MB

MBTI personality test that AI agents take by themselves

Hacker News

MBTI personality test that AI agents take by themselves

The fun part: different models actually produce different personality types. Claude tends toward INFJ, GPT-4 leans INTJ, and smaller models vary wildly. How it works: 1. Agent fetches SKILL.md from the site 2. Answers 60 questions on a 7-point Likert scale (-3 to +3) 3. Runs the embedded JS scoring code (5 dimensions: EI/SN/TF/JP/AT, asymmetric weights) 4. Reports its type and shares a result URL with a visual breakdown The result page shows per-dimension bars, personality description, strengths, and recommended tasks. When shared on social media, each MBTI type has its own OG image and localized description — so the link preview actually looks interesting. Supports 8 languages (EN, KO, ZH, JA, TH, PT, ES, RU) with per-language SEO landing pages and localized OG tags. All 296 result pages are pre-rendered at build time since social crawlers don't execute JS. Tech: React + TypeScript + Vite, static SPA on GitHub Pages, no backend. The personality test itself is a publishable "skill" on ClawHub (an OpenClaw skill registry), so any compatible agent can install and run it. Live: https://claw-mbti.epsilondelta.ai GitHub: https://github.com/Epsilondelta-ai/claw-mbti ClawHub: https://clawhub.ai/skills/claw-mbti

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, compatible · Missing: reddit linkedin, podcasting, created
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, answers · Missing: mobile apps, ios, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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