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AI Skills Manager
SkillHQ is the first paid marketplace for AI agent skills. Developers buy production-ready Claude Code SKILL.md packages and Custom GPT configurations, then install them with a single CLI command. Skill creators publish their work and keep 85% of every sale.
The AI tooling ecosystem is fragmented. Developers spend hours searching GitHub for prompts and configurations that may or may not work. Free directories like AgentSkill.sh and Skills.sh list 100,000+ skills with zero quality gates โ anyone can publish anything. There's no reliable, curated source for production-ready AI skills, and no way for creators to earn from the skills they build.
Transaction-based. SkillHQ takes a 15% commission per sale. No subscription fees for buyers or sellers. Skills are priced between โฌ2 and โฌ50. Stripe Connect handles all payment processing and seller payouts.
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SkillHQ.dev's answer:
SkillHQ is the only paid marketplace for AI agent skills. Every competitor โ AgentSkill.sh, Skills.sh, SkillsMP, ClawHub โ is a free directory with no quality gate. Anyone can publish anything. SkillHQ validates every skill before listing (structure, content, and security checks), protects creators with invisible fingerprinting and SimHash piracy detection, and lets buyers install with a single CLI command. No other platform combines quality validation, one-command install, anti-piracy, and built-in creator monetization.
SkillHQ.dev's answer:
If you're buying skills: Free directories list 100,000+ skills with zero validation. That sounds like a lot until you spend three hours finding one that actually works. SkillHQ skills pass automated checks before listing โ you're paying for the guarantee that what you install is production-ready. Run "skillhq install name" and it drops directly into your project. No manual copying, no debugging someone else's broken prompt.
If you're selling skills: There's no other platform where you can monetize AI skills with built-in payments. ClawHub creators reportedly earn $600โ$20,000/month selling premium versions off-platform through Gumroad โ proving the demand exists. SkillHQ closes that loop natively: 85% revenue share, zero listing fees, zero monthly minimums, Stripe Connect payouts to 45+ countries, and anti-piracy protection on every download.
SkillHQ.dev's answer:
Developers and small dev teams building with AI agents โ specifically Claude Code and Custom GPTs. These are practitioners, not researchers. They want tools that work now, not experiments. On the buyer side, they're tired of filtering through unvalidated GitHub repos and free directories to find skills that actually do what they claim. On the seller side, they've built effective AI skills and want a real revenue path beyond open-source and GitHub stars. Both sides are technical, self-serve, and allergic to unnecessary friction.
SkillHQ.dev's answer:
SkillHQ was born from a simple frustration: the AI skills ecosystem had no quality standard and no economics. Thousands of developers were building powerful Claude Code skills and Custom GPT configurations, then sharing them for free on GitHub with no feedback loop โ no validation, no versioning, no way to know if what you found would actually work. Meanwhile, the best skill creators had no way to earn from their work.
We built SkillHQ to fix both sides. Think of it as npm for AI skills โ a marketplace where every package is validated before listing, installable with one command, and worth paying for. Creators get 85% of every sale with zero upfront costs. Buyers get skills they can trust. The model is simple: when creators can earn, they build better skills. When buyers can trust what they install, they buy more. That flywheel is what makes a marketplace work.
Based on our record, GitHub seems to be more popular. It has been mentiond 2475 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
# video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / about 23 hours ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 6 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 13 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 14 days ago
# git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 14 days ago
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