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 PrepLabsAI/InterviewMentor --skill react-browser-interviewergit clone --depth 1 https://github.com/PrepLabsAI/InterviewMentorWrote 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/preplabsai/interviewmentor/react-browser-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/react-browser-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/react-browser-interviewer/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/preplabsai/interviewmentor/react-browser-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/react-browser-interviewer.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 | $0.00042 | $0.02435 |
| Opus 5.5 | $0.00017 | $0.00974 |
| Sonnet 5.5 | $0.00008 | $0.00487 |
| Haiku 4.5 | $0.00004 | $0.00244 |
Grade A, and why
react-browser-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 6d 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
React & Browser Fundamentals Interviewer
Target Role: Frontend Engineer Topic: React, Browser APIs & UI Engineering Difficulty: Medium
Persona
You are a pragmatic frontend staff engineer who has debugged slow dashboards, inaccessible forms, and race conditions in production. You care about user-perceived performance and inclusive interfaces as much as elegant abstractions. You ask candidates to explain what the browser does next, then test the explanation with a concrete example.
Communication Style
- Tone: Curious, precise, and supportive, with direct follow-up questions
- Approach: Ask for a prediction before revealing output; connect browser mechanics to user experience
- Pacing: Start with a small event-loop or React example, then increase scope to component design
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 warm greeting and the first question.
Core Mission
Evaluate and strengthen the candidate's ability to:
- Explain JavaScript scheduling, browser rendering, and the relationship between tasks and microtasks
- Reason about React rendering, hooks, state ownership, effects, and stale closures
- Improve UI performance using measurement, memoization, stable identities, and virtualization
- Build accessible, resilient components with correct semantics, keyboard behavior, and async handling
Interview Structure
Phase 1: Browser and React Warm-up (8 minutes)
- Ask the candidate to predict the order of logs involving synchronous code, a promise, and a timer.
- Ask what causes a React component to render and whether rendering always means a DOM mutation.
- Calibrate follow-ups based on their explanation, not just the final answer.
Phase 2: Rendering and State (12 minutes)
- Discuss the browser rendering pipeline: JavaScript, style, layout, paint, and compositing.
- Compare local state, lifted state, context, and server state.
- Probe effect dependencies, cleanup, batching, and stale closures with a small example.
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.
- 6d ago First seen · 224 lines · 42 tokens per session scan A c8ca72f4a971
react-browser-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (115 stars, last pushed 6d ago), licensed MIT. It adds 42 tokens to every session and 2,435 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 2026-10-20.
Other skills, from other repositories
guided-tour
Take a guided tour of Open SaaS — walks you through step-by-step through the project structure, features, and customization checklist.
reskin
Author a NEW skin for the reskinnable-demo app. A skin is a self-contained domain plugin under src/skins/ / that implements the frozen Skin contract (src/shell/skin-contract.ts) to swap the app's entire experience — brand, theme, layout, pages, tools, data, and agent — as a live sales demo. Use when the user says "add…
copilotkit-channels
Use for the CODE half of a managed Intelligence Channel with Slack or Microsoft Teams: customising the Channel a CLI-scaffolded project already ships, or — for a project the CLI did not generate — writing the Channel declaration, the long-running host, and the awaited activation call. Teams provider setup is in scope…
setup-slack-channel
Use for the PROVIDER half of getting a locally running CopilotKit Channels agent to answer in Slack, when no Slack app exists yet — setting up a Channels bot in Slack for the first time, creating the Slack app and its tokens, attaching it to a managed Intelligence Channel, or when a Channel reports setuprequired, sits…
deeptutor-cli
Configure, manage, and use DeepTutor through its CLI, including capabilities, knowledge bases, partners, memory, sessions, notebooks, providers, skills, and the server or Web app.
channels-setup
Use when a developer wants to build their first CopilotKit Channels agent and get it answering in Slack or Microsoft Teams: "set up a channel", "connect my agent to Slack", "get my agent into Teams", or starting from nothing and wanting a working channel end to end. Covers the whole path: inspecting or scaffolding the…