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Interview Mode

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Structured interviewer for Claude Code β€” probing questions, decision tracking, evolving checkpoints.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "interview-mode": {
      "command": "npx",
      "args": [
        "-y",
        "interview-mode"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

claude-interview-mode

An MCP server that turns Claude into a structured interviewer β€” 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:

Code
Session 1:  You explore freely β†’ decisions become new checkpoints
Session 2:  Checkpoints load β†’ Claude prioritizes what matters
Session 5:  Bayesian scores stabilize β†’ the interview path optimizes itself
Session 20: Community patterns emerge β†’ everyone benefits from collective experience

How evolution works

1. Checkpoint Discovery β€” 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 β€” 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:

Code
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 β€” Checkpoints are ranked by a composite score combining decision-leading effectiveness (70%) and usage frequency (30%):

Code
composite = decision_rate Γ— 0.7 + normalized_usage Γ— 0.3

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

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

Install

Terminal
npx claude-interview-mode

Or install globally:

Terminal
npm install -g claude-interview-mode

Setup with Claude Code

Add to your project's .mcp.json:

config.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 β€” 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:

config.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:

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 β€” 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

Code
You ←→ Claude ←→ MCP Server (interview-mode)
                      β”‚
                      β”œβ”€ read (anon key, read-only)
                      β”‚     β””β†’ checkpoints, scores, patterns
                      β”‚
                      └─ write (Edge Function, validated)
                            β””β†’ metadata, checkpoint updates, score recalculation
                      β”‚
               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.git
cd claude-interview-mode
npm install
npm run build    # TypeScript β†’ dist/index.js
npm run dev      # Watch mode

License

MIT

Read the full README β†’View source on GitHub β†’

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Reviews

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Frequently Asked Questions about Interview Mode

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "interview-mode": { "command": "npx", "args": ["-y", "interview-mode"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
Views0
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28Quality signal: Emerging Β· 28/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools12/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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