Documentation | BetterPrompt

Getting Started

What is BetterPrompt?

BetterPrompt analyzes your AI sessions to reveal how you collaborate with AI. It examines your prompts, patterns, and working style to generate a personalized assessment — so you can see clearly what's working and where to grow.

Whether you're a PM, designer, founder, or builder of any kind: if you use Claude to get things done, BetterPrompt can help you use it better.

Three Steps to Your Assessment

Connect your AI toolOpen your terminal and run one command. It reads your Claude sessions from your machine — nothing is uploaded until you choose to share.

Get your assessmentThe tool analyzes your AI sessions in about a minute and generates a personalized report with your collaboration style and key insights.

Start improvingReview your strengths, identify patterns that slow you down, and get curated resources to level up your AI collaboration.

Run the Command

Open your terminal (Terminal on Mac, or any command prompt) and run:

bash

$ npx no-ai-slop

That's it. The tool will guide you through sign-in and analysis automatically. First-time setup takes about 30 seconds; analysis runs in about a minute.

What Data is Analyzed

BetterPrompt reads your Claude AI sessions — the conversations you've had with Claude across your projects. It looks at how you ask questions, how you respond to Claude's output, and how you guide AI toward your goals.

Your session data stays on your machine. Only the analysis results (scores, insights, your report) are sent to our servers.

How It Works

Once you run the command, BetterPrompt works in two main stages.

Stage 1: Finding Your Best Sessions

The tool scans your AI sessions and picks the ones that best represent how you work — focusing on recent, substantive sessions where real collaboration happened. No AI is involved yet; this is purely about finding good signal.

Stage 2: Deep Analysis

Five specialized AI analysts examine your sessions in parallel, each focused on a different dimension of your collaboration:

Thinking QualityHow you plan and reason before acting

Communication PatternsHow clearly you express your intent to AI

Learning BehaviorHow you adapt when things don't go as planned

Context EfficiencyHow well you manage information flow with AI

Session OutcomeWhether your sessions actually achieve what you set out to do

The results are combined into your profile type, a personality-style narrative, and actionable focus areas — then delivered as a shareable report.

Advanced: Full pipeline breakdown (for the technically curious)

The pipeline runs across 8 phases (11–12 LLM calls total), using Gemini 3 Flash for all AI-powered stages.

Phase 1: Data Extraction (CLI)

A memory-efficient deterministic algorithm identifies your highest-quality sessions. No LLM calls are made in this phase.

File Discovery — Scans ~/.claude/projects/ for session files
Pre-filter — Filters by size and recency to reduce candidates
Quality Scoring — Scores top 100 candidates on message count, duration, tool diversity, recency
Parse Selection — Parses the top 15 highest-quality sessions

Phase 1.5: Session Summarization

Generates concise one-line summaries for each session using a single batched LLM call. These summaries provide context for downstream workers and appear in your activity timeline.

Phase 2: Insight Workers

Five specialized workers run in parallel, each analyzing a distinct domain.ProjectSummarizer generates project-level summaries and WeeklyInsightGenerator produces weekly narrative highlights — both running in parallel. Total: 7 parallel LLM calls.

Phase 2.5: Type Classification

A single LLM call classifies your style into a 5×3 matrix (5 types × 3 control levels = 15 combinations) and generates an MBTI-style personality narrative describing your behavioral patterns.

Phase 2.75: Knowledge Matching

A deterministic resource matcher (no LLM) maps your identified strengths and growth areas to curated professional resources from a knowledge database.

Phase 2.8: Evidence Verification

An LLM-based verifier cross-checks evidence quotes against original session utterances, ensuring all referenced quotes are accurate and traceable.

Phase 3: Content Writer

Generates the top focus areas narrative from all prior insights. A single LLM call synthesizes worker outputs into actionable guidance.

Phase 4: Translator

Conditional translation for non-English users. Skipped entirely for English reports (0 calls), uses 1 LLM call for other languages.

Pipeline Overview

~/.claude/projects/ --> DataExtractor --> Sessions
        |
   Gemini 3 Flash
        |
  Phase 1.5: SessionSummarizer (1 LLM)
        |
  +-----------------------------------------------+
  |  5 Insight Workers (parallel, 5 LLM)          |
  |  ThinkingQuality ----+                        |
  |  CommunicationPatt --+                        |
  |  LearningBehavior ---+--> Worker Insights     |
  |  ContextEfficiency --+                        |
  |  SessionOutcome -----+                        |
  |                                               |
  |  ProjectSummarizer (1 LLM) ─┐  (parallel)    |
  |  WeeklyInsightGen  (1 LLM) ─┘                |
  +-----------------------------------------------+
        |
  Phase 2.5:  TypeClassifier (1 LLM)
  Phase 2.75: KnowledgeResourceMatcher (0 LLM)
  Phase 2.8:  EvidenceVerifier (1 LLM)
        |
  Phase 3: ContentWriter (1 LLM)
  Phase 4: Translator (0-1 LLM)
        |
   Supabase --> Web Report

Your Report

Your AI Collaboration Style

Your report starts with a personality-style profile: a combination of your primary working style and how you navigate ambiguity. Think of it like an MBTI for your AI collaboration habits.

The 5 Builder Styles

Style	Description
Architect	Plans extensively before acting — knows the destination before starting
Analyst	Verifies and investigates thoroughly — never accepts output without checking
Conductor	Orchestrates AI tools and workflows — focuses on directing, not doing
Speedrunner	Optimizes for velocity — ships fast, iterates faster
Trendsetter	Explores cutting-edge approaches — always pushing what's possible

The 3 Navigation Modes

Mode	Description
Explorer	Open exploration — discovering solutions through experimentation
Navigator	Balanced navigation — exploration with a loose route in mind
Cartographer	Strategic mapping — charting the territory before advancing

The 6 Dimensions

Beyond your style profile, your report scores six dimensions that capture the nuanced aspects of how you work with AI:

Dimension	What it measures
AI Collaboration Mastery	Planning quality, verification habits, instruction clarity
Context Engineering	How well you manage what AI knows at any given moment
Tool Mastery	Breadth and intentionality of AI tool usage
Burnout Risk	Session length, frequency, and cognitive load patterns
AI Control Index	Strategic direction vs. reactive acceptance of AI output
Skill Resilience	Your independent capability alongside AI reliance

Our Mission

The Question

Are you getting better with AI—or just more dependent?

Our Philosophy

Technology moves fast. AI is making it exponential.

Here's the uncomfortable truth: when thinking becomes optional, we stop doing it. And when we stop thinking, we stop growing.

AI isn't the problem. Unconscious dependency is.

The builders who thrive won't be those who delegate everything—they'll be those who know when to think for themselves and when to let AI multiply their work. AI should be a force multiplier, not a replacement for judgment.

Our Vision

We're building a future where humans use tools—not the other way around.

Our goal is simple: help you see yourself clearly. How are you actually using AI? What patterns keep repeating? Are you thinking critically—or just accepting?

Self-awareness is the first step to growth. We start with builders at the frontier of human-AI collaboration — and we're just getting started.

Our Values

Think First

AI amplifies judgment. It doesn't replace the need for it.

Open Source

Our methodology is transparent. See exactly how we evaluate.

Privacy First

Your data stays yours. Analysis happens locally.

Technical Details

For builders who want to understand exactly what's happening under the hood — or who need to troubleshoot, audit, or extend BetterPrompt.

Session data format (JSONL structure)

BetterPrompt reads Claude Code session logs from ~/.claude/projects/. These are JSONL files containing your conversation history with Claude.

JSONL Message Types

Each session file contains message blocks of these types:

user — Your prompts and messages
assistant — Claude's responses
tool_use — Tool invocations (Read, Edit, Bash, etc.)
tool_result — Results from tool executions
queue-operation — Internal queue metadata (not analyzed)
file-history-snapshot — File state snapshots (not analyzed)

Path encoding

Claude Code encodes project paths by replacing / with -. For example, /Users/you/projects/myapp becomes -Users-you-projects-myapp.

BetterPrompt handles this automatically via encodeProjectPath and decodeProjectPath utilities.

CLI commands reference

Main Command

bash

$ npx no-ai-slop

Analyzes your Claude AI sessions and generates a shareable report. First-time users are guided through device-flow authentication.

Check Authentication Status

bash

$ npx no-ai-slop status

Shows your current authentication status and linked account.

Logout

bash

$ npx no-ai-slop logout

Clears your local authentication credentials.

Help

bash

$ npx no-ai-slop help

Displays all available commands and usage information.

Authentication Flow

BetterPrompt uses OAuth-style device flow authentication:

CLI generates a unique device code
Browser opens to authentication page
You sign in with your account
CLI receives authentication token
Token is stored locally for future sessions

Context Engineering dimension — scoring detail

The Context Engineering score evaluates four strategies:

WRITE — Proactively providing relevant context upfront
SELECT — Choosing which information to include or exclude
COMPRESS — Summarizing or condensing context to stay within limits
ISOLATE — Keeping concerns separate to avoid noise