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claude-interview-mode

by teabagkim

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)

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, positions

Any personal or project-specific content

Related MCP server: DevPlan MCP Server

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

Tool

Description

start_interview

Begin a session — loads scored checkpoints and recommended path

record

Record a Q&A or decision, with checkpoint coverage tracking

get_context

Review progress, see uncovered checkpoints ranked by score

end_interview

End 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:

Table

Purpose

checkpoints

Checkpoint dictionary per category (name, usage count, decision count)

checkpoint_scores

Bayesian scores per checkpoint (decision rate, avg position, samples)

interview_patterns

Coverage sequences per session (which checkpoints, in what order)

interview_metadata

Session 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

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

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