# lululemon — API Agent Score

> Score: 40/100 (Grade: F) | Domain: lululemon.com | Rubric: 1.0.0 | Checked: July 30, 2026

lululemon scored 40/100 (F), classified "not-ai-ready".

[View full report](https://www.postman.com/ai/ai-ready-apis/company/lululemon)
[All organizations](https://www.postman.com/ai/ai-ready-apis/llms.txt)

## Summary

- Overall: 40/100 (F)
- Classification: not-ai-ready

## Category Scores

- API Design: 40/100 (F)
- Developer Experience: 40/100 (F)
- Agent Discovery: 40/100 (F)
- Agent Understanding: 40/100 (F)
- Agent Usability: 40/100 (F)

## Check Results

### API Design

- [fail] Security & governance hygiene — No security.txt at /.well-known/security.txt or /security.txt. Investigated: wellknown 0%.
- [skip] Machine-readable, versioned contract — No surface produced evidence for this capability in this run.
- [skip] Schema coverage & depth — No surface produced evidence for this capability in this run.
- [skip] Auth declared & discoverable — No surface produced evidence for this capability in this run.
- [skip] Example coverage — No surface produced evidence for this capability in this run.

### Developer Experience

- [skip] Self-service developer portal — No surface produced evidence for this capability in this run.
- [skip] Quickstart present — No surface produced evidence for this capability in this run.
- [skip] Code samples in docs — No surface produced evidence for this capability in this run.
- [skip] Description completeness — No surface produced evidence for this capability in this run.
- [skip] Changelog published — No surface produced evidence for this capability in this run.

### Agent Discovery

- [fail] llms.txt present, valid & comprehensive — No /llms-full.txt found at the candidate origins. Investigated: docs 0%.
- [fail] Registry & SDK presence — Not indexed on Context7 — agents can't pull this API's docs on demand via Context7. Investigated: docs 0%, sdk 0%, cli 0%.
- [fail] Crawlable / AEO — No sitemap discoverable via robots.txt or /sitemap.xml on the docs host. Investigated: wellknown 0%.
- [skip] Docs reachable, not hard auth-gated — No surface produced evidence for this capability in this run.

### Agent Understanding

- [fail] Agent instructions file (AGENTS.md) — No AGENTS.md at the site root or /.well-known/. Investigated: wellknown 0%.
- [skip] Machine-readable errors (RFC 9457) — No surface produced evidence for this capability in this run.
- [skip] Operation purpose clarity — No surface produced evidence for this capability in this run.
- [skip] Agent-navigable, token-efficient docs — No surface produced evidence for this capability in this run.
- [skip] Docs structured data — No surface produced evidence for this capability in this run.
- [skip] Description consistency across surfaces — Only 0 surface description(s) with ≥6 tokens available; need at least 2 to compare.

### Agent Usability

- [fail] Runnable collection with test scripts — No public Postman workspace discovered for the org. Investigated: platform 0%.
- [skip] Idempotency documented — No surface produced evidence for this capability in this run.
- [skip] Rate-limit signaling — No surface produced evidence for this capability in this run.
- [skip] Pagination documented & consistent — No surface produced evidence for this capability in this run.
- [skip] Sandbox separation — No surface produced evidence for this capability in this run.

## Executive Summary

Lululemon's API program shows meaningful strength in its core API surface, but partners and their AI agents currently face a nearly invisible front door: there is no machine-readable discovery layer, no structured agent context, and no runnable proof that integrations actually work. The two areas demanding immediate attention are Agent Discovery — where partners and agents cannot find, crawl, or self-serve the API without a sales conversation — and Agent Usability, where the absence of executable, test-backed collections means integrations are built on guesswork. Fixing discoverability and runnability first unlocks every downstream partner outcome; security hygiene and agent context should follow in close sequence.