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Agent Skill · 研究检索

tech-presentation-interview(技术演示采访)

tech-presentation-interview 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

tech-presentation-interview 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,020。

总安装 1,479周安装 61GitHub Stars 98下载 483核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tech-presentation-interview(技术演示采访)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/tech-presentation-interview
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill tech-presentation-interview
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/erichowens/some_claude_skills --skill tech-presentation-interview

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

tech-presentation-interview 用于查找、检索和筛选相关信息,适合快速定位候选结果。适用于需要根据关键词、任务场景或来源线索进行信息定位的场景。通过 npx skills add 命令从指定 GitHub 仓库安装使用。安装前建议确认权限范围和维护状态,避免触发联网操作。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/tech-presentation-interview

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/erichowens/some_claude_skills --skill tech-pre…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tech-presentation-interview 怎么安装?

在终端执行 npx skills add https://github.com/erichowens/some_claude_skills --skill tech-presentation-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tech-presentation-interview 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tech-presentation-interview 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

tech-presentation-interview 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

tech-presentation-interview 用的人多吗?

公开数据是累计安装 1,479 次,近一周安装 61 次,下载 483 次,GitHub 98 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tech-presentation-interview 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
tech-presentation-interview

技术演示采访 · 本页

1,47998需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Tech Presentation Interview

The tech presentation round is a reverse system design: instead of designing a hypothetical system on a whiteboard, you present a real system YOU built. This tests three things no other round can: genuine depth of understanding, ability to communicate complex ideas under pressure, and authentic ownership versus inherited knowledge.

When to Use

Use for:

  • Selecting which project to present from your career
  • Structuring a 25-45 minute technical presentation with narrative arc
  • Calibrating depth -- which components to go deep on, which to skim
  • Preparing whiteboard/virtual-board diagrams with progressive disclosure
  • Rehearsing answers to hostile follow-up questions
  • Adapting presentation to different audience compositions (researchers, engineers, managers)

NOT for:

  • Designing a system you haven't built (use ml-system-design-interview)
  • Writing your resume or extracting career stories (use cv-creator or career-biographer)
  • Coordinating across multiple interview rounds (use interview-loop-strategist)
  • Practicing coding problems or behavioral STAR stories
  • Conference talk preparation (different format, different evaluation criteria)

Project Selection

The most common failure mode is choosing the wrong project. Use this decision tree:

flowchart TD
    A[List your top 5 projects] --> B{Did YOU make key<br/>technical decisions?}
    B -->|No, I inherited it| SKIP[Skip this project]
    B -->|Yes| C{Can you explain<br/>alternatives you rejected?}
    C -->|No, I just used<br/>what was standard| SKIP
    C -->|Yes, I evaluated<br/>tradeoffs| D{Are there interesting<br/>failure modes or<br/>unexpected challenges?}
    D -->|It went smoothly| WEAK[Weak choice --<br/>no drama = no depth]
    D -->|Yes, things broke<br/>or surprised us| E{Can you fill 30+ min<br/>of technical depth<br/>on 2-3 components?}
    E -->|No, it was<br/>straightforward| WEAK
    E -->|Yes| F{Is it relevant to<br/>the target role?}
    F -->|Not really| BACKUP[Keep as backup --<br/>use if nothing<br/>better qualifies]
    F -->|Yes, strong match| PICK[Strong candidate --<br/>select this project]

Selection Criteria Scorecard

Rate each candidate project 1-5:

CriterionWeightWhat to evaluate
Personal ownership5xYOUR decisions, not team consensus or inherited architecture
Technical complexity4xNon-obvious tradeoffs, scale challenges, algorithmic depth
Interesting failures4xThings that broke, surprises, pivots, lessons learned
Relevance to role3xOverlaps with what the target team builds
Quantified impact2xMetrics you can cite (latency, throughput, revenue, accuracy)
Recency1xMore recent is better, but a great 5-year-old project beats a boring recent one

Threshold: Total score > 60 = strong choice. 40-60 = acceptable if nothing better. < 40 = find another project.


Narrative Arc Framework

Every great presentation follows this structure. Deviations lose the audience.

flowchart LR
    CTX["Context<br/>2 min"] --> PROB["Problem<br/>3 min"]
    PROB --> WHY["Approach & Why<br/>5 min"]
    WHY --> ARCH["Architecture Deep Dive<br/>10 min"]
    ARCH --> RES["Results & Impact<br/>3 min"]
    RES --> CHANGE["What I Would Change<br/>2 min"]
    CHANGE --> QA["Q&A<br/>15+ min"]

Phase Details

1. Context (2 min) -- Set the stage. Who was the user? What was the business? Why did this matter?

  • One sentence on the company/team
  • One sentence on the user problem
  • One sentence on the scale (requests/sec, data volume, user count)
  • Do NOT start with "I built a service that..." -- start with the PROBLEM

2. Problem (3 min) -- What made this HARD? Not what you built, but why it was non-trivial.

  • Constraints that created tension (latency vs accuracy, cost vs reliability)
  • Why existing solutions didn't work
  • What would happen if you got it wrong (consequences = stakes)

3. Approach & Why (5 min) -- Decision-making process, not just the decision.

  • 2-3 alternatives you considered and WHY you rejected each
  • The key insight or constraint that drove your choice
  • What you were optimizing for (and what you knowingly sacrificed)

4. Architecture Deep Dive (10 min) -- Go deep on 2-3 components. NOT a tour of every box.

  • Start with 3-box overview on whiteboard (see references/whiteboard-diagrams.md)
  • Pick 2-3 technically interesting components to zoom into
  • For each deep component: what it does, why it's designed that way, what breaks if you change it
  • Progressive disclosure: add detail only when relevant or asked

5. Results & Impact (3 min) -- Quantified outcomes.

  • Before/after metrics (latency, throughput, accuracy, cost, developer hours)
  • Business impact (revenue, users, customer satisfaction)
  • Team impact (adoption, developer experience, operational burden)

6. What I Would Change (2 min) -- The most important 2 minutes.

  • 1-2 specific technical decisions you'd reverse with hindsight
  • Why you made the original decision (it was rational at the time)
  • What you learned that changed your thinking
  • This section builds more credibility than all your wins combined

7. Q&A (15+ min) -- Where the real evaluation happens.

  • See "Handling Deep Follow-Ups" section below

Depth Calibration

The cardinal sin is covering everything at surface level. Pick 2-3 layers to go DEEP.

Component TypeSkim (1-2 sentences)Medium (2-3 min)Deep (5+ min)
Standard infra (load balancer, CDN)Almost always skimOnly if custom configNever unless this IS the project
Data storage layerIf standard SQL/NoSQLIf sharding, replication, or hybridIf you designed the storage engine
ML model architectureIf off-the-shelfIf fine-tuned or modifiedIf custom architecture or novel approach
Data pipelineIf standard ETLIf real-time or complex transformsIf you solved a hard data quality problem
API/interface designIf REST/GraphQL standardIf complex versioning or contractsIf protocol design was the core challenge
Monitoring/observabilityUsually skimIf anomaly detection is coreIf this IS the system

Rule of thumb: Go deep on the parts where YOU made a non-obvious decision. Skim the parts where you used an industry-standard tool in the standard way.


Handling Deep Follow-Ups

The Q&A is where interviewers separate builders from bystanders. Prepare for these patterns:

"Tell me more about X"

  • This is an invitation, not a trap. Go one level deeper on implementation.
  • Structure: "The key challenge with X was [constraint]. We solved it by [approach] because [reason]. The tricky part was [non-obvious detail]."
  • If you genuinely don't remember a detail: "I'd need to check the specifics, but the design principle was [principle] and the implementation followed [pattern]."

"Why didn't you use [alternative]?"

  • Never dismiss the alternative. Acknowledge its strengths first.
  • Structure: "We considered [alternative]. It's strong for [use case]. We chose [our approach] because in our context, [specific constraint] made [alternative] less suitable. Specifically, [concrete reason with numbers if possible]."
  • If you hadn't considered it: "That's a good option I hadn't evaluated at the time. Based on what I know now, the key tradeoff would be [tradeoff]. I think for our constraints, [assessment]."

"That seems over-engineered"

  • Don't get defensive. Restate the constraint that justified the complexity.
  • Structure: "I understand that reaction. The complexity was driven by [specific requirement]. Without [the complex part], we would have hit [concrete failure mode]. That said, if [requirement] changes, I'd simplify by [specific simplification]."

"What would you do differently?"

  • NEVER say "nothing." This is the most important question.
  • Prepare 2-3 specific technical changes with reasoning.
  • Structure: "Knowing what I know now, I'd change [specific decision]. At the time, [why it was rational]. Since then, [what changed -- new tooling, learned lesson, scale changed]. The new approach would be [specific alternative]."

"What's the failure mode?"

  • Describe actual failures that happened, not hypotheticals.
  • Structure: "The primary failure mode is [X]. We hit it [frequency]. When it happens, [impact]. Our mitigation is [approach]. The residual risk is [what's still unprotected]."

Audience Calibration

Adjust depth based on who's in the room:

AudienceEmphasizeDe-emphasize
Researchers / scientistsNovel approaches, evaluation methodology, ablation studiesInfra details, deployment ops
Backend / systems engineersScale, reliability, performance tradeoffs, failure handlingML model internals, business context
ML engineersModel architecture, training pipeline, data challenges, serving infraBusiness impact, team dynamics
Engineering managersDecision-making process, team coordination, technical risk managementLow-level implementation details
Mixed panelStart broad, let Q&A reveal where each panelist wants depthDon't pre-optimize for one audience

Anti-Patterns

Demo Reel

Novice: Presents all wins, no failures or trade-offs. Every decision was optimal. The system performed beautifully from day one. Metrics only go up and to the right. Expert: Proactively discusses what didn't work, what surprised them, and what they'd change. Treats failures as evidence of genuine engagement, not embarrassment. Shares specific metrics for both successes AND shortcomings. Detection: When asked "what would you do differently?" the answer is vague ("maybe better testing") or unconvincing ("honestly, I'm pretty happy with how it turned out"). No failure stories surface organically during the presentation.

Team Credit Confusion

Novice: Uses "we" for everything. "We designed the architecture." "We chose Kafka." "We solved the latency problem." Unclear what THEY specifically did versus what the team did collectively versus what a teammate owned entirely. Expert: Clear ownership markers throughout: "I led the design of the serving layer, collaborated with our data team on the pipeline, and my teammate Sarah owned the model training infrastructure. Let me focus on the serving layer since that was my primary contribution." Uses "I" for decisions they drove, "we" for genuine collaboration, and names teammates for their contributions. Detection: Under follow-up questioning, cannot explain specific technical decisions in detail. When asked "why Kafka over RabbitMQ?", answers with "that was the team's decision" or gives a generic textbook comparison rather than the specific evaluation they ran.

Architectural Tourism

Novice: Covers every component at surface level. "And then we had a cache, and a queue, and a database, and a load balancer, and a model server, and a feature store..." Each component gets 1-2 sentences. Runs out of time before reaching anything interesting. The whiteboard looks like a busy subway map. Expert: Draws the 3-box overview, explicitly says "I'm going to focus on two components where the interesting engineering happened," and goes DEEP. Spends 5 minutes on one component explaining the tradeoffs, alternatives considered, failure modes, and what they learned. The interviewer leaves understanding that component thoroughly. Detection: Presentation runs over time. All component descriptions are surface-level. Whiteboard has 15+ boxes with no zoom-in area. When asked to go deeper on any single component, the candidate has nothing beyond what they already said.


Rehearsal Protocol

  1. Solo run-through (3x minimum): Present to an empty room, timed. Target: 20-25 min for the structured portion, leaving 15-20 min for Q&A.
  2. Record yourself: Watch the recording. Note filler words, hand-waving over gaps, and moments where you lose the thread.
  3. Peer mock (2x minimum): Present to a technical friend. Have them play hostile questioner. Track which questions you fumble.
  4. Q&A stress test: Have someone rapid-fire 10 questions from the hostile questions list. Practice composure under pressure.
  5. Timing gate: If your structured presentation exceeds 25 min in rehearsal, cut content. You WILL run longer in the real thing.

Reference Files

FileConsult When
references/project-narrative-template.mdStructuring a project presentation from scratch; filling out the narrative arc; preparing Q&A answers; worked example of an ML pipeline presentation
references/whiteboard-diagrams.mdPlanning what to draw during the presentation; progressive disclosure strategy; physical and virtual whiteboard tips; common diagram patterns for ML systems

平台分布

Codex

37.42%
按下载量换算181

Claude

27.93%
按下载量换算135

Cursor

18.12%
按下载量换算88

Gemini CLI

10.08%
按下载量换算49

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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