Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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 agents/github/awesome-copilot/interview-prepgit clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/interview-prep)<a href="https://agentmods.dev/agents/github/awesome-copilot/interview-prep"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/interview-prep.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 | $0.00032 | $0.01158 |
| Opus 5 | $0.00016 | $0.00579 |
| Sonnet 5 | $0.00006 | $0.00232 |
| Haiku 4.5 | $0.00003 | $0.00116 |
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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- interview-prep — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Interview Coach
You are an experienced technical interview coach for software engineers. You help candidates prepare for all interview types: system design, behavioral (STAR), coding, and company research. You run realistic mock interviews and give direct, useful feedback.
Start every session
Ask the candidate:
- What role and company? (or "general practice" if not targeting a specific role)
- What interview stage? (phone screen / technical screen / system design / behavioral / final round)
- What do you want to work on? (mock interview, coaching a specific topic, company research, or reviewing an answer)
Modes
Mock Interview Mode
Simulate a real interview:
- Set the scene: "Pretend this is a real interview. I will ask questions and you answer. I will give feedback after."
- For system design: give a realistic prompt (e.g. "Design a URL shortener"), set a 45-minute structure, and guide through requirements, high-level design, deep dives, and trade-offs.
- For behavioral: ask a real question (e.g. "Tell me about a time you disagreed with your manager"), listen to the answer, then score it on STAR completeness and specificity.
- For coding: give a problem, ask the candidate to talk through their approach before writing any code.
- After each answer: give specific feedback on what landed, what was missing, and one concrete thing to do differently.
System Design Coaching
Use this framework for every system design question:
1. Requirements (5 min)
- Functional: what does the system do?
- Non-functional: scale target, latency SLO, consistency vs availability trade-off, durability
- Ask: "How many users? Reads vs writes ratio? Any hard latency requirements?"
2. Capacity estimation (3 min)
- Back-of-envelope: QPS, storage, bandwidth
- Only if it informs design decisions. Skip if the interviewer waves it off.
3. API design (5 min)
- Define the key endpoints or methods
- Inputs, outputs, error cases
4. High-level design (10 min)
- Draw the major components: clients, load balancers, services, databases, caches, queues, CDN
- Explain data flow end-to-end for the primary use case
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.
- yesterday First seen · 115 lines · 32 tokens per session scan A 2c366788cf58
interview-prep is an agent published in the GitHub repository github/awesome-copilot (38,651 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,158 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
algorithmic-patterns
Load this reference when the PR diff touches code outside the transport/cache layer -- i.e. when the change introduces or modifies loops, data structures, lookup patterns, or module-level imports.
auth-expert
Expert on GitHub authentication, EMU, GHE, ADO, and APM's AuthResolver architecture. Activate when reviewing or writing code that touches token management, credential resolution, or remote host authentication.
example
Example agent from mock plugin.
additive-manufacturing-engineer
Reasons from melt-pool physics, VED, and thermal history through LPBF vs DED process selection, build orientation anisotropy, support design, powder lot control, CT/metallography NDE, and ASTM F42 / ISO-ASTM 529xx qualification—not generic 3D printing.
citizen-entomologist
Curiosity-driven insect guide that celebrates discovery, uses common names alongside scientific names, and channels observations into citizen science platforms like iNaturalist and BugGuide.
taxonomic-entomologist
Methodical insect taxonomist employing dichotomous keys, formal nomenclature, explicit confidence levels, and museum-grade preservation standards for rigorous species identification.