Agent skill

Senior Coding Interview

by curiositech in curiositech/some_claude_skills

Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups.

MITAuto-check passedFrontend & Design

Install Senior Coding Interview

skills CLI
$ npx skills add curiositech/some_claude_skills --skill senior-coding-interview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install curiositech/some_claude_skills senior-coding-interview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/curiositech/some_claude_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/senior-coding-interview .claude/skills/senior-coding-interview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
senior-coding-interview
GitHub stars
243
Token cost
~3.1k tokens
SKILL.md length
1,341 words
Files
5 (incl. references)
Skills in repo
109
Repo updated
First seen
Licence
MIT

At a glance

Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups.

  • Works in 4 steps: Clarify (5 minutes) → Skeleton (20 minutes) → Iterate (10 minutes) → …
  • Practicing real-world system-building problems
  • SKILL.md covers When to Use, The 4-Stage Approach, Problem Archetypes and Communication Protocol, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Senior Coding Interview is an agent skill from curiositech/some_claude_skills. Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups. Use when practicing real-world system-building problems or preparing communication strategies for live coding. Activate on "coding interview", "staff interview", "codesignal", "live coding", "rate limiter interview". NOT for LeetCode/competitive programming, behavioral interviews, or system design whiteboard.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `.claude-plugin/plugin.json`, `references/codesignal-incremental.md` and `references/problem-archetypes.md`).

It sits in Frontend & Design, covering Interview preparation and State management. The repository describes itself as: Claude skills that make my life easier. The licence is MIT.

When your agent uses it

  • Practicing real-world system-building problems
  • Preparing communication strategies for live coding

Example prompts

  • “coding interview”
  • “staff interview”
  • “codesignal”
  • “/senior-coding-interview”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Clarify (5 minutes)
  2. Skeleton (20 minutes)
  3. Iterate (10 minutes)
  4. Optimize & Discuss (5 minutes)

What it can do on your machine

Read from SKILL.md and the folder at commit 6713fc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are mermaid).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Senior Coding Interview loads about 3.1k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 1,341 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~19k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from curiositech/some_claude_skills at commit 6713fc7, republished under its MIT licence (© curiositech). 1,341 words, ~3,095 tokens.

Download SKILL.mdSave it as .claude/skills/senior-coding-interview/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
senior-coding-interview
description
Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups. Use when practicing real-world system-building problems or preparing communication strategies for live coding. Activate on "coding interview", "staff interview", "codesignal", "live coding", "rate limiter interview". NOT for LeetCode/competitive programming, behavioral interviews, or system design whiteboard.
allowed-tools
Read, Write, Edit
metadata.gated
true
metadata.category
Career & Interview
metadata.tags
interview, coding, python, senior-engineer

Senior Coding Interview

Execute L6+ real-world coding interviews where the problem is building a small system, not solving an algorithm puzzle. The core differentiator at Staff+ level is not whether you can solve it, but how you solve it: clean abstractions, narrated reasoning, graceful iteration, and production sensibility.

When to Use

  • Practicing real-world coding problems (in-memory stores, rate limiters, task schedulers)
  • Reviewing interview code for senior-level signals
  • Preparing communication strategy for live coding sessions
  • Working through CodeSignal incremental-style problems
  • Mock interview practice with follow-up extensions

NOT for:

  • LeetCode/competitive programming (segment trees, suffix arrays, contest optimization)
  • Behavioral interviews (use interview-loop-strategist)
  • System design whiteboard with no code (use ml-system-design-interview)
  • Resume or career strategy

The 4-Stage Approach

mermaid
flowchart LR
    C[1. CLARIFY\n5 min] --> S[2. SKELETON\n20 min]
    S --> I[3. ITERATE\n10 min]
    I --> O[4. OPTIMIZE\n5 min]

    C -.- C1["Ask 3-5 questions\nRestate problem\nConfirm API contract\nIdentify edge cases"]
    S -.- S1["Data structures first\nPublic API methods\nCore logic\nManual test 1 case"]
    I -.- I1["Edge cases\nError handling\nFollow-up extensions\nRefactor if needed"]
    O -.- O1["Complexity analysis\nTrade-off discussion\nConcurrency mention\nScaling path"]
Stage 1: Clarify (5 minutes)

Goal: Demonstrate you think before coding. Ask questions that reveal ambiguity the interviewer planted intentionally.

Mandatory questions for every problem:

  1. Scale: "How many items/requests are we expecting?" (determines data structure choice)
  2. API surface: "Should this be a class with methods, or standalone functions?"
  3. Constraints: "Are keys always strings? Can values be None/null?"
  4. Concurrency: "Single-threaded for now, or should I consider thread safety?"
  5. Error handling: "Should invalid input raise exceptions or return error values?"

Restate the problem in your own words before writing any code. This catches misunderstandings early and signals comprehension.

Stage 2: Skeleton (20 minutes)

Goal: Get a working solution for the core case. Not perfect, not optimized -- working.

Order of implementation:

  1. Define the data model (dataclass or NamedTuple for structured data)
  2. Write the class/function signatures with type hints
  3. Implement the happy path
  4. Manually trace through one example out loud

Senior signal: Start with the public API, not the internal helpers. Show top-down thinking.

Stage 3: Iterate (10 minutes)

Goal: Handle follow-ups. This is where Staff+ candidates differentiate -- each extension should feel like a natural evolution, not a rewrite.

The follow-up ladder (interviewers typically go 2-3 levels deep):

  1. Working -- Base problem solved
  2. Edge Cases -- Empty inputs, duplicates, overflow, None values
  3. Concurrent -- Thread safety, locks, atomic operations
  4. Distributed -- Multiple nodes, consistency, partitioning
  5. Fault-tolerant -- Crash recovery, persistence, graceful degradation

Senior signal: When asked "how would you make this distributed?", discuss the trade-offs before changing code. Name specific patterns (consistent hashing, write-ahead logs). You don't need to implement distributed systems in 40 minutes -- you need to show you know the path.

Stage 4: Optimize & Discuss (5 minutes)

Goal: Show you understand what you built and where it breaks.

Cover:

  • Time and space complexity for each operation
  • What would break at 10x scale
  • What you would change given more time
  • Testing strategy (what tests would you write first?)

Problem Archetypes

ArchetypeCore Data StructureKey Follow-upsReference
In-Memory Key-Value Storedict + metadataTTL, transactions, snapshotsreferences/problem-archetypes.md
File System AbstractionTrie or nested dictGlob patterns, watchers, permissionsreferences/problem-archetypes.md
Rate Limiterdeque or sorted listSliding window, distributed, token bucketreferences/problem-archetypes.md
LRU CacheOrderedDict or dict+DLLGenerics, TTL, size-based evictionreferences/problem-archetypes.md
Task SchedulerHeap + dictPriorities, dependencies, cancellationreferences/problem-archetypes.md
Event/Pub-Sub Systemdefaultdict(list)Typed events, wildcards, async deliveryreferences/problem-archetypes.md
Log Parser/AnalyzerGenerators + CounterStreaming, time windows, aggregationreferences/problem-archetypes.md
API Client with RetryState machineBackoff, circuit breaker, idempotencyreferences/problem-archetypes.md

Communication Protocol

Senior interviews are 50% code and 50% communication. The interviewer is evaluating whether they want to work with you, not just whether you can solve the problem.

What to Narrate
  • Before writing: "I'm going to use a dict with timestamps as values because we need O(1) lookup and the TTL check can be lazy."
  • At decision points: "I could use a heap here for O(log n) insert, but since we're told the number of items is small, a sorted list with bisect is simpler and good enough."
  • When stuck: "I'm not sure about the best way to handle concurrent access here. Let me get the single-threaded version working first, then we can discuss locks."
  • After completing: "The core operations are O(1) for get/set. The cleanup sweep is O(n) but only runs periodically."
What NOT to Do
  • Don't narrate syntax: "Now I'm writing a for loop..." -- the interviewer can see that.
  • Don't go silent for more than 60 seconds. If you're thinking, say so.
  • Don't ask "Is this right?" -- instead say "Let me trace through an example to verify."

Senior Signals Checklist

These are the things that make an interviewer write "strong hire" for L6+:

SignalHow to Demonstrate
Clean abstractionsSeparate concerns: data model, business logic, I/O
Production sensibilityError handling, input validation, logging mentions
Testing awareness"I'd test the TTL edge case where expiry happens during a get"
ExtensibilityDesign classes that can be extended without rewriting
Trade-off fluencyName multiple approaches, choose one, explain why
Complexity awarenessState big-O for each operation without being asked
Concurrency knowledgeMention thread safety even if not implementing it
Stdlib masteryUse dataclasses, defaultdict, deque, generators naturally

Show full SKILL.md (537 more words)Show less

Python Patterns for Senior Interviews

Senior candidates use Python idioms that signal deep experience. See references/python-patterns-senior.md for the complete catalog with examples.

Key patterns to internalize:

  • @dataclass for any structured data (not raw dicts)
  • Context managers for resource cleanup
  • Generators for streaming/lazy evaluation
  • collections.defaultdict, Counter, deque -- know the stdlib
  • Type hints on public methods (skip on internal helpers in time-pressured interviews)
  • Exception hierarchies for domain errors

Anti-Patterns

Anti-Pattern: LeetCode Brain

Novice: Reaches for algorithmically elegant solutions (segment trees, suffix arrays, Fenwick trees) when a hash map or sorted list suffices. Spends 15 minutes on optimal time complexity for a problem where n < 1000.

Expert: Chooses the simplest correct solution first. Uses built-in data structures (dict, list, deque, heapq) unless the problem explicitly demands otherwise. Discusses when algorithmic sophistication matters only if asked about scale. The goal is working, readable, maintainable code -- not a competitive programming submission.

Detection: Solution is asymptotically optimal but unmaintainable. Candidate cannot explain trade-offs between their approach and a simpler one. No working solution exists at the 25-minute mark because they're still optimizing.

Anti-Pattern: Silent Coder

Novice: Writes code for 15+ minutes without speaking. Treats the interview like a solo coding session. When they do speak, they narrate syntax ("Now I'm writing a for loop") rather than intent.

Expert: Narrates intent before writing code ("I'm going to use a dict here because we need O(1) lookup by key"). Asks clarifying questions when ambiguity appears. Signals uncertainty honestly ("I'm not sure if Python's heapq supports decrease-key -- let me use a different approach that I'm confident in"). Treats the interviewer as a collaborator, not an examiner.

Detection: Interviewer has to prompt "what are you thinking?" more than twice. Long silences followed by large code blocks. No questions asked during the clarify phase.

Anti-Pattern: Premature Optimization

Novice: Starts with the distributed/concurrent/fault-tolerant version before solving the single-machine case. Adds caching, sharding, or thread pools before there's a working solution to optimize. Designs for 10 million users when the problem says "a few thousand."

Expert: Gets a working solution first, then optimizes when asked. Separates "what I'd do in production" from "what I'm implementing in this 40-minute interview." When the interviewer asks about scale, discusses the optimization path verbally: "I'd add a write-ahead log for durability, then shard by key hash for horizontal scaling."

Detection: No working solution at the 25-minute mark. Code has Lock, ThreadPoolExecutor, or asyncio imports but no passing test case. Architecture diagram exists but core logic doesn't.


CodeSignal Incremental Format

CodeSignal's pre-recorded incremental format (used by Anthropic and others) differs from live interviews. See references/codesignal-incremental.md for detailed strategy.

Key differences:

  • No interviewer to ask questions -- you must self-clarify from the problem statement
  • Incremental stages build on your previous code -- design for extension from the start
  • Time pressure is real but self-managed -- no one tells you to move on
  • You can re-read the problem statement -- do it before each stage

Time Budget Decision Tree

mermaid
flowchart TD
    START[Problem received] --> READ["Read ENTIRE problem\n(2 min)"]
    READ --> KNOWN{Recognize\nthe archetype?}
    KNOWN -->|Yes| FAST["Fast-track clarify\n(2 min)"]
    KNOWN -->|No| DEEP["Deep clarify\n(5 min)"]
    FAST --> CODE["Code skeleton\n(18 min)"]
    DEEP --> CODE
    CODE --> CHECK{Working\nsolution?}
    CHECK -->|No, 25 min mark| TRIAGE["Simplify approach\nGet SOMETHING working\n(5 min)"]
    CHECK -->|Yes| EXTEND["Handle follow-ups\n(10 min)"]
    TRIAGE --> EXTEND
    EXTEND --> WRAP["Complexity + trade-offs\n(5 min)"]

References

  • references/problem-archetypes.md -- Consult for worked examples of 8 problem archetypes with skeletons, clarifying questions, and follow-up extensions
  • references/python-patterns-senior.md -- Consult for senior Python idioms that signal experience: dataclasses, context managers, generators, stdlib mastery, testing hooks
  • references/codesignal-incremental.md -- Consult when preparing for CodeSignal's pre-recorded incremental format: time management, extension strategies, self-testing without an interviewer

© curiositech, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in .claude/skills/senior-coding-interview of curiositech/some_claude_skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • references/codesignal-incremental.md
  • references/problem-archetypes.md
  • references/python-patterns-senior.md

Open the folder on GitHubat commit 6713fc7

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Questions about Senior Coding Interview

What does Senior Coding Interview do?

Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups. Senior Coding Interview is an agent skill from curiositech/some_claude_skills. Prepare for L6+ coding interviews — in-memory databases, concurrency, state management, iterative follow-ups.

When should I use Senior Coding Interview?

Senior Coding Interview fits situations like: practicing real-world system-building problems; preparing communication strategies for live coding.

How do I install Senior Coding Interview in Claude Code?

Run `npx skills add curiositech/some_claude_skills --skill senior-coding-interview -a claude-code`. Or copy the skill folder (.claude/skills/senior-coding-interview in curiositech/some_claude_skills) into .claude/skills/senior-coding-interview in your project. Claude Code loads it when a task matches its description.

How do I install Senior Coding Interview in Codex?

Run `npx skills add curiositech/some_claude_skills --skill senior-coding-interview -a codex`. Or copy the skill folder (.claude/skills/senior-coding-interview in curiositech/some_claude_skills) into .agents/skills/senior-coding-interview in your project. Codex loads it when a task matches its description.

Can I use Senior Coding Interview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add curiositech/some_claude_skills --skill senior-coding-interview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-coding-interview, .gemini/skills/senior-coding-interview, .github/skills/senior-coding-interview and .opencode/skills/senior-coding-interview in your project.

What does Senior Coding Interview need to run?

SKILL.md names no scripts, command-line tools or credentials: Senior Coding Interview is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit.

Does Senior Coding Interview access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Senior Coding Interview safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Senior Coding Interview use?

Senior Coding Interview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Senior Coding Interview use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Senior Coding Interview?

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Who maintains Senior Coding Interview?

curiositech (a GitHub organization) maintains it in curiositech/some_claude_skills, which has 243 GitHub stars. The repository holds 109 skills in this directory. The repository was last updated on September 6, 2026.

Source: curiositech/some_claude_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.