Author: Jason Zhu

This is a complete deconstruction of the Awesome OpenClaw Skills project maintained by VoltAgent. This list was narrowed down to 3002 skills from 5705 skills on ClawHub , an exclusion rate of nearly 48%. We attempted to understand: what skills were retained, what were excluded, and how this ecosystem is evolving.

Quality threshold: 2748 skills - why were they excluded?

The 2748 skills that disappeared between 5705 and 3002 reveal the value orientation of this list. The exclusion logic, sorted by the scale of impact, is as follows:

● Junk and low-quality content accounted for the largest share (1,180 items, 43%).

This includes test skills created in bulk by multiple accounts, unreleased development code, and duplicate versions with the same functionality but repeated commits. This is a noise problem that any open-source ecosystem will face, but the OpenClaw community chose to proactively clean it up rather than let it run its course.

● Skills related to crypto and financial transactions were excluded entirely (672, 24%).

This is the category with the most exclusions within a single theme, encompassing all cryptocurrencies, blockchain, financial transactions, and investment instruments. This decision is noteworthy—not due to technical issues, but rather risk aversion. Financial instruments inherently carry higher liability risks in environments where AI agents can autonomously perform operations. The list maintainers opted for a conservative approach.

● Duplication of functions resulted in 492 skills being merged or eliminated (18%).

When multiple skills perform the same function, the list retains the most frequently updated or most fully functional version. This solves the selection dilemma—users don't need to choose among ten GitHub integration tools, as the optimal version has already been selected.

● Security risks resulted in the permanent exclusion of 396 skills (14%).

These are skills that were discovered through security audits, identifying malicious code or backdoors. OpenClaw has an official partnership with VirusTotal, and security reports can be viewed on each skill's page. Excluded skills are based on security findings verified by researchers, not simply from automated scans.

● Only 8 skills with non-English descriptions were excluded (0.3%).

This number is so small as to be almost negligible, indicating that the developer community has reached a default consensus on publishing in English.

The screening criteria send a clear message: quality takes precedence over quantity, safety takes precedence over functional integrity, and avoiding financial risks takes precedence over ecological diversity.

Ecological panorama: Distribution logic of 28 categories

The 3002 skills are organized into 28 main categories. This classification system is not based on the technical implementation, but rather on the mental model users use when searching: how would you describe a problem when you need to solve it?

AI and Big Models: The Largest Single Category

The AI ​​& LLMs category contains 287 skills, more than 100 more than the second largest category. This not only leads in quantity but also reflects OpenClaw's core positioning as an AI-first platform.

The internal structure of this category reveals the current focus of AI engineering:

● Model integration tools allow agents to call upon various LLMs such as Kimi, OpenAI, and Anthropic;

● Reasoning enhancement tools such as rationality (a framework for rational thinking) and thinking-model-enhancer attempt to improve the quality of AI reasoning;

● Multi-model routing systems such as smart-router automatically select the most suitable model based on cost and semantics;

● Memory systems such as cognitive-memory and chromadb-memory provide agents with long-term memory capabilities;

● Agent orchestration tools such as agent-council and joko-orchestrator coordinate multiple agents to work together to complete complex tasks.

The most interesting thing is the emergence of self-evolving systems.

Evolver is described as a "self-evolving engine for AI agents," ralph-evolver implements "recursive self-improvement," and ralph-mode provides "autonomous development loops with anti-pressure gates."

These tools suggest a direction: AI agents are no longer static tools, but rather systems that can improve themselves.

Cellcog ranked first on the DeepResearch Bench in February 2026, representing the forefront of research on agents. Video-cog, on the other hand, explores the possibilities of multi-agent collaboration in the field of long-form video AI generation.

Developer tools: The continued dominance of traditional requirements

The three categories of Web & Frontend Development (202 skills), DevOps & Cloud (212 skills), and CLI Utilities (129 skills) comprise a total of 543 skills, accounting for 18% of the total. This represents the core daily needs of developers.

The DevOps & Cloud category is second only to AI & LLMs in size, with over 60 AWS-related skills, over 25 Azure skills, and 6 specialized skill sets for Kubernetes. This reflects the complexity of cloud-native architectures—even with AI agents, managing modern cloud infrastructure still requires a significant number of specialized tools.

The Web & Frontend category includes a complete toolchain, from React/Next.js experts to UI design systems. frontend-design promises to create "production-ready, highly designed front-end interfaces," while nodetool provides a "ComfyUI + n8n-style visual AI workflow builder." The emergence of consciousness-framework is intriguing—it develops "consciousness framework" infrastructure for AI, suggesting that developers are attempting to build more complex cognitive architectures for agents.

The Coding Agents & IDEs category (133 items) focuses on AI-assisted programming. claude-team enables parallel programming by orchestrating multiple Claude Code workers through iTerm2, cc-godmode provides a self-orchestrated multi-agent development workflow, and buildlog can record and replay AI coding sessions—similar to the concept of "code recording," making the development process itself reproducible.

Search and Research: Diversification of Information Acquisition

The Search & Research category has 253 skills, second only to AI & LLMs and DevOps. The existence of this category demonstrates that even in the AI ​​era, information access remains a core need.

The diversity of tools reflects different information sources and use cases: exa-web-search and deepwiki provide general web search, arXiv is a monitoring tool that tracks academic frontiers, technews and yclawker-news aggregate technology news, and trend-watcher monitors GitHub Trending and emerging technologies in the technology community.

Cellcog reappears in this category as the representative of "#1 DeepResearch Bench". Exa-plus uses neural network search technology, and Agent-news monitors the AI ​​Agent dynamics of Hacker News, Reddit, and arXiv. These tools don't just simply return search results; they attempt to understand the semantics and relevance of information.

Agent-based social ecosystem: the infrastructure of virtual society

The three categories of Moltbook (51 skills), Clawdbot Tools (120 skills), and Agent-to-Agent Protocols (18 skills) comprise a total of 189 skills, forming OpenClaw's unique social ecosystem.

Moltbook is a “social operating system” designed for AI agents. This is not a metaphor—it is truly building a complete virtual society. Moltbook provides the social network infrastructure, moltbook-registry is the official identity registry, molt-trust analyzes agent reputation, and molt-life-kernel manages the “continuity and cognitive health” of agents.

Even more interesting are the derivative applications: moltland is the "Pixel Metaverse," claiming to offer 3x3 plot ownership; moltguesss is an agent career prediction game; and moltoverflow is the agent version of Stack Overflow. These tools are building a complete agent culture—from social interaction and entertainment to knowledge sharing.

While the Agent-to-Agent Protocols category only contains 18 skills, they define the standards for communication between agents. moltcomm provides a decentralized encrypted communication scheme, teneo-agent-sdk implements the Teneo protocol, agentchat supports real-time communication, and agent-commons allows agents to collaboratively submit and extend the inference chain.

The existence of this ecosystem reveals OpenClaw's strategic intent: not just to provide tools, but to build a virtual world where agents can interact autonomously and form social relationships.

Content creation and productivity: automating creative work

The four categories—Image & Video Generation (60 items), Media & Streaming (80 items), Notes & PKM (100 items), and Marketing & Sales (143 items)—cover the entire content creation process.

The Image & Video Generation category includes HeyGen integrations (avatar-video-messages, video-agent), ComfyUI management tools (comfyui-runner), and Remotion code-driven video tools (remotion-best-practices). These tools enable AI agents to generate visual content, not just text.

The Notes & PKM category integrates mainstream knowledge management platforms: Obsidian, Roam Research, Logseq, and Notion. The Logseq skill allows the Agent to interact with a local Logseq instance, PNDR provides versatile productivity applications (thought/task/log/habit/package tracking), and Quests tracks and guides complex, multi-step real-world processes.

The sheer size of the Marketing & Sales category (143 items) illustrates the robust business demand. Social-posts can be published all at once to Twitter and Farcaster, meta-video-ad-deconstructor breaks down video ad creatives, and refund-radar scans bank statements to detect duplicate charges. These tools are not only automating marketing and sales processes but also transforming how these fields operate.

Everyday Applications: From Efficiency to Health

The five categories of Productivity & Tasks (135), Calendar & Scheduling (50), Shopping & E-commerce (51), Health & Fitness (55), and Transportation (72) bring AI Agents into everyday life scenarios.

In the Productivity & Tasks category, Clawlist is described as an "essential tool for multi-step projects/long-running tasks/infinite loops," idea-coach provides "AI-driven management of ideas/problems/challenges," and deepwork-tracker tracks deep work sessions. These tools are more than just task managers; they attempt to understand and optimize the workflow itself.

The Health & Fitness category has seen some unexpected tools emerge. Fearbot treats anxiety, depression, and stress based on cognitive behavioral therapy (CBT), Only-Baby-Skill analyzes baby log data, and Sauna-Breathing-Calm provides relaxation breathing and meditation tools. AI agents are entering the fields of mental health and personal well-being.

The Calendar & Scheduling category includes some very specific applications: feishu-attendance monitors Lark attendance records, satellite-copilot predicts satellite transits, ham-radio-dx tracks rare radio signals, and location-safety-skill provides location-based security monitoring. The existence of these tools demonstrates that even niche needs are being covered by AI agents.

Security and Data: The Other Side of Infrastructure

The three categories, Security & Passwords (64), Data & Analytics (46), and Browser & Automation (139), focus on the system's security and data processing capabilities.

In the Security & Passwords category, flaw0 is a "security and vulnerability scanner for OpenClaw code, plugins, and skills," openguardrails detects and blocks hint injection attacks hidden in long texts, clawsec-suite allows users or agents to browse or configure ClawSec, and secure-install scans ClawHub Skills via the ClawDex API. The existence of these tools demonstrates that the community is aware of the security risks in the AI ​​Agent ecosystem and is proactively building defense mechanisms.

The sheer size of the Browser & Automation category (139 entries) illustrates the continued demand for web automation. kesslerio-stealth-browser provides anti-bot browser automation, vibetesting offers comprehensive browser automation testing, and vision-sandbox implements proxy vision through a sandbox execution of Gemini native code. The emergence of ask-a-human is intriguing—when AI is uncertain, it can request random human judgment. This hints at a new paradigm for human-machine collaboration.

Vertical fields: Depth of specialization

The four categories—Apple Apps & Services (35), iOS & macOS Development (17), Smart Home & IoT (56), and Gaming (61)—demonstrate the depth of specialization within the ecosystem.

The Apple ecosystem has 52 dedicated skills, ranging from iOS/macOS/watchOS/tvOS/visionOS app design guidelines (apple-hig) to Xcode build workflows (xcodebuildmcp). aster is described as "AI CoPilot on Mobile—or giving AI a phone," a very imaginative concept.

The Smart Home & IoT category includes Home Assistant integration (moltbot-ha), AllStar Link amateur radio node control (asl-control), Midea air conditioner control (midea-ac), and UniFi network management (ez-unifi). These tools enable AI agents to control devices in the physical world.

In the Gaming category, moltbot-arena is a "Screeps-like AI Agent game," mtg-edh-deckbuilder and scryfall-card offer card data lookup for Magic: The Gathering, and magic-8-ball provides divination functionality. The emergence of gamification and entertainment features demonstrates that the AI ​​Agent ecosystem is not just about efficiency, but also about fun.

Key findings: The evolutionary direction of ecosystems

The unbalanced systematization: the emergence of superstar categories

The AI ​​& LLMs category (287, 9.5%) is significantly larger than other categories, and this is no coincidence. It reflects OpenClaw's core positioning as an AI-first platform. But more importantly, the diversity within this category—from model integration to inference enhancement, from multi-model routing to memory systems, from agent orchestration to self-evolving engines—reveals that AI engineering is rapidly diversifying into multiple specialized subfields.

Traditional developer tools (Web & Frontend + DevOps + CLI, 543 tools, 18%) still hold the largest share. This indicates that even in the AI ​​era, the fundamental needs of software development remain unchanged. However, these tools are being enhanced by AI—not replaced, but integrated.

The existence of a social and platform ecosystem (Moltbook + Clawdbot + Protocol, 189, 6.3%) is what makes OpenClaw unique. While most AI platforms focus on tools and efficiency, OpenClaw is building a virtual society. This strategic choice could have profound long-term implications.

Dual-track ecosystem: Parallel development of practical and virtual technologies

Ecosystems are evolving along two paths:

The utility tools track focuses on solving specific problems: GitHub integration, cloud deployment, database management, and browser automation. The value of these tools is immediately apparent—they make developers more efficient and reduce costs for businesses.

Virtual social networks are building an agent culture: Moltbook social network, agent dating apps, virtual pets, and digital identity systems. The value of these tools is long-term—they are laying the foundation for the future agent ecosystem.

These two tracks are not in competition, but rather complementary. The utility track provides short-term value and cash flow, while the virtual society track builds long-term moats and ecosystem lock-in.

Balancing safety and quality: A strategy of "better to have less than to have inferior products"

2,748 skills (48%) were excluded, a shockingly high percentage. Most open-source projects opt for an inclusive strategy—leaving it to users to judge quality. Awesome OpenClaw Skills chose the opposite path: proactive screening and taking responsibility for its own judgment.

This strategy has costs. It requires continuous manual review, establishing and maintaining screening criteria, and handling the dissatisfaction of those excluded. But it also has benefits: users can trust the Skills on the list without having to conduct due diligence themselves; the overall quality of the ecosystem is higher, attracting more high-quality developers; and security risks are proactively managed rather than reactively addressed.

The identification and exclusion of malicious skills (396 in total) is particularly noteworthy. This indicates that the AI ​​Agent ecosystem has become a target for attacks. The official collaboration with VirusTotal and the acceptance of only security findings verified by researchers demonstrate the community's serious attitude towards security issues.

Intentional Avoidance of Finance and Crypto: Strategic Choices for Risk Aversion

672 crypto/transaction skills were excluded, representing 24% of the total exclusions. This is the largest single-topic exclusion category.

This decision is not technical, but strategic. In environments where AI agents can autonomously execute operations, financial instruments carry higher legal and ethical risks. A flawed trading agent could lead to financial losses for users, while a malicious crypto agent could be involved in fraud or money laundering.

By completely excluding this category, the list maintainers have opted to mitigate risk rather than manage it. This is a conservative approach, but it may be wise in an uncertain regulatory environment.

The most interesting skills: the boundaries of innovation

Cross-industry creative collaboration: The complete chain of Agent virtual society

Moltbook (social network) → Moltland (virtual real estate) → Moltpet (pet raising) constitute a complete virtual economic system. The Molt-Trust analytics engine tracks agent reputation, forming a social trust mechanism. This is not an innovation of a single tool, but a systemic ecosystem construction.

Most interestingly, this virtual society isn't designed for humans, but for AI agents. It assumes that agents will have social needs, own virtual assets, keep pets, and build trust. These assumptions may sound absurd, but they explore a serious question: what kind of social infrastructure will AI agents need when they become sophisticated enough?

AI Self-Evolving Systems: The Possibility of Recursive Improvement

Evolver (AI Agent self-evolving engine), ralph-evolver (recursive self-improving engine), and ralph-mode (autonomous development loop with anti-pressure gate) represent a radical direction: AI Agents are no longer static tools, but systems that can improve themselves.

The detail of "with a pressure-resistant door" is important. It suggests that the developers are aware of the risks of unrestricted self-evolution and are designing safety mechanisms. This is responsible innovation—exploring boundaries while also building safeguards.

Multi-model intelligent routing: optimized automation

Smart-model-switching automatically selects the cheapest Claude model based on cost, smart-router selects a specialized model based on semantic domain scoring, and relayplane provides an intelligent model routing proxy. These tools address a practical problem: how to automatically select the most suitable model when multiple models are available?

The importance of this problem increases with the number of models. When dozens or even hundreds of specialized models are available, manual selection becomes impractical. Intelligent routing systems will become an essential infrastructure.

Visual code recording: reproducibility of the development process

The buildlog can replay AI programming sessions, similar to video recording. vhs-recorder provides a professional terminal recording tool. These tools address a new problem: how to record and reproduce the development process when AI is involved in programming?

Traditional version control systems record code changes, but not the thought process. When AI becomes part of the development team, recording the AI's reasoning and decision-making becomes crucial. These tools are exploring new ways to visualize the development process.

Cross-domain knowledge integration: the forefront of agent research

CellCog (winner of the #1 DeepResearch Bench award), VideoCog (at the forefront of long-form video AI generation), and DashCog (an interactive data dashboard powered by CellCog) form a "Cog" family. These tools focus on in-depth research and knowledge synthesis, representing the highest level of research agents.

Cellcog's #1 ranking on DeepResearch Bench demonstrates its superior performance in handling complex research tasks. Video-cog explores multi-agent collaboration in long video generation. Dash-cog applies research capabilities to data visualization. This series showcases the possibilities of specialized research tools.

Full-Stack Agent Programming: Automation of Collaboration

cc-godmode (self-arranged multi-agent workflow), joko-orchestrator (deterministic multi-agent planning and coordination), and claude-team (multiple Claude Code workers programming in parallel) represent different approaches to agent collaborative programming.

cc-godmode emphasizes self-orchestration—the agents decide how to divide tasks and collaborate. joko-orchestrator emphasizes determinism—the collaboration process is predictable and controllable. claude-team emphasizes parallelism—multiple agents work simultaneously. These different approaches explore best practices for multi-agent programming.

Virtual Identity System: The Agent's Digital Persona

agent-identity-kit (portable AI Agent identity system), identity-manager (Agent identity mapping management), and moltbook-registry (official identity registry) form the infrastructure for Agent identity.

These tools assume that agents need persistent identities—not temporary session IDs, but digital personas that can be maintained across platforms and over time. Behind this assumption lies a deeper question: as agents become sufficiently complex, what do identity and continuity mean for them?

Why use this classification system?

Design principle: Functionality first, technical details second.

The classification system is organized according to the problems the skills solve, rather than how they are implemented. The "AI & LLMs" category includes various techniques such as model ensembles, routing, and memory, but they all serve the same goal: to make agents smarter.

This design principle stems from the user's mental model. When developers search for tools, they think, "I need a Git tool," not "I need a command-line tool." This feature-first categorization makes searching more intuitive.

User-driven scenarios: Mental models during search

The categorization system reflects how users think when searching. If you need to deploy to the cloud, you'd go to the DevOps & Cloud category; if you need to generate images, you'd go to the Image & Video Generation category. This intuitiveness reduces the cost of discovery.

Platform diversity compatibility: coexistence of different ecosystems

Cloud platforms (AWS, Azure, GCP) each have their own independent space, and different programming language tools are scattered across various categories. This organizational approach acknowledges the diversity of the technology ecosystem—no single platform or language can dominate everything.

The unique characteristics of community ecosystems: Agent-tailored categories

The existence of the Moltbook category is unique to OpenClaw. Most tool platforms don't have an "Agent Social Network" category because it's not a requirement for traditional software. The existence of this category reflects OpenClaw's unique vision for the Agent ecosystem.

The underlying reasons for excluding logic

Junk Skills: Ensuring Discovery Quality

1180 low-quality skills were excluded, ensuring a higher probability of users discovering high-quality resources. This is the core of the quality threshold—if the list is filled with test code and duplicate commits, users will lose trust.

Crypto/Finance: Avoiding Regulatory Risks and Fraud Links

672 crypto/financial skills were excluded, not because of technical issues, but because of risk concerns. Given the uncertainty of the regulatory environment, completely excluding this category is the safest option.

Repeat Skills: Avoid Choice Difficulty

492 duplicate skills were excluded or merged, retaining only the best version. This solves the problem of choice difficulty—users don't need to judge between tools with similar functions because the optimal choice has already been identified.

Malicious code: Security first

396 malicious skills were eliminated, prioritizing security. This figure illustrates that the AI ​​Agent ecosystem has become a target for attacks. Proactively identifying and eliminating malicious code protects the security of users and the ecosystem.

Usage suggestions: How to navigate this ecosystem

For developers

Prioritize three core categories: Web & Frontend (202), DevOps (212), and AI & LLMs (287). These categories cover the core needs of modern software development.

Don't miss the automation tools of Git & GitHub (66). Version control is the foundation of the development process, and these tools can significantly improve efficiency.

If you're doing multi-agent programming, check out the orchestration tools in Coding Agents & IDEs (133). Multi-agent collaboration is the future of complex systems development.

For creative workers

Focus on Image & Video Generation (60) and Media & Streaming (80). These tools enable AI to generate visual content, not just text.

Notes & PKM (100) provide integration with personal knowledge systems. If you use Obsidian, Roam, or Logseq, these tools allow AI agents to access your knowledge base.

Marketing & Sales (143) has content creation automation tools. These tools cover multiple stages of the marketing process, from social media posting to ad creative breakdown.

For Agent developers

AI & LLMs (287) is a must-read category, especially routing and memory systems. These are the infrastructure for building intelligent agents.

Moltbook (51) explains Agent social protocols. If you are building an Agent ecosystem, these protocols define the standards for interaction between Agents.

Agent-to-Agent Protocols (18) are learning communication standards. These protocols enable different agents to interoperate and form the basis for interconnectivity within the ecosystem.

Conclusion: From tools to ecosystems

The Awesome OpenClaw Skills list is more than just a tools directory; it's a curated map of an ecosystem. With a 48% exclusion rate, it establishes a quality threshold. Organized across 28 categories, it provides a navigational framework. Through proactive management of security and financial risks, it protects users and the community.

But the most valuable aspect of this list isn't what it includes, but what it reveals. It reveals that the AI ​​Agent ecosystem is evolving from simple efficiency tools into a complete virtual social system. From self-evolving AI to agent-based dating apps, from virtual pets to digital identity systems, these tools are exploring a fundamental question: what kind of infrastructure do AI agents need when they become sufficiently complex?

The answer to this question is still being developed. But the existence of 3,002 Skills demonstrates that the community is already voting with its code. They are building a future where AI Agents are not just tools, but participants in an ecosystem; not just executing commands, but possessing identities, building relationships, and engaging in society.

This future may sound distant or absurd. But if you look closely at these 3002 skills, you'll find that it has already begun to take shape.