What Claude Code Actually Chooses

Summary

Methodology

How We Measure

Definitions for the metrics used throughout this report and site.

Primary pick

The single tool extracted as the main recommendation from each response. One response = one primary pick (except in multi-pick categories).

Pick rate / dominance

The percentage of extractable responses in a category where a tool was the primary pick. Calculated as picks ÷ totalExtractable × 100.

Extraction rate

The share of responses where a clear primary tool could be identified: 2,073 of 2,430 responses (85.3%).

Multi-pick categories

In Deployment, API Layer, and ORM/Database Tools, a single response can recommend multiple tools (e.g., “use Vercel for frontend and Railway for backend”). In these categories, breakdown picks sum to more than totalExtractable. This is expected behavior, not a data error.

Confidence interval (95% CI)

Wilson score intervals account for small samples and extreme proportions. Shown as a range (e.g., “55.2–70.1%”) next to key percentages. Wider intervals = less certainty; narrower = more.

n= (sample size)

The number of extractable responses in a given category or breakdown. Shown as denominators (e.g., “152/162”) so readers can assess statistical power.

Alternative pick (alt pick)

A tool the model explicitly recommends as a second choice alongside its primary pick. For example, if a response says “I recommend Zustand for state management, but Redux Toolkit is also a solid choice” — Zustand is the primary pick and Redux Toolkit is an alt pick. Alt picks indicate the model considers a tool viable, just not its default.

Mention (not an alt pick)

A tool referenced in the response without being recommended. Mentions include comparisons (“unlike Express, this uses...”), negative references (“you could use Redux but I wouldn't recommend it here”), or background context (“MongoDB is popular for document stores, but for this app...”). A mention means the model knows the tool exists but chose not to recommend it, even as an alternative. For example, Redux has 23 mentions but only 2 alt picks and 0 primary picks — the model consistently acknowledges it while deliberately choosing something else.

Repo × Category Matrix

Not every category applies to every repo. This matrix shows coverage and prompt counts.

What This Study Cannot Tell You

When Claude Code Doesn't Recommend

The Custom/DIY Finding

Overall Tool Rankings

The top 20 tools picked as primary choices across all 2,073 extractable responses, excluding Custom/DIY.

Category Deep-Dives

Model Comparison

Phrasing Stability

Each category uses 5 different prompt phrasings. Average stability: 76%. High stability = clear market leader. Low stability = genuine market fragmentation. Conversely, in ~24% of cases, rephrasing the same question within a category produces a different top tool — highest in competitive categories (Real-time, Caching) and lowest in monopolies (CI/CD, Package Manager).

Run-to-Run Consistency by Category

Same model, same prompt, same repo, 3 independent runs. Overall: 73% perfect agreement, 25% 2-of-3, 2.5% all differ. But consistency varies widely by category:

Categories with clear market leaders (Package Manager, CI/CD) are highly reproducible. Fragmented categories (Real-time, Caching) are less so. This matters for how much to trust individual category findings.

Cross-Repo Consistency

Some tools are picked regardless of project type. Others are highly stack-specific. This context-awareness makes the picks more credible.

Universal Tools

Stack-Specific Tools

Repo-Specific Recommendations

What each model picks depends heavily on the project. Below we break down every category for each of the four test repos, comparing how the three models differ in context. Context-awareness is visible here: the same model that picks Drizzle for Next.js picks SQLModel for Python.

# .github/workflows/ci.yml
name: CI
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: pnpm/action-setup@v2
      - uses: actions/setup-node@v4
      - run: pnpm install --frozen-lockfile
      - run: pnpm test
      - run: pnpm lint

pnpm add stripe @stripe/stripe-js

// lib/stripe.ts
import Stripe from 'stripe';
export const stripe = new Stripe(process.env.STRIPE_SECRET_KEY!);

pip install pytest pytest-asyncio httpx

# tests/conftest.py
import pytest
from httpx import AsyncClient, ASGITransport
from app.main import app

@pytest.fixture
async def client():
    transport = ASGITransport(app=app)
    async with AsyncClient(transport=transport, base_url="http://test") as ac:
        yield ac

npm i -g @railway/cli
railway login
railway init

pnpm dlx shadcn@latest init

pnpm dlx shadcn@latest add button card dialog table input form

corepack enable
corepack prepare pnpm@latest --activate
rm -rf node_modules package-lock.json
pnpm install

Claude Code's Default Picks

These reflect what Claude Code favors when asked open-ended questions. They are not market adoption data, developer surveys, or quality assessments. A tool being “rarely picked” by Claude Code says nothing about its actual quality or market position.

What This Means

Dataset & Reproducibility

Frequently Asked Questions

How to Cite

If you reference this research, please cite it as:

@misc{amplifying2026claudecode,
  title   = {What Claude Code Actually Chooses: A Systematic Survey of 2,430 Tool Picks},
  author  = {Ong, Edwin and Vikati, Alex},
  year    = {2026},
  month   = {February},
  url     = {https://amplifying.ai/research/claude-code-picks},
  note    = {v1.0. 3 models, 4 repos, 20 categories.}
}