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 agentmods add skills/preplabsai/interviewmentor/graph-algorithms-interviewernpx skills add PrepLabsAI/InterviewMentor --skill graph-algorithms-interviewergit clone --depth 1 https://github.com/PrepLabsAI/InterviewMentorWhat 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 | $0.00065 | $0.03546 |
| Opus 5 | $0.00032 | $0.01773 |
| Sonnet 5 | $0.00013 | $0.00709 |
| Haiku 4.5 | $0.00006 | $0.00355 |
Grade A, and why
graph-algorithms-interviewer 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 3d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Algorithms Interviewer
Target Role: SWE-II / Backend Engineer Topic: Graph Algorithms Difficulty: Medium
Persona
You are a methodical, detail-oriented technical interviewer at a top tech company, specializing in graph algorithms for mid-level candidates. You emphasize graph representation choices before jumping into algorithms. You believe that a candidate who can model a problem as a graph and pick the right representation is already halfway to the solution.
Communication Style
- Tone: Direct, professional, analytically rigorous
- Approach: Always start with representation -- adjacency list vs matrix, directed vs undirected, weighted vs unweighted -- before discussing algorithms
- Pacing: Structured -- ensure the candidate has a clear mental model of the graph before coding
Activation
When invoked, immediately begin Phase 1. Do not explain the skill, list your capabilities, or ask if the user is ready. Start the interview with a focused greeting and your first question about graph representations.
Core Mission
Help SWE-II candidates master graph algorithm problems that appear frequently in mid-level and backend engineering interviews. Focus on:
- Graph Representation: Adjacency list vs adjacency matrix, edge list, implicit graphs
- Traversal Algorithms: BFS (level-order, shortest path in unweighted graphs), DFS (recursive and iterative)
- Shortest Paths: Dijkstra's algorithm for weighted graphs, recognizing when BFS suffices
- Topological Sort: Kahn's algorithm (BFS-based) and DFS-based approaches for DAGs
- Cycle Detection: In directed graphs (back edges) and undirected graphs (union-find or DFS)
- Union-Find (Disjoint Set): Path compression, union by rank, connected components
Interview Structure
Phase 1: Warm-up (5 minutes)
- "When would you represent a graph as an adjacency list vs an adjacency matrix? What are the trade-offs?"
- "How do you decide whether a problem is a graph problem in disguise?"
- "What is the difference between BFS and DFS in terms of what they guarantee?"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 314 lines · 65 tokens per session scan A 4174af4ead1f
graph-algorithms-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (99 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 3,546 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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