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Interview Processor

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cafe3310
interview-processor

处理面试记录,包含面试大纲规划、提问提取与纠错、总结和知识图谱存储

Overview

Publishercafe3310
Repositorypublic-agent-skills
Skill nameinterview-processor
Stars
256
Forks
30
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cafe3310 on GitHub. Read the source before you install it.

Installation

Install the Interview Processor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cafe3310/public-agent-skills/tree/main/skills/interview-processor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cafe3310/public-agent-skills.git /tmp/public-agent-skills
mkdir -p .claude/skills
cp -r /tmp/public-agent-skills/skills/interview-processor .claude/skills/interview-processor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview Processor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Interview Processor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Interview Processor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Agent Skill: interview-processor (面试记录处理技能)

本技能用于辅助面试官在面试前后进行高效的结构化处理,沉淀招聘成果并客观自评提问水平。它可以配合 memories-off 技能的 memocli 将生成数据安全写入本地知识图谱。

1. 依赖工具与规范声明 (Dependencies)

本技能依赖本地优先的知识库管理工具 memocli。

外部依赖与规范说明

本 Skill 依赖 memories-off 库进行实体管理与长期记忆。在执行任何任务前,您必须先查阅并完整遵循当前目录下的 memories-off-declare.md 声明文档,以获取其定义的实体类型规范及封装的子过程操作细节。

同时,为了简化指令的输入长度,建议您在 ~/.config/memocli/config.yaml 中配置全局路径别名,并使用简写别名(如 -p work 或 -p life)来运行本技能涉及的所有 memocli 指令。


2. 动态上下文加载机制 (Interviewer Persona)

为了保证面试总结中“团队介绍”以及对面试官表现的自评贴合面试官当前的最新岗位和团队,本技能采用知识图谱优先的动态画像加载机制:

  1. 第一步:在执行任何子任务前,Agent 必须运行:
    bash
    memocli search-entities "我自己的面试官画像" -p <path>
  2. 第二步:
    • 若实体存在,Agent 应读取其正文(通常包含 ## 当前岗位与团队 和 ## 个人面试偏好),以其内容作为当前的背景上下文。
    • 若实体不存在,Agent 应友好提醒面试官,并引导其使用以下命令初始化该画像:
      bash
      echo "## 当前岗位与团队
      [在此写明您当前的岗位、所负责团队及核心业务介绍]
      
      ## 个人面试偏好
      [在此写明您的面试风格、提问原则和关注特质]" | memocli create-entity -p <path> -e "我自己的面试官画像" -t "个人画像" --content-stdin --reason "初始化面试官个人画像"

3. 数据实体建模方案 (KG Entity Models)

本技能在本地知识库中管理四类实体。为符合 memories-off 规范,所有实体必须遵循严格的 H1 (# 实体名) 与 H2 (## 章节名) 标题层级,禁止使用 H3 及以下标题。

3.1 个人画像 (Interviewer Persona)

  • 命名规范:我自己的面试官画像
  • 实体类型 (type):个人画像
  • 核心章节:
    • ## 当前岗位与团队
    • ## 个人面试偏好

3.2 面试方法论 (Interview Methodology)

  • 命名规范:[领域/岗位]专家面试风格-YYYYMMDD (例如:iOS专家面试风格-20250718)
  • 实体类型 (type):面试方法论
  • 核心章节:
    • ## 面试风格与偏好(列出面试官在该领域的提问原则、风格和追问逻辑)

3.3 面试问题集 (Interview Question Set)

  • 命名规范:[领域/岗位]面试问题集-YYYYMMDD (例如:大模型产品面试问题集-20250729)
  • 实体类型 (type):面试问题集
  • 核心章节:根据面试的方向或技术模块划分的多个 H2 章节。
    • ## 核心认知与边界理解
    • ## 系统设计与成本控制
    • ... (根据实际方向动态创建)
  • 出站关系:
    • target_methodology 指向其对应的 [领域/岗位]专家面试风格-YYYYMMDD 实体。

3.4 面试记录 (Interview Record)

  • 命名规范:对[候选人姓名]的面试总结 (YYYY-MM-DD)
  • 实体类型 (type):面试记录
  • 核心章节:
    • ## 基本信息(候选人姓名、招聘类型 [校招/社招/数字马力]、岗位名称、面试日期)
    • ## 面试反馈(建议层级、优势、不足、结论)
    • ## 沟通记录与评价(核心讨论话题列表与候选人回答情况评估)
    • ## 面试官表现自评(以专家级别对面试官提问、追问、得体度做出的客观改进要求)
  • 出站关系:
    • use_style 指向对应 面试方法论 实体。
    • use_question_set 指向对应 面试问题集 实体。
    • candidate 指向候选人个人实体(若有)。

4. 子任务导航 (Subtasks Navigator)

当用户触发本技能时,请根据当前所处的面试阶段,跳转至对应的子任务执行详细指南:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Interview Processor AI skill do?

处理面试记录,包含面试大纲规划、提问提取与纠错、总结和知识图谱存储

Why use Interview Processor on TypingMind?

Because you install it once and use it with any model. Interview Processor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Interview Processor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cafe3310/public-agent-skills/tree/main/skills/interview-processor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Interview Processor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Interview Processor?

As many as you like. As long as a model supports skills, you can use Interview Processor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Interview Processor AI skill free?

Yes. It is published on GitHub by cafe3310 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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