SkillNet: Create, Evaluate, and Connect AI Skills

arXiv:2603.04448 · cs.AI, cs.CL, cs.CV, cs.LG, cs.MA · Submitted 2026-08-23 · Read on arXiv

Listen

Radio episode about this paper

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "SkillNet: Create, Evaluate, and Connect AI Skills".

Jane: The paper was written by Yuan Liang, Ruobin Zhong, Haoming Xu, Chen Jiang, Yi Zhong et al. from Zhejiang University and Tongji University and Southeast University and Alibaba Group and Ant Group and Tencent Company, Limited (Brand Name) and OPPO (Brand Name) and HomologyAI (Brand Name) and Fudan University and MemTensor Technology in Shanghai, Limited and University of California, Los Angeles and University of California San Diego and The University of Edinburgh and Monash University and National University of Singapore and Nanyang Technological University (Full Name) and Huzhou University (Full Name) and Hornor Device Company Limited and Hangzhou Institute for Advanced Study, UCAS (University of California, San Diego).

Tom: Stay tuned as we take you through the paper and discuss its implications.

Summary: Jane: So, moving past just defining what a skill is, the paper really zeroes in on *how* to manage them through its summary. They propose a structured methodology for both creation and rigorous evaluation across diverse tasks.

Tom: Right, it's not enough just to say "we need skills"; we need to know how to build them out and prove they work consistently, no matter the task domain. What does this summary section actually detail about that process?

Meng: What jumped out at me was the emphasis on 'diverse tasks.' It means they aren't training for one type of scenario; they're making sure the skill is generalizable, which is what any engineer wants—a robust component.

Lu: I think the core contribution in the summary is detailing the mechanism for interconnectivity. They show how linking skills together forms a 'network,' meaning simply having good individual skills isn't enough; they must interact correctly under complex prompts.

Jane: Exactly, Lu. Think of it like a team project: one person is great at writing, another at research, but if they don't know how to hand off their output to the next person smoothly, the final product falls apart. SkillNet addresses that handoff mechanism.

Lalam: And when they talk about evaluation across diverse tasks, I see this as a massive boost to AI equity; it means we can benchmark capabilities fairly across different types of users and industrial applications globally.

Tom: So, it’s not just about passing a test for one thing, but proving competence in a whole suite of related activities. Meng, how does this evaluation process change what you'd build in your startup?

Meng: It forces us to document our internal system logic much more rigorously. Instead of having opaque black boxes where the AI just 'works,' we have to map out the specific inputs, outputs, and failure modes for every single skill we claim it has. That’s a huge operational improvement.

Jane: And that rigor is what builds trust with end-users, isn't it? It gives them confidence because they know the system isn't magic; it's built on verifiable components.

Lu: The methodology itself seems to incorporate advanced testing paradigms, pushing beyond simple accuracy metrics and into complex reasoning chains. This moves the goalposts for AI capability testing significantly higher.

Lalam: From a systemic perspective, this formalized structure of 'SkillNet' provides a common language for researchers and industry partners worldwide, accelerating the pace of responsible innovation by standardizing excellence.

Tom: So we've covered the foundation and the mechanics of defining skills. But what about next steps? The paper also discusses improvements or potential future work for this framework. That sounds like where things get even more exciting, right?

Improvements: Jane: Right, because no framework is ever finished! The discussion around suggested improvements shows that the community sees immediate pathways to make SkillNet even better and more comprehensive.

Tom: It’s less about fixing flaws in the core concept and more about expanding its reach. What kind of expansion are they suggesting? Are we talking about adding new types of skills, or improving how they connect?

Lu: I noticed a focus on integrating 'dynamic skill adaptation.' That suggests that instead of just defining skills upfront, the system should be able to observe a gap in capability and proactively learn or incorporate a new, temporary skill on the fly.

Meng: From an implementation standpoint, dynamic adaptation is incredibly difficult because it introduces variables into the system's definition. How do you reliably test something that changes its own structure while running? That’s a massive computational hurdle.

Jane: But if we can solve that—if we can make the system self-correct its skillset—then AI agents move from being sophisticated tools to becoming true, adaptive colleagues.

Lalam: This push for dynamic adaptation is deeply aligned with how human intelligence works; we don't have a fixed skill set; we learn and adjust based on the environment and the

Paper discussion segment 3: Tom: The paper really emphasizes that this framework is meant to be an open-world system, so the next steps focus on making it dynamic and adaptable rather than static.

Jane: That means the agents don't just rely on what we gave them initially; they should be able to discover and learn new skills as they encounter problems in unexpected ways.

Lu: I am incredibly excited about that idea of dynamic skill acquisition, especially when you consider the potential for cross-domain transfer, which allows the agent to pull a concept from one area and apply it in another way before the entire process is even defined.

Meng: But how do we build that into a real system? If an agent is constantly generating new skills on the fly, we need automated systems that are robust enough to handle those new inputs without breaking existing ones.

Lalam: The implication of continuous learning is a shift in human-AI interaction; instead of managing fixed tools, we' manage an evolving partnership where the AI gains expertise as it works alongside us.

Tom: Exactly, Lalam, so we aren're talking about moving beyond just a library of fixed tools to an agent that can actually self-improve its own skillset over time.

Jane: It’s about making sure the system evolves with real-world data rather than just relying on old datasets, which makes sense for any long-term application.

Lu: And we also have this idea of multi-agent collaboration, where SkillNet acts as a shared knowledge base that allows different agents to share their findings and build complex workflows together seamlessly.

Meng: That sounds like a massive coordination challenge. We need protocols to ensure that when one agent uses a skill, the other agents can understand the output and trigger the next step without any communication lag.

Lalam: A truly collective intelligence where multiple specialized avatars, each having unique skills, can function as a unified team toward achieving goals.

Tom: So we've seen how SkillNet is pushing towards dynamic evolution and multi-agent coordination; but what about the specific relationship between the AI models themselves and these external skills?

Jane: The paper suggests looking at model-skill synergy, which means making sure the LLM isn's just picking a skill but is actively guided by its structure to make better decisions.

Meng: That’s where we need to see some deep integration; how do we ensure the neural network doesn't override a specific, verified skill instruction because of model hallucination?

Lu: The potential here is that the the skills act as constraints on the model's output, forcing the structure and ensuring that our reasoning always aligns with verifiable, executable knowledge.

Lalam: It elevates AI from merely guessing to reliably executing complex tasks based on a shared, evolving understanding of excellence.

Tom: This ability to enforce reliable execution is crucial for scaling up. But how does this relate to other systems already out there?

Conclusion: Tom: So, we’ve seen how SkillNet is designed to be an open infrastructure for creating and organizing AI skills at scale, which is a huge step forward for how we think about agentic work.

Jane: It moves us away from just having a big pile of random tools to having a structured, reliable system that helps build up capability over time.

Lu: I’m truly optimistic because it creates this foundational knowledge base that allows for the scalable, continuous improvement of AI agents across different domains.

Meng: The practical impact is huge; we finally have a way to ensure the consistency and maintainability of these skills in production-level AI applications at scale.

Lalam: This whole concept suggests a future where AI doesn't just execute commands but acts as a consistently reliable, self-improving partner that elevates the standard of work itself.

Tom: It’s clear that by formalizing skills into an interconnected network, we are setting the stage for truly durable and transferable mastery in AI.

Jane: We’ve really seen how this infrastructure supports systematic accumulation, which is a huge win for any kind of long-term project.

Lu: I think the structure of the skill ontology will allow us to explore capabilities that were simply too complex to handle with existing models, opening up such interesting new research avenues.

Meng: I just hope that this framework can help accelerate the deployment of these skills into real-world scenarios without getting bogged down in excessive administrative overhead.

Lalam: SkillNet: Create, Evaluate, and Connect AI Skills provides the scaffolding needed to turn fragmented experience into a cohesive, reliable intelligence that benefits us all.

Tom: That’s a perfect way to sum it up. It’s definitely going to change how we look at agentic systems.

Jane: And I think I can't wait to see what kind of applications this drives in the next few months.

Lu: Are you ready to see how this affects the future, or do we need a little more time?

Yuan Liang, Ruobin Zhong, Haoming Xu, Chen Jiang, Yi Zhong, Runnan Fang, Jia-Chen Gu11, Shumin Deng15, Yunzhi Yao1000003333444556667788999922222, Mengru Wang1, Shuofei Qiao1, Xin Xu12, Tongtong Wu14, Kun Wang16, Yang Liu16, Zhen Bi17, Jungang Lou17, Yuchen Eleanor Jiang7000003333444556667788999922222, Hangcheng Zhu4, Gang Yu4, Haiwen Hong4, Longtao Huang4, Hui Xue4, Chenxi Wang10000033334455667788999922222, Yijun Wang6, Zifei Shan6, Xi Chen6, Zhaopeng Tu6, Feiyu Xiong10000033334455667788999922222, Xin Xie5, Peng Zhang5, Zhengke Gui5, Lei Liang5, Jun Zhou5, Chiyu Wu8, Jin Shang8, Yu Gong8, Junyu Lin9000003333444556667788999922222, Changliang Xu19, Hongjie Deng1, Wen Zhang1, Keyan Ding1, Qiang Zhang1, Fei Huang18, Ningyu Zhang†000003334455667788999922222, Jeff Z. Pan13, Guilin Qi3, Haofen Wang2, Huajun Chen1

Zhejiang University · Tongji University · Southeast University · Alibaba Group · Ant Group · Tencent Company, Limited (Brand Name) · OPPO (Brand Name) · HomologyAI (Brand Name) · Fudan University · MemTensor Technology in Shanghai, Limited · University of California, Los Angeles · University of California San Diego · The University of Edinburgh · Monash University · National University of Singapore · Nanyang Technological University (Full Name) · Huzhou University (Full Name) · Hornor Device Company Limited · Hangzhou Institute for Advanced Study, UCAS (University of California, San Diego)

cs.AI, cs.CL, cs.CV, cs.LG, cs.MA

Submitted: 2026-08-23

Updated: 2026-08-25

Comments: http://skillnet.openkg.cn/; add SkillNet-Gym, a benchmark for evaluating skill retrieval, utilization, composition, and SkillNet-Fabric for task-specific skill routing through lightweight Wikis

Code: https://github.com/zjunlp/SkillNet

Project page: http://skillnet.openkg.cn

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 89/100

The gist: " * Summary of SkillNet: Create, Evaluate, and Connect AI Skills The paper addresses a critical limitation in current agentic systems: the lack of a systematic mechanism for skill consolidation and

Key concepts

SkillNet
A structured methodology proposed in the paper for managing AI skills. It provides a framework to build and rigorously evaluate skills across diverse tasks, moving beyond simple definitions to establish verifiable components.
Dynamic Skill Adaptation
The ability for an AI system to observe a capability gap and proactively learn or incorporate new, temporary skills on the fly. This moves the system from being static to self-correcting and adaptive.
Multi-agent Collaboration
A concept where SkillNet acts as a shared knowledge base, allowing multiple specialized AI agents to share findings and work together seamlessly to build complex workflows toward a common goal.
Model-Skill Synergy
The idea of guiding the Large Language Model (LLM) using the structure of defined skills. This ensures that the LLM's decisions are actively constrained by verifiable, executable knowledge rather than relying solely on its internal generation.

Terminology

Summary

"

Summary of SkillNet: Create, Evaluate, and Connect AI Skills

The paper addresses a critical limitation in current agentic systems: the lack of a systematic mechanism for skill consolidation and transfer. The authors argue that without a unified mechanism for skill consolidation, agents frequently reinvent the wheel, rediscovering solutions in isolated contexts without leveraging prior strategies. This gap highlights a tension between transient experience and long-term capability, which is exacerbated by the fact that current AI methods rely on manual engineering or transient in-context learning.

To overcome these limitations, the authors introduce SkillNet, an open infrastructure designed to "create, evaluate, and organize AI skills at scale. SkillNet structures skills within a unified ontology that supports creating skills from heterogeneous sources, establishing rich relational connections, and performing multi-dimensional evaluation."

SkillNet Architecture and Methodology:

The SkillNet framework is built upon three core modules: Skill Creation, Skill Evaluation, and Skill Analysis.

  1. Skill Creation (Abstraction): This module transforms fragmented human knowledge into structured capabilities. It employs a multi-source automated skill creation pipeline which abstracts skills from four major categories of data sources:

  • Execution trajectories and conversational interaction logs.

  • Open-source GitHub repositories.

  • Semi-structured documents (PDF, PowerPoint, and Word files).

  • Direct natural language prompts provided by users.

  1. Data-Driven Filtering and Consolidation: To ensure quality, SkillNet applies a multi-stage curation process: deduplication is performed by jointly comparing skill directory structures and MD5 hashes, followed by filtering (eliminating low-quality or semantically meaningless skills), categorization (into ten functional categories such as Development, AIGC, Research, etc.), and tagging.

  2. Skill Evaluation: The framework rigorously validates the skills using a multi-dimensional evaluation protocol that assesses five core dimensions:

  • Safety: Assesses potential risks like unauthorized file deletion or adversarial manipulation.

  • Completeness: Evaluates whether the skill encapsulates all critical procedural steps and prerequisites.

  • Executability: Verifies if the skill can be successfully implemented in sandboxed environments, identifying hallucinated tool calls or ambiguous instructions.

  • Maintainability: Measures the modularity and composability of skills.

  • Cost-awareness: Quantifies execution overhead, including time latency, computational resource consumption, and API usage costs.

  1. Skill Analysis (Relational Modeling): SkillNet constructs a large-scale skill graph that models structural relations between skills. These relations include:

  • similar to: For redundancy detection and interchangeability.

  • belong to: To capture hierarchical structures and modularization.

  • compose with: To enable automatic workflow composition and pipeline generation.

  • depend on: For explicit dependency tracking and safe execution planning.

Open Resources: SkillNet has aggregated over 200,000 candidate skills from open internet resources, with more than 150k high-quality skills are curated in the final repository. This is supported by an open infrastructure including a front-end website and a versatile Python toolkit (skillnet-ai).

Experimental Results:

The effectiveness of SkillNet was evaluated across three text-based simulated environments: ALFWorld, WebShop, and ScienceWorld. The results demonstrated that agents augmented with SkillNet achieved substantial gains:

  • SkillNet significantly enhances agent performance, improving average rewards by 40% and reducing execution steps by 30% across multiple backbone models.

The performance improvements were robust across various backbone models (e.g., DeepSeek V3.2, Gemini 2.5 Pro, and o4 Mini), validating that systematic skill accumulation effectively enhances agent competence cumulatively rather than episodically.

Key Contributions:

  1. SkillNet is introduced as a unified framework for action-able knowledge engineering, transforming fragmented experience into a structured network of modular, composable skills.

  2. A rigorous skill evaluation protocol is established to quantitatively measure safety, completeness, executability, maintainability, and cost-awareness.

  3. The release of an open-source ecosystem (over 200k candidate skills) provides benchmarks that empirically demonstrate significant performance improvements in agent planning and execution tasks.

Improvements for AI systems

(Note: Given the extremely high stakes of this role, I must synthesize these disparate references into a coherent, state-of-the-art architectural improvement rather than listing simple features. The core breakthrough lies in moving from single-pass LLM calls to a robust, self-correcting agentic loop.)

The primary systemic improvement required is the transition from monolithic Large Language Model (LLM) calls to a Meta-Agent Architecture built around three interconnected, dynamic layers: Procedural Memory, Skill Graph Management, and Iterative Self-Improvement.

  • Improvement: Implement a dual-component memory system:

  1. Epistemic/Procedural Memory: A structured, graph-based knowledge base that records the steps taken, the inputs required, and the outputs generated for specific task sequences (e.g., To book a flight, step 1 requires Origin Code X and Date Y). This is not simple retrieval (RAG); it is a record of successful execution paths.

  2. Generative Latent Memory: A compressed, high-dimensional vector representation of the overall context and failure modes encountered across multiple sessions. This allows the agent to generalize lessons learned (We failed because we ignored time zone shifts) rather than just retrieving specific facts.

  • System Capability: The improved system can maintain coherence over weeks-long, multi-domain tasks. It can diagnose why a previous attempt failed (e.g., The error was not in the data input, but in the assumption that the API endpoint was synchronous) and proactively adjust its plan before execution.

  • Improvement: Replace simple function calling with a Skill Graph. This graph treats all tools and external APIs not as discrete functions, but as nodes capable of polymorphic abstraction.

  1. Skill Discovery & Refinement: The agent must actively monitor its own execution failures and gaps in knowledge. When a task requires an ability it lacks (e.g., I need to scrape data from a paywalled PDF), it initiates a meta-search process, proposing or refining the necessary skill (e.g., I will first use Skill A to find the archive link, then Skill B for OCR, and finally Skill C for structured extraction).

  2. Skill Composition Engine: The system must dynamically compose complex workflows by chaining multiple foundational skills into novel mega-skills that were never explicitly defined in the initial prompt (e.g., combining API authentication + Web scraping + Data normalization into a single, callable Extract Corporate Financials skill).

  • System Capability: The agent can self-improve its toolkit in real-time. It does not just use existing skills; it designs and registers new, reusable skills into its personal graph, making it continually more capable with every interaction.

  • Improvement: Integrate a mandatory Reflection/Critique Phase after every significant task completion or failure. This phase utilizes a dedicated, specialized LLM instance (the Critic Module) that does not execute code but only evaluates the process.

  1. Hypothesis Testing: The Critic Module generates alternative reasoning chains and critiques the efficiency, robustness, and safety of the executed plan.

  2. Reinforcement Learning Integration: These critiques are used to generate synthetic training data for a lightweight Policy Model (the RL component). This allows the agent to learn from its failures much faster than standard fine-tuning, optimizing its decision policy (e.g., When faced with ambiguity between Source A and Source B, always prioritize checking the metadata timestamp first).

  • System Capability: The system achieves Goal-Directed Self-Correction. It moves beyond merely answering questions; it learns how to answer them better. If a task is complex, it generates a detailed, human-readable Self-Improvement Report detailing what it learned about its own limitations and how those limitations were patched for the next attempt.

Sources

Related papers