills/interview-prep"><img src="https://agentmods.dev/badge/skills/jamestorrevillas/dev-skills/interview-prep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jamestorrevillas/dev-skills/interview-prep"><img src="https://agentmods.dev/badge/skills/jamestorrevillas/dev-skills/interview-prep.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00061 | $0.00933 |
| Opus 5.5 | $0.00024 | $0.00373 |
| Sonnet 5.5 | $0.00012 | $0.00187 |
| Haiku 4.5 | $0.00006 | $0.00093 |
Grade A, and why
interview-prep 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Preparation
Interview Types & What They Test
| Type | What They're Testing | How to Prepare |
|---|---|---|
| Coding / DSA | Problem-solving, CS fundamentals | LeetCode patterns, not random problems |
| System Design | Architecture thinking, trade-offs | Practice frameworks, read real systems |
| Behavioral | Past behavior predicts future | STAR stories for 6-8 scenarios |
| Technical Discussion | Depth of knowledge, communication | Know your stack deeply, read vs write trade-offs |
Coding Interview: Pattern-First Approach
Don't grind random problems. Learn patterns:
| Pattern | When to Use |
|---|---|
| Two Pointers | Sorted arrays, palindromes, pair sums |
| Sliding Window | Subarray problems, longest/shortest window |
| Fast & Slow Pointers | Cycle detection, linked list middle |
| BFS / DFS | Trees, graphs, shortest path |
| Binary Search | Sorted data, "find in X" problems |
| Dynamic Programming | Overlapping subproblems, optimization |
| Heap / Priority Queue | K largest/smallest, streaming data |
Problem-Solving Template
1. Clarify: "Can I have duplicates? What's the input range? What to return if empty?"
2. Example: Walk through a small example out loud
3. Brute force: State the naive solution first
4. Optimize: "I can improve this by..."
5. Code: Write clean, named code
6. Test: Trace through your example + edge cases
7. Complexity: State time and space complexity
System Design Interview Framework
1. Clarify requirements (2-3 min)
- Functional: What does it do?
- Scale: Users, requests/sec, data volume
- Non-functional: Latency, availability, consistency
2. Estimate scale (2 min)
- Back-of-envelope: storage, throughput, bandwidth
3. High-level design (5-10 min)
- Draw main components and their relationships
- Define the API
4. Deep dive (10-15 min)
- Zoom into the hardest part
- Address bottlenecks proactively
5. Bottlenecks & trade-offs (5 min)
- What would fail at scale?
- What did you consciously trade off?
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.
- 1mo ago First seen · 121 lines · 0 tokens per session scan A 93d38c2bf175
interview-prep is a skill published in the GitHub repository jamestorrevillas/dev-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 61 tokens to every session and 933 once invoked, about $0.0002 per session on Opus 5.5. 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 skills, from other repositories
coaching-techniques
GROW model, active listening, developmental feedback, and team growth approaches.
counseling-psychology
Therapeutic frameworks, assessment, ethical practice, and client documentation for counselors and psychologists.
cognitive-load
Don't overwhelm — chunk, scaffold, summarize first.
learning-psychology
Help humans learn through partnership, not instruction.
academic-paper-reviewer
Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on…
principal-agent
A Chinese-language guide based on the principal-agent idea: the person who owns a decision or its result may have different incentives from someone acting on another person's behalf. It applies this lens to jobs, companies, and partnerships.