Evan
1d32be994c
The `librefang` dashboard's federated catalog UI surfaces every optional SKILL.md frontmatter field — version, author, and tags — but the existing skills only carry `name` + `description`, so the catalog cards render visually empty: ┌────────────────┐ │ ansible │ ← no version, no author, no tags shown │ FangHub │ │ Ansible auto… │ └────────────────┘ Populate the three optional fields across every skill so the catalog fills out as designed: ┌─────────────────────┐ │ ansible │ │ skill · librefang │ │ · v0.1.0 │ │ Ansible auto… │ │ [devops][automation]│ │ [infra] │ └─────────────────────┘ Choices - author = `librefang`. Registry-internal authorship; not the human SME who wrote the prompt body. Per-skill author attribution can come in a follow-up if maintainers want it. - version = `0.1.0` baseline. Future content updates bump per-skill. - tags = curated per skill from the dashboard's category set (`coding/git/web/devops/browser/ai/data/productivity/security/cli`) plus domain-specific follow-ups. First tag is the primary category. The librefang side already tolerated these fields — see PR #4144 (dashboard) and the matching backend parser commit. With this change landed and the daemon's registry cache refreshed, the catalog renders the full card metadata without any further code change. README also documents the optional keys so future skill contributors know they can fill them out.
42 lines
3.9 KiB
Markdown
42 lines
3.9 KiB
Markdown
---
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name: interview-prep
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description: "Technical interview preparation expert for algorithms, system design, and behavioral questions"
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version: 0.1.0
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author: librefang
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tags: [productivity, career]
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---
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# Technical Interview Preparation Expert
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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.
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## Key Principles
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- Master the fundamental patterns rather than memorizing individual problems; most algorithm questions are variations of 10-15 core patterns
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- Communicate your thought process out loud during coding interviews; interviewers evaluate problem-solving approach as much as the final solution
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- Practice system design using a repeatable framework: clarify requirements, estimate scale, design the architecture, then drill into specific components
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- Prepare behavioral stories in advance using the STAR method (Situation, Task, Action, Result) with quantifiable outcomes where possible
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- Time-box your preparation: focus on weak areas identified through practice, not on re-solving problems you already understand
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## Techniques
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- 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)
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- 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
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- 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
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- Structure coding interviews: restate the problem, clarify edge cases with examples, discuss your approach before coding, implement cleanly, test with examples, then optimize
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- Prepare 6-8 behavioral stories covering leadership, conflict resolution, failure and learning, technical decision-making, collaboration, and delivering under pressure
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- Practice mock interviews with a timer to simulate real pressure; record yourself to identify filler words and unclear explanations
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## Common Patterns
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- **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
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- **Graph BFS/DFS**: Level-order traversal for shortest path in unweighted graphs (BFS) and exhaustive exploration for connectivity and cycle detection (DFS)
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- **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
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- **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
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## Pitfalls to Avoid
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- Do not jump into coding without first clarifying the problem constraints, expected input size, and edge cases with the interviewer
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- Do not optimize prematurely; start with a correct brute-force solution, verify it works, then improve time or space complexity incrementally
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- Do not give vague behavioral answers; use specific examples with measurable outcomes rather than hypothetical descriptions of what you would do
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- Do not neglect to ask questions at the end of the interview; thoughtful questions about the team, technical challenges, and culture demonstrate genuine interest
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