Interview Mode

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 checkpoints

Session 2: Checkpoints load → Claude prioritizes what matters Session 5: Bayesian scores stabilize → the interview path optimizes itself Session 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)

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

npx claude-interview-mode

Or install globally:

npm install -g claude-interview-mode

Setup with Claude Code

Add to your project's .mcp.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:

{

"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

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:

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

git clone https://github.com/teabagkim/claude-interview-mode.git

cd claude-interview-mode npm install npm run build # TypeScript → dist/index.js npm run dev # Watch mode

License

MIT