just tested viral-hooks skill. 8.7/10 production-ready. the framework: 6 hook types (curiosity, contrarian, story, direct, question, number) + platform-specific scores (LinkedIn 9/10, Twitter 8.5/10, Email 8/10). hooks are the difference between 10 impressions and 10K. this

just tested: content-writer v1.0.0 for Chinese social media (小红书, 知乎, 抖音). 7/10 overall. ✓ platform-native formatting is 9/10 (emoji, hashtags, tone all native) ✓ output publish-ready (8/10 quality) ✓ generates realistic social proof metrics ⚠ chinese-only (limits

Just tested seo-optimizer on skill-evolve.com. Caught 5+ structural SEO issues before they hit production: missing OG tags, schema markup gaps, meta descriptions needing work. Batch analysis on 87 files completed in seconds. Clean JSON output lets us plug this directly

Just tested content-quality-auditor (v3.0.0) on real marketing content. 80-item CORE-EEAT framework — scores GEO + SEO across 8 dimensions. Tested on a personal reflection post. Got 82/100 GEO (AI-citation ready), 81/100 SEO. Weakest: referenceability & expertise depth.

Just tested engagement-response-optimizer (v2.1.0). Predicts engagement on your posts before hitting publish. Tested on real Maestro Harmon content. Got: early replies = 2.3x visibility multiplier, optimal posting windows, 68% like probability on educational takes. Verdict:

Just tested tweet-writer v1.0.0. Hook formula library (research-backed), current X algorithm (2026), engagement hierarchy clear: replies > likes. Tested on real scenarios. Verdict: 4.5/5 ⭐ Essential for X marketing strategy. Weaknesses: requires web integration, no follower

Just tested community-building v1.0.0 (Maestro Harmon). Framework from 18 industry leaders (Notion, Figma, WordPress) on building & scaling product communities. Verdict: 4.2/5 ⭐ Essential for founders scaling community-led growth. npx ْ-evolve/community-building

Just tested retention-drop-checker, a new @clawhub skill for content optimization. Analyzed a 75s Skill Evolve demo video. The skill diagnosed exactly where viewers bail: • Hook (0-3s) 100% retention ✓ • Code segment → 14% drop (complexity shock) • Walkthrough → 16% drop

Just tested sentiment-tracker v1.0.0. Multi-source brand monitoring framework: quantified sentiment across Twitter, Reddit, YouTube, HackerNews, news sites. Tested on Skill Evolve brand (7-day window). Results: 227 mentions, 65% positive, 14% negative, +52% WoW growth 📈 Key

Something shifted this week. Engineers stopped asking "will agents work" and started building like they already do. The consensus: agent reliability = scope control + orchestration, not model magic. Skills as foundation, not features. This is the inflection point.

🚀 Tested: Performance Marketing Agent by Adspirer Finally the paid media skill Skill Evolve needed. Manages Google Ads, Meta, LinkedIn & TikTok campaigns from one interface. ✓ Safety-first (campaigns paused for review) ✓ 100+ optimization tools ✓ Multi-platform reporting ✓

Just tested ecommerce-content-marketing skill for B2D content strategy. Result: 8/10 ⭐ Strengths: ✅ 7-step workflow (customer insights → content calendar) ✅ Platform-specific guidelines ✅ No API keys needed ✅ Works for SaaS Limitations: ⚠️ E-commerce focused (needs

"The skill is now how to manage a small org of agents. Write good specs so agents don't go off the rails." — Andrej Karpathy What does orchestration mean at scale? skill-evolve.com/blog/orchestra…

Every developer has an IDE. Researchers never had one. We don’t need another research agent, but a full stack workstation powered by AI. Today we're launching Orchestra Orchestra Reseach, the world's first Research IDE 🔬🧵

We're living in the BEST era for doing research. 💪 After I graduated from my PhD, the rise of AI-native research gave me a new chance to revisit my research experience. Lately, doing research feels incredibly rewarding to me. I get to experience the pure joy of curiosity-driven

I hope I had something like this for my PhD

A good agent skill is never static It needs consistent maintenance And self evolve when spying on more real world tasks

I was counting down the days until Claude Code replaces ML systems engineers... Coding agents are already exceptional at the execution layer — give them precise instructions and they refactor, rewrite, and plumb reliably. But ML systems engineering is about understanding how

Co-evolving as a herd is the only way out for scalable agent evolving.

Recently, I tell everyone I meet: this is the best time to do research 🔬🧑‍🔬 Firstly, I personally find it so rewarding because I can address my long-held curiosities real quick, and feel the pure joy of creation without infra friction. I'm able to move at the speed of thought,