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Hercules Agent Adds Accessibility Testing as an Open-Source Skill

Hacker News

Hercules Agent Adds Accessibility Testing as an Open-Source Skill

Hey HN, We’re excited to share a new skill added to *Hercules*, our open-source agent for automated testing. Starting today, Hercules can now perform *Accessibility Testing*, helping developers check their web applications against *WCAG 2.0, 2.1, and 2.2 standards*. Here’s what it offers: - Automated scans for accessibility compliance - Detailed insights and suggestions for fixes - Super cost-efficient (scans can be done for less than a dollar) Our goal is to make accessibility testing more accessible (pun intended!) for developers and teams. This feature is built to enable engineers to integrate accessibility checks into their workflows without needing specialized expertise or large budgets. We’d love feedback from the community—whether you’re interested in contributing, testing it out, or just want to discuss how we can collectively make the web more inclusive. Repo link: https://github.com/test-zeus-ai/testzeus-hercules/tree/main Release: https://github.com/test-zeus-ai/testzeus-hercules/releases/t... Demo Video: https://youtu.be/s_LaHIuurVQ?si=GL12D1Vz2xQXEvQ_ Looking forward to hearing your thoughts!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, new, open · Missing: mac, agents, macos
91%91% 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 · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
30%30% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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