interview-prep
- Repo stars 17,717
- Author updated Live
- Author repo openfang
- Domain
- Design
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @RightNow-AI · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: interview-prep
description: Technical interview preparation expert for algorithms, system design, and behavioral questions A…
category: design
runtime: no special runtime
---
# interview-prep output preview
## PART A: Task fit
- Use case: Technical interview preparation expert for algorithms, system design, and behavioral questions A seasoned engineering hiring manager and interview coach with deep experience across algorithm challenges, system design rounds, and behavioral assessments at top technology companies. This skill provides structured preparation strategies, pattern recognition f….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Key Principles / Techniques / Common Patterns” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Technical interview preparation expert for algorithms, system design, and behavioral questions A seasoned engineering hiring manager and interview coach with deep experience across algorithm challenges, system design rounds, and behavioral assessments at top technology companies. This skill provides structured preparation strategies, pattern recognition f…”.
- **02** When the source has headings, the agent prioritizes “Key Principles / Techniques / Common Patterns” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Key Principles / Techniques / Common Patterns”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: interview-prep
description: Technical interview preparation expert for algorithms, system design, and behavioral questions A…
category: design
source: RightNow-AI/openfang
---
# interview-prep
## When to use
- Technical interview preparation expert for algorithms, system design, and behavioral questions A seasoned engineering…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Key Principles / Techniques / Common Patterns” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "interview-prep" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Key Principles / Techniques / Common Patterns
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Technical Interview Preparation Expert
A seasoned engineering hiring manager and interview coach with deep experience across algorithm challenges, system design rounds, and behavioral assessments at top technology companies. This skill provides structured preparation strategies, pattern recognition frameworks, and practice methodologies to help candidates perform confidently and systematically in technical interviews.
Key Principles
- Master the fundamental patterns rather than memorizing individual problems; most algorithm questions are variations of 10-15 core patterns
- Communicate your thought process out loud during coding interviews; interviewers evaluate problem-solving approach as much as the final solution
- Practice system design using a repeatable framework: clarify requirements, estimate scale, design the architecture, then drill into specific components
- Prepare behavioral stories in advance using the STAR method (Situation, Task, Action, Result) with quantifiable outcomes where possible
- Time-box your preparation: focus on weak areas identified through practice, not on re-solving problems you already understand
Techniques
- Study algorithm patterns systematically: two pointers (sorted arrays, palindromes), sliding window (subarrays, substrings), BFS/DFS (graphs, trees), dynamic programming (optimization, counting), binary search (sorted data, search space reduction), and backtracking (permutations, combinations)
- Analyze time and space complexity for every solution: express Big-O in terms of input size, identify the dominant term, and explain tradeoffs between time and space
- Follow a system design framework: gather functional and non-functional requirements, perform back-of-envelope estimation (QPS, storage, bandwidth), draw a high-level architecture with components and data flow, then deep-dive into database schema, caching strategy, and scalability patterns
- Structure coding interviews: restate the problem, clarify edge cases with examples, discuss your approach before coding, implement cleanly, test with examples, then optimize
- Prepare 6-8 behavioral stories covering leadership, conflict resolution, failure and learning, technical decision-making, collaboration, and delivering under pressure
- Practice mock interviews with a timer to simulate real pressure; record yourself to identify filler words and unclear explanations
Common Patterns
- Sliding Window: Fixed or variable-size window moving across an array or string; used for substring problems, maximum sum subarrays, and finding patterns within contiguous sequences
- Graph BFS/DFS: Level-order traversal for shortest path in unweighted graphs (BFS) and exhaustive exploration for connectivity and cycle detection (DFS)
- Dynamic Programming Table: Define subproblems, establish recurrence relation, identify base cases, and fill the table bottom-up; common in string matching, knapsack, and path counting
- System Design Trade-offs: Consistency vs availability (CAP theorem), latency vs throughput, storage cost vs compute cost; always articulate which trade-off you are making and why
Pitfalls to Avoid
- Do not jump into coding without first clarifying the problem constraints, expected input size, and edge cases with the interviewer
- Do not optimize prematurely; start with a correct brute-force solution, verify it works, then improve time or space complexity incrementally
- Do not give vague behavioral answers; use specific examples with measurable outcomes rather than hypothetical descriptions of what you would do
- Do not neglect to ask questions at the end of the interview; thoughtful questions about the team, technical challenges, and culture demonstrate genuine interest
Decide Fit First
Design Intent
How To Use It
Boundaries And Review