The Best Agenticskills.io Alternative for Developers in 2026

AgenticSkills.io is a curated directory of AI agent skills and MCP servers, tracking 193+ skills, 196 MCP servers and 9 platforms as of October 2026. Every listing is open source, organized into 16 categories, and filterable by platform — Claude Code, OpenAI Codex, Cursor, Gemini CLI and every agent that reads SKILL.md. Beyond the directory, it publishes a substantial learning library: 41,000+ words across guides like the npx skills CLI walkthrough (verified against skills 1.7.0), a complete guide to the Agent Plugins open standard agreed on by OpenAI, Microsoft, AWS, GitHub, Cursor and Vercel, and a security analysis of 1,862 real-world findings across 23,000+ public MCP servers. Its featured picks include Anthropic's Frontend Design and MCP Builder skills, Vercel Labs' React Best Practices, and obra's debugging and planning series, each carrying an editorial S-rank or A-rank. The site states clearly that its 'why it's indexed' notes are editorial judgment, not measurement, and that listing is never paid for.

pricing See pricing

• + Largest combined skills-plus-MCP index in the research: 193+ skills and 196 servers
• + Genuinely free — browsing, guides and newsletter cost nothing, and listings are never paid for
• + Deep, current learning content, including coverage of the Agent Plugins open standard
• + Transparent about methodology limits: rankings are labeled editorial judgment, not measurement
• + Tracks 9 platforms, so it works whether you use Claude Code, Codex, Cursor or Gemini CLI

• − Ranks are editorial judgment, not measured results — no sandbox benchmarking or Lift figures
• − No per-skill security scanning, license checking or commit-SHA pinning of versions
• − Its own security research (73% of public MCP servers failing basic checks) underscores the risk of unverified installs
• − No per-agent loadout or MCP connector — you install and manage skills yourself

https://agenticskills.io/

SkillzBench is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose. It exists because agent skills — SKILL.md instruction packages for Claude Code, Codex, Cursor, Gemini CLI and chat-app agents — have proliferated with no way to tell which ones help and which are risky: published research found roughly 26% of skills carry at least one vulnerability. SkillzBench's answer is a five-pillar pipeline: Discover (continuously crawling and deduping skills from GitHub), Verify (pinning every version to a commit SHA, validating, license-checking, security-scanning and quality-judging), Benchmark (testing every skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points), Equip (one free MCP server plus per-agent loadouts you toggle, so each agent only sees what was chosen), and Stay current (tracking upstream changes, re-scanning, re-benchmarking and notifying). A 0-100 trust score combines security, quality, license, maintenance and community signals, and Lift is always shown separately — money never touches scores. Everything is free; revenue comes from advertising around blog articles, never from users.

pricing See pricing

• + Everything is free — directory, MCP connector, benchmarks and alerts
• + Verification-first: commit-SHA pinning, security scanning and license checks before you install
• + Benchmarks measure actual Lift rather than relying on editorial taste
• + Per-agent loadouts limit each agent's visibility to exactly the skills you chose
• + Scores are insulated from money; ad revenue never influences rankings

• − Pre-launch — the product is actively being built; only the waitlist and newsletter are live today
• − No published catalog size or benchmark results yet, unlike AgenticSkills' 193+ indexed skills
• − Smaller learning library than AgenticSkills' 41,000-word guide collection

https://skillzbench.com

E2B appears in the AI Sandboxes category of the ecosystem as Firecracker microVM sandboxes purpose-built for running AI-generated code. Where a skills directory tells your agent what to know, a sandbox is where generated code actually runs — isolated, disposable, and safe to fail in. E2B's Firecracker foundation means each run gets a lightweight virtual machine rather than a shared container, which is the isolation model most security-conscious agent builders prefer for untrusted code. It sits alongside Modal and Daytona as one of the three named sandbox alternatives developers encounter when researching this stack, and it complements rather than replaces a skills directory: you might pick skills from one index and run the code they produce inside E2B.

pricing See pricing

• + Purpose-built for the exact workload agent skills generate: untrusted AI code execution
• + Firecracker microVM isolation is a stronger boundary than container-only sandboxes

• − Solves execution, not skill discovery or verification — it does not tell you which skills to install
• − No published rates in the directory listing, so budgeting requires checking the vendor directly

Modal is listed in the AI Sandboxes category as serverless Python compute with GPUs, built for inference and heavy ML workloads. For agent builders, that positions it one layer heavier than a code-execution sandbox: when your agent needs to run a fine-tuned model, batch embeddings, or GPU-bound inference as part of a workflow, Modal is the named option in this ecosystem. It shares the category with E2B and Daytona, and like them it is complementary to a skills directory — the skills tell the agent how to work, Modal supplies the compute the work runs on. The directory listing notes it is proprietary and hosted, with a usage-based pricing model.

pricing See pricing

• + GPU access without managing infrastructure — a fit for inference-heavy agent pipelines
• + Serverless model means you pay for compute time, not idle servers

• − Not a skills tool — no discovery, verification or benchmarking of agent skills
• − Usage-based pricing with no rates shown in the ecosystem listing

Daytona is described in the AI Sandboxes category as fast, stateful development environments for agents that need to persist a workspace. That persistence is the differentiator: where E2B's sandboxes are typically ephemeral, Daytona keeps a workspace alive between agent sessions, which matters for long-running development tasks where an agent returns day after day to the same repository and environment state. It is one of the three named sandbox alternatives in this ecosystem, proprietary and hosted, and like the others it pairs with — rather than competes with — a skills directory: Daytona is where the agent works, the directory is where the agent's instructions come from.

pricing See pricing

• + Stateful workspaces suit multi-session agent development workflows
• + Positioned as fast to start, which matters for agents spinning environments up repeatedly

• − No role in skill discovery, security vetting or benchmarking
• − No published rates in the ecosystem listing

Fly.io appears in the AI Sandboxes category as the option with persistent block storage and no execution ceiling, which the listing notes most sandbox platforms cap by design. It is proprietary, hosted, language-agnostic ('any'), and tagged firecracker, edge, long-running and persistent — a profile suited to agents that need to keep running and keep their data rather than spin up disposable microVMs. Notably, the directory entry is candid that its 'why it's indexed' note is editorial judgment, not a measurement, and that listing is never paid for. It also warns that pricing is shown as a model, not a rate, and that published rates change without notice — so budgeting means checking Fly.io's own page.

pricing See pricing

• + No execution ceiling — fits long-running agent workloads that other sandboxes cap
• + Persistent storage keeps agent state across runs

• − Usage-based pricing with no published rate in the listing; rates change without notice
• − Infrastructure only — no skill discovery, verification or benchmarking