Interview Mode

io.github.teabagkim由 GitHub 账号 teabagkim 发布v0.5.1更新于 Oct 24, 2026

Structured interviewer for Claude Code — probing questions, decision tracking, evolving checkpoints.

已验证STDIO仅桌面效率与工作流知识与记忆

概览

AI 生成的概览

一个本地 MCP 服务器,把 Claude Code 变成结构化访谈者,按主题类别记录问答与决策,并维护不断演进的检查点。

可以做什么
开展与管理访谈:开始会话时加载带评分的检查点和推荐路径,结束时上传元数据并演进检查点体系。记录问题、回答与决策,并跟踪检查点覆盖情况。查看进度,以及按评分排序的未覆盖检查点。检查点按类别跨会话被发现、评分和排序,并支持并行会话。
适用场景
适合希望由 Claude 主导结构化探索对话的场景,例如定价策略、API 设计或产品决策,同时保留决策记录,并复用随多次会话不断改进的主题清单。
运行要求
通过 npx 或全局 npm 安装后在本地运行(需要 Node.js),在 Claude Code 的 .mcp.json 中以 stdio 配置。默认连接共享的社区 Supabase 数据库即可使用;也可选设置 SUPABASE_URL 和 SUPABASE_ANON_KEY 使用自己的 Supabase 项目并执行提供的 schema。访问共享数据库需要网络。
安装前请注意
默认会把匿名元数据——类别名称、检查点名称、使用次数、评分和位置——上传到共享社区数据库;问答内容和决策细节据称不会共享。结束访谈会触发该上传。使用自己的 Supabase 需提供 SUPABASE_URL 和 SUPABASE_ANON_KEY。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Interview Mode,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

claude-interview-mode

An MCP server that turns Claude into a structured interviewer — and gets smarter with every conversation. Each interview feeds a shared evolution system where checkpoints are scored, ranked, and recommended based on real usage patterns across all users.

The Evolution System

This isn't just an interview tool. It's a collectively evolving knowledge system.

Every time anyone runs an interview in a category (e.g., "saas-pricing"), the system learns:

Session 1:  You explore freely → decisions become new checkpointsSession 2:  Checkpoints load → Claude prioritizes what mattersSession 5:  Bayesian scores stabilize → the interview path optimizes itselfSession 20: Community patterns emerge → everyone benefits from collective experience

How evolution works

1. Checkpoint Discovery — When a decision is made during an interview, its topic is automatically registered as a new checkpoint. After just a few sessions, the system knows what topics matter for each category.

2. Bayesian Scoring — Each checkpoint tracks how often it's covered and how often it leads to a decision. The score uses Bayesian smoothing to handle sparse data:

decision_rate = (decisions + 0.6) / (times_covered + 2)

The prior (0.6/2 = 30% base rate) ensures new checkpoints start with a reasonable score. After ~5 sessions, real data dominates.

3. Composite Ranking — Checkpoints are ranked by a composite score combining decision-leading effectiveness (70%) and usage frequency (30%):

composite = decision_rate × 0.7 + normalized_usage × 0.3

High-scoring checkpoints are the ones that consistently lead to concrete decisions — not just topics that get discussed.

4. Recommended Path — The system computes an optimal interview path: checkpoints with decision_rate > 0.2, sorted by their average position in past sessions. This tells Claude not just what to ask, but when to ask it.

5. Community Evolution — All metadata flows to a shared database. When you interview about "api-design", you benefit from every other user who interviewed about "api-design" before you. The checkpoints, scores, and paths evolve collectively.

What gets shared (and what doesn't)

Shared (metadata only)Never shared
Category names (e.g., "saas-pricing")Your actual questions and answers
Checkpoint names (e.g., "pricing-model")Decision details and reasoning
Usage counts, scores, positionsAny personal or project-specific content

What it does

  • Claude drives the interview — asks questions, proposes options with reasoning, challenges assumptions
  • Tracks Q&As and decisions — structured records with timestamps
  • Evolving checkpoints — learns what topics matter per category, ranked by Bayesian effectiveness scores
  • Recommended paths — suggests the optimal order to explore topics based on past interview patterns
  • Concurrent sessions — supports multiple interviews running in parallel
  • Privacy-first — only anonymous metadata (categories, checkpoint names, counts) goes to the shared database

Install

bash
npx claude-interview-mode

Or install globally:

bash
npm install -g claude-interview-mode

Setup with Claude Code

Add to your project's .mcp.json:

json
{  "mcpServers": {    "interview-mode": {      "type": "stdio",      "command": "npx",      "args": ["-y", "claude-interview-mode"]    }  }}

Restart your Claude Code session to load the MCP server. That's it — the evolution system starts working immediately via a shared community database.

Optional: Your own Supabase

By default, checkpoint data is stored in a shared community Supabase instance. If you want your own private database:

json
{  "mcpServers": {    "interview-mode": {      "type": "stdio",      "command": "npx",      "args": ["-y", "claude-interview-mode"],      "env": {        "SUPABASE_URL": "https://your-project.supabase.co",        "SUPABASE_ANON_KEY": "your-anon-key"      }    }  }}

Then run supabase/schema.sql in your Supabase SQL Editor to create the tables.

Usage

Start an interview with Claude Code:

> Let's do an interview about my SaaS pricing strategy

Claude will lead the conversation. As the interview progresses:

  • Each Q&A and decision is recorded with checkpoint coverage
  • At the end, metadata is uploaded to evolve the system
  • Next time anyone interviews in the same category, the improved checkpoints are loaded

Tools

ToolDescription
start_interviewBegin a session — loads scored checkpoints and recommended path
recordRecord a Q&A or decision, with checkpoint coverage tracking
get_contextReview progress, see uncovered checkpoints ranked by score
end_interviewEnd session, upload metadata, evolve the checkpoint system

Architecture

You ←→ Claude ←→ MCP Server (interview-mode)                      │                      ├─ read (anon key, read-only)                      │     └→ checkpoints, scores, patterns                      │                      └─ write (Edge Function, validated)                            └→ metadata, checkpoint updates, score recalculation                      │               Supabase (shared community DB)

4 database tables power the evolution:

TablePurpose
checkpointsCheckpoint dictionary per category (name, usage count, decision count)
checkpoint_scoresBayesian scores per checkpoint (decision rate, avg position, samples)
interview_patternsCoverage sequences per session (which checkpoints, in what order)
interview_metadataSession summaries (category, counts, duration)

Security:

  • Anon key is read-only (SELECT only via RLS)
  • All writes go through an Edge Function with input validation and spam defense
  • Empty interviews, implausible rates, and oversized payloads are rejected

Development

bash
git clone https://github.com/teabagkim/claude-interview-mode.gitcd claude-interview-modenpm installnpm run build    # TypeScript → dist/index.jsnpm run dev      # Watch mode

License

MIT

来源:README.md,提交 8c3c3b2

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.5.1最新Sep 16, 2026

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