
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.
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
在 SourceWeft 中
- 開啟 儀表板中的 Interview Mode,將其新增到工作區。
- 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:
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:
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%):
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)
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
Or install globally:
Setup with Claude Code
Add to your project's .mcp.json:
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:
Then run supabase/schema.sql in your Supabase SQL Editor to create the tables.
Usage
Start an interview with Claude Code:
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
Architecture
4 database tables power the evolution:
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
License
MIT
來源:README.md,提交 8c3c3b2
工具
0版本歷史
1- v0.5.1最新Sep 16, 2026
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