Agent设计
- 作者仓库星标 17,717
- 作者更新于 实时读取
- 作者仓库 openfang
- 领域
- 设计与多媒体
- 兼容 Agent
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- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @RightNow-AI · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 即装即用
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- 无特殊要求
- 文件与系统权限
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- 只读
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 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: 设计与多媒体
runtime: 无特殊运行时
---
# interview-prep 输出预览
## PART A: 任务判断
- 适用问题:视觉内容、演示材料、信息图或设计交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Key Principles / Techniques / Common Patterns”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于视觉内容、演示材料、信息图或设计交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Key Principles / Techniques / Common Patterns”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件。
先用一个小任务确认它会围绕“Key Principles / Techniques / Common Patterns”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: interview-prep
description: Technical interview preparation expert for algorithms, system design, and behavioral questions A…
category: 设计与多媒体
source: RightNow-AI/openfang
---
# interview-prep
## 什么时候使用
- 把设计与视觉方向的常用动作沉淀成 Agent 可调用的技能 适合处理界面、视觉、封面、信息图或演示材料交付,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤…
- 面向视觉内容、演示材料、信息图或设计交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Key Principles / Techniques / Common Patterns」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "interview-prep" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Key Principles / Techniques / Common Patterns
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} 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
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核