Interview Mode

io.github.teabagkim由 GitHub 帳號 teabagkim 發布v0.5.1更新於 Oct 24, 2026

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

已驗證STDIO僅桌面效率與工作流知識與記憶

概覽

AI 產生的概覽

一個本機 MCP 伺服器,把 Claude Code 變成結構化訪談者,依主題類別記錄問答與決策,並維護持續演進的檢查點。

可以做什麼
進行與管理訪談:開始工作階段時載入附評分的檢查點與建議路徑,結束時上傳中繼資料並演進檢查點系統。記錄問題、回答與決策,並追蹤檢查點涵蓋情況。檢視進度,以及依評分排序的未涵蓋檢查點。檢查點依類別跨工作階段被發現、評分與排序,並支援同時進行多個工作階段。
適用情境
適合希望由 Claude 主導結構化探索對話的情境,例如定價策略、API 設計或產品決策,同時保留決策紀錄,並重複使用隨多次工作階段持續改善的主題清單。
執行需求
透過 npx 或全域 npm 安裝後在本機執行(需要 Node.js),在 Claude Code 的 .mcp.json 中以 stdio 設定。預設連線共享的社群 Supabase 資料庫即可使用;也可選擇設定 SUPABASE_URL 與 SUPABASE_ANON_KEY 使用自己的 Supabase 專案並執行提供的 schema。存取共享資料庫需要網路。
安裝前請注意
預設會把匿名中繼資料——類別名稱、檢查點名稱、使用次數、評分與位置——上傳到共享社群資料庫;問答內容與決策細節據稱不會共享。結束訪談會觸發該上傳。使用自己的 Supabase 需提供 SUPABASE_URL 與 SUPABASE_ANON_KEY。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Interview Mode,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

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

來源:README.md,提交 8c3c3b2

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.5.1最新Sep 16, 2026

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