Introduction

As AI tools become standard in daily productivity, Claude Skills is redefining the underlying logic of human-AI collaboration. This article deeply analyzes how Skill design philosophy breaks through the limitations of traditional prompts, revealing how Anthropic addresses the core pain points of AI performance instability through a systems engineering approach, equipping you with the design methodology for next-generation AI collaboration tools.

Every time you open Claude and say, “Help me write a product analysis report,” it can give you a seemingly decent document. However, if you have 20 different tasks—writing reports, analyzing data, coding, replying to emails—you may find a frustrating reality: the AI’s performance fluctuates, with good outputs one day and poor ones the next. The issue isn’t with the AI itself; it’s that you’re reinventing the wheel each time. The emergence of Skills aims to resolve this fundamental contradiction.

Misunderstanding Skills

“Isn’t a Skill just a prompt?” This is the most common and harmful misconception about Skills. If you view Skills as “a piece of text fed to AI,” you only see the surface. The true definition of a Skill is: a carefully designed capability loading mechanism—it answers four fundamental questions: when to trigger, what to load, what to execute, and how to iterate.

This is a systems engineering proposition, not a prompt-writing proposition. What’s the difference? Prompts are static, while Skills are dynamic. Prompts are point-to-point instructions, while Skills are combinable units of capability. Once a prompt is written, it ends; Skills require testing, iteration, and coexistence with other Skills. Anthropic defines Skills as:

“A skill is a set of instructions—packaged as a simple folder—that teaches Claude how to handle specific tasks or workflows. Instead of re-explaining your preferences, processes, and domain expertise in every conversation, skills let you teach Claude once and benefit every time.”

“Teach once, benefit forever”—this is the essence of Skills. It’s not about telling AI “what to do” but about enabling AI to do the right thing at the right time and in the right way. This is the core conflict. With the same Claude capabilities, some achieve a 90% task success rate using Skills, while others start from scratch with only 30%. The difference lies not in the model but in the quality of Skill design.

Historical Context of Skills

To understand Skills, they must be placed back in historical context. They are not a random invention but an inevitable product of the evolution of AI capability expression.

Pre-Skill Era

Reinventing the earliest uses of AI was quite simple: users spoke a sentence, and AI responded. There was no memory, no context accumulation, and no capability reuse. Every conversation began with AI as a blank slate. After discussing data analysis preferences for half an hour, the next time you open the window, everything resets. This means that all high-quality outputs are built on repetitive labor. Professional users quickly realized that this was not a sustainable usage model.

System Prompt Era

Around 2022, the concept of System Prompts began to gain popularity. Essentially, this involved “hardcoding” commonly used instructions at the beginning of conversations, allowing AI to possess a certain role or capability upon startup. This was much better than re-explaining each time. However, System Prompts have three fundamental limitations:

1. Inability to load on demand. Regardless of the current task, all content of the System Prompt is loaded into the context. The more complex the task, the longer the System Prompt, leading to severe context bloat. Anthropic’s documentation specifically points out that cramming a large amount of Skill content into SKILL.md is one of the most common mistakes.
2. Incompatibility. Multiple System Prompts often interfere with each other. When you have a “data analysis expert” System Prompt and a “code reviewer” System Prompt coexisting, AI’s behavior often contradicts itself due to the lack of a clear loading and triggering mechanism.
3. Inability to iterate. System Prompts are a “one-time purchase”; once written, they are fixed. Without built-in testing, validation, and feedback mechanisms, you can only rely on intuition to judge effectiveness.

Plugin/GPTs Era

In 2023, OpenAI launched the Plugin ecosystem and GPTs, attempting to solve the capability expansion problem. Users can “install” a plugin in ChatGPT, allowing AI to access external APIs, obtain real-time data, and perform specific operations.

This represented a significant paradigm shift: AI evolved from a “question-answering tool” to a “task-executing tool.”

However, the Plugin system has a fundamental flaw: strong coupling and incompatibility. GPTs’ Actions are essentially wrappers of OpenAPI specifications—when you provide AI with API descriptions, it decides whether to call them based on that description. The limitation of this model is that what APIs can do is fixed, and AI can only operate within that scope.

More critically, multiple Actions within a GPT are unaware of each other’s existence and cannot coordinate to complete workflows that require cross-service collaboration.

In summary, the Plugin era addressed the question of “what tools can AI call” but did not solve the issue of “in what scenarios should AI use which tools and how should those tools collaborate.”

The Skill Era: A Qualitative Leap

By late 2024 to early 2025, Anthropic officially launched Claude Skills. Compared to Plugins, the Skill system achieved three key leaps:

1. Progressive Disclosure. This may be the most profound innovation in Skill design. It divides capability loading into three levels:
   • The first level is YAML frontmatter, always loaded but very lightweight (about 100 tokens), just enough for AI to “know” that this capability exists;
   • The second level is the SKILL.md body, loaded on demand, containing complete execution instructions;
   • The third level is references and scripts, loaded on demand, providing detailed support. Tokens are a scarce resource.

The essence of progressive disclosure is that not all capabilities need to be present simultaneously. This is a design philosophy about “when to load what,” not just a technical implementation.

2. Composability. Claude can load multiple Skills simultaneously, with clear boundaries between each Skill, allowing them to work independently without interference. This means Skills are truly modular units—you can have both a “report generation” Skill and a “code review” Skill working simultaneously without conflict.
3. Portability. The same Skill behaves consistently across Claude.ai, Claude Code, and the API. This breaks the curse of “platform binding”—a Skill debugged locally can work on any supported platform.

At this point, the core insight emerges: the evolution of Skills is fundamentally a paradigm shift from “static configuration” to “dynamic capability loading.”

The core issue in the first two eras was “how to let AI know more,” while the core issue in the Skill era is “how to let AI call the right knowledge at the right moment.” These are not different answers to the same question; they are two completely different questions.

Core Design Principles of Skills

From Anthropic’s official guidelines, we extract the most important design principles. However, the key is not to remember them but to understand why they exist.

Principle 1: YAML Frontmatter is the Lifeline

Whether a Skill can be triggered entirely depends on how well the description field in the frontmatter is written. This is not an exaggeration. A Skill can be uploaded and everything seems normal, but Claude will never automatically load it—90% of the time, it’s because the description is poorly written. The core structure of a description has three elements: what it does + when to trigger + key trigger words.

A good description example from Anthropic:

description: Manages Linear project workflows including sprint planning, task creation, and status tracking. Use when user mentions “sprint”, “Linear tasks”, “project planning”, or asks to “create tickets”.

A bad description:

description: Helps with projects.

“Helps with projects”—this summarizes the Skill’s function but does not describe the triggering conditions. When Claude receives this description, how can it determine when the user needs this Skill? It cannot. This leads to a counterintuitive but extremely important design principle: the description is not written for humans; it is written for AI’s trigger judgment. Its reader is Claude’s semantic understanding engine, not the user. What you write is not product documentation; it is a trigger condition checklist.

Good description = “what it does” (clear semantic capability boundaries) + “trigger signals” (what users might say). Bad description = vague functional summary.

Principle 2: Progressive Disclosure is Token Economics

As mentioned earlier, progressive disclosure is the most important design philosophy of Skills, and here we elaborate on why it matters. When you cram all content into SKILL.md, Claude has to process this content every time it starts up. A single Skill might be fine, but enabling 10 Skills simultaneously leads to a context window filled with various capability descriptions, severely distracting AI’s attention.

The practical impact: slower responses and increased costs.

Claude consumes computational resources for each context token processed, resources that could be used to handle your actual tasks. Anthropic’s advice is specific: keep SKILL.md concise, moving detailed documentation to the references/ directory for on-demand referencing.

This may seem like a file organization issue, but it is fundamentally a matter of managing capability loading priorities.

In practice, there are several operational tips:

• Store API documentation, example code, and detailed error handling instructions in the references/ directory. These files are not loaded by default and are only referenced when Claude needs to understand specific details.
• Store executable scripts in the scripts/ directory—such as data validation scripts or format-checking scripts. These scripts are called during Skill execution instead of describing validation logic in natural language.
• Code is deterministic; language descriptions are not.
• When validation rules are complex, using scripts is much more reliable than using textual descriptions.

Principle 3: Specificity of Instructions Determines Execution Quality

This is the area in Skill design where problems are most likely to arise and where there is the most room for improvement. Vague instructions:

“Continue after validating data”

This instruction has too much “interpretive space.” What does validation mean? Which fields should be validated? What counts as validation success? What happens if validation fails? When faced with such instructions, Claude either has to guess or repeatedly confirm, both of which lead to unsatisfactory results. Specific instructions:

“Run python scripts/validate.py –input {filename} to check data format. Common reasons for validation failure include: missing required fields (return the names of missing fields to the user) and incorrect date formats (use YYYY-MM-DD format). Only proceed to the next step after all validations pass.”

The difference lies in the specific instructions that tell AI which tool to use, what parameters to pass, what output to expect, and how to handle failures.

With all four elements in place, Claude’s execution path becomes clear. Anthropic particularly emphasizes a high-level technique: for critical validations, consider encapsulating the checking logic as a script rather than describing it in natural language.

The logic behind this is simple: the results of code execution are deterministic, while the outputs of language models are probabilistic. When precision is important, abandon probability.

Principle 4: Composability is a Design Constraint, Not an Option

When designing a Skill, you must assume it is not the only one in existence. This assumption brings specific constraints: Skills cannot monopolize global context, cannot assume all tools are available, and cannot make conflicting behavioral assumptions with adjacent Skills. Conversely, this requires you to clearly declare what capabilities your Skill depends on (through the compatibility field) and what scenarios it does not handle (through negative triggers in the description). Anthropic’s documentation provides an example of a negative trigger:

description: Advanced data analysis for CSV files. Use for statistical modeling, regression, clustering. Do NOT use for simple data exploration (use data-viz skill instead).

This boundary declaration appears to “reject” certain scenarios, but it actually protects the Skill’s focus. A Skill that claims to do everything effectively does nothing well.

Principle 5: Portability Begins with Naming

A detail in Skill design that is easily overlooked is the naming conventions for folders and the name field.

Anthropic’s rules are clear: use kebab-case (all lowercase, hyphen-separated), prohibit spaces, underscores, and uppercase letters. Folder names must match the name field exactly.

Correct

name: figma-design-handoff

Incorrect

name: Figma_Design_Handoff

This is not a formatting quirk; it is the foundation of system compatibility. When a Skill may migrate between Claude.ai, Claude Code, and the API, any naming inconsistency can lead to loading failures. Naming conventions are the most basic guarantee of portability.

Comparative Overview of Skill Systems Across Platforms

Skills are not just Anthropic’s solo act. Placing them in a broader perspective allows us to see their characteristics and limitations more clearly.

Comparison Framework

I selected four representative platforms for comparison: Anthropic Claude Skills, OpenAI GPTs/Actions, Coze Bot, and LangChain/LangGraph.

Anthropic Claude Skills: The Leader in Precision

The Claude Skill system leads the industry in “precision of capability loading.” The three-tier progressive loading is the most elegant context management solution to date, resolving the core contradiction of the Plugin era: the tension between capability richness and context bloat.

YAML frontmatter as a triggering judgment layer is a small but beautiful design decision. Loading only a few hundred tokens of metadata allows AI to “perceive” all available Skills and only load complete content when needed—this fundamentally optimizes token usage efficiency.

However, its limitations are also evident: Skills themselves lack execution tools and rely on the MCP (Model Context Protocol) for the tool layer. Skills tell you “how to do it,” while MCP allows you to “do it.” Only the combination of the two creates a complete capability loop.

Additionally, the Claude Skill system is relatively new (maturing in 2025), and its community ecosystem and toolchain are not as rich as those of older platforms.

OpenAI GPTs/Actions: Configuration-Driven but Strongly Coupled

The greatest advantage of GPTs/Actions is their low barrier to entry—anyone can create a GPT that can call external APIs in just a few minutes. However, this advantage is also its limitation.

Actions are built on OpenAPI specifications, essentially telling ChatGPT the description of your API. This solves a basic problem: how does AI know what APIs it can call? But the OpenAPI specification is descriptive; it tells you “what the interface is” but does not tell you “when to use which interface and how to coordinate multiple interfaces.”

Multiple Actions within GPTs are isolated and lack an inherent cross-Action coordination mechanism. When you need to read events from Google Calendar, send notifications via Slack, and write the results back to Notion—GPTs cannot orchestrate this process; you need to write additional logic or rely on an intermediary like Zapier.

In simple terms: GPTs are suitable for single-point capability expansion but not for complex workflows.

Coze: The Low-Code Optimal Solution for Chinese Ecosystem

Coze represents another approach: using visual orchestration to lower the barrier for skill construction.

Coze’s Bot construction has several core components: persona and prompts (equivalent to System Prompts), plugins (equivalent to tool calls), workflows (visual node orchestration), and knowledge bases (RAG enhancement). For non-technical users, this system has a very low onboarding cost—dragging and dropping can create a usable Agent.

Workflows are the most distinctive part of Coze. It externalizes the Agent’s thought process through a graphical interface: input → LLM node processing → plugin calls → conditional branches → output. Each node has clear input and output definitions, and every step of the process is traceable.

However, Coze also has significant limitations: it is highly bound to the Coze ecosystem, and once you invest a lot of effort into building a complex workflow, the migration cost is very high. Additionally, while visual orchestration lowers the entry barrier, it also limits the upper limit of expressing complex logic—when there are enough branching processes, the graphical interface itself becomes an understanding barrier.

LangChain/LangGraph: A Workshop for Developers

LangChain and LangGraph represent a completely different user group: they are designed for programmers.

LangChain 1.0 (released in October 2025) provides the create_agent abstraction, allowing you to create an Agent with tool-calling capabilities in just a few lines of code. LangGraph provides a graphical state machine at the bottom for building highly complex Agent workflows.

From a technical perspective, LangChain/LangGraph is currently the most flexible solution: you can define any tools, any state transition logic, and any middleware processing. ReAct loops, checkpoint persistence, human-in-the-loop intervention—these advanced features are natively supported. However, the cost is that this is a code-first solution requiring Python/JavaScript programming skills. For users without an engineering background, this is an insurmountable gap.

Core Judgments

The ranking of platforms in terms of “precision of capability loading” is: Claude Skills > LangChain > Coze > GPTs.

The ranking of platforms in terms of “development convenience” is: Coze > GPTs > Claude Skills > LangChain.

Conclusion: Anthropic’s Skill system is indeed leading in engineering design but targets users with a certain level of technical understanding. To truly leverage the capabilities of Skills, users need to understand the logic of progressive loading, write effective descriptions, and organize the references structure well—this requires more cognitive investment than dragging nodes in Coze.

Both routes have their value. The market needs low-barrier solutions for broader adoption and precise systems for deep users to build truly reliable production-grade capabilities.

Five Practical Modes: From Patterns to Practice

Anthropic’s documentation distills five validated Skill execution modes. These modes are not dogma but workflow templates validated by real scenarios. Each mode has its best applicable scenarios.

Mode 1: Sequential Workflow Orchestration

Applicable Scenario: Multi-step processes where each step depends on the previous one, and the order is fixed.

Typical example: customer onboarding process—create account → configure payment → establish subscription → send welcome email. Each step’s output is the next step’s input, and failures in between require rollback.

The core of this mode is not “many steps” but clear dependencies between steps and failure handling.

Key technical points for sequential orchestration: each step clearly states dependent fields (“get customer_id from Step 1 and pass it to Step 3”); each phase includes validation logic, and without validation, it cannot proceed to the next step; provide clear rollback instructions in case of failure, not just a statement like “stop on error.”

Anthropic’s documentation emphasizes: Rollback instructions are the most easily overlooked yet most critical part of sequential workflows. If the fourth step fails, how do you handle the operations performed in the first three steps? Without this step, Skills will leave a mess in failure scenarios.

Mode 2: Multi-MCP Coordination

Applicable Scenario: Workflows that span multiple external services, each handled by independent MCPs.

Typical example: design-development handover process—export design resources from Figma MCP → store resources in Drive MCP → create development tasks in Linear MCP → notify the team via Slack MCP.

The challenge of multi-MCP orchestration lies in phase separation and data transfer.

After Phase 1 (design export) is completed, the resource links need to be captured and passed as parameters to Phase 2 (storage). After Phase 2 is completed, the storage path needs to be passed to Phase 3 (task creation). The boundaries of each phase must be clear, and the data handover must be standardized.

Another critical point of this mode is centralized error handling. When a certain MCP call fails, there needs to be a unified error handling strategy—should it retry a few times? Skip this step and continue with the subsequent process? Or should the entire process be halted? Each phase cannot operate independently.

Mode 3: Iterative Refinement

Applicable Scenario: Output quality improves with the number of iterations, and quality standards can be clearly defined.

Typical example: report generation. First draft → quality check (script validation) → identify issues (missing chapters, format inconsistencies, data validation errors) → targeted modifications → re-validation → until quality standards are met.

The essence of this mode is to shift quality control from “AI judging itself” to “programmatic validation.”

The core of iterative refinement is not “generate a few more times” but that quality standards must be established upfront. Before starting iterations, it must be clear: what constitutes a “high-quality” report? Complete structure (which chapters must be included)? Accurate data (how to validate)? Unified format (what template to use)?

Anthropic’s documentation provides a key insight: knowing when to stop iterating is as important as knowing how to iterate. Without stopping conditions, Skills may fall into an infinite loop—each generation is slightly better than the last, and then continue to modify, never outputting a final version.

Common strategies for setting stopping conditions: validation scripts return pass (programmatic standards); reach maximum iteration count (hard limit); human confirmation step (human-in-the-loop).

Mode 4: Context-Aware Tool Selection

Applicable Scenario: The same goal, but different tool choices depend on the specific characteristics of the input.

Typical example: intelligent file storage—deciding storage location based on file type and size. Large files (>10MB) go to cloud storage MCP, collaborative documents go to Notion/Docs MCP, code files go to GitHub MCP, and temporary files go to local storage.

The key to this mode is a clear decision tree and fallback options.

The decision tree must exhaust all possible input scenarios. When Claude encounters an “unseen” scenario, what should it do? The fallback option provides the answer: default to the most general option rather than reporting an error directly.

Another important design point: explain to users why this choice was made. Anthropic’s documentation considers “providing context to users” a necessary component of this mode. A transparent decision-making process builds user trust and allows users to correct issues promptly.

Mode 5: Domain-Specific Intelligence

Applicable Scenario: Skills need to embed domain knowledge beyond tool access, involving compliance or auditing requirements.

Typical example: financial compliance payment processing—transactions must check sanction lists, validate jurisdictional permissions, and assess risk levels before processing. After processing, all compliance checks must be logged for auditing.

The uniqueness of this mode lies in that compliance checks precede business operations, and auditing trails run throughout.

Many domains have such mandatory constraints: medical AI must check contraindications before recommending treatment plans, and industrial AI must verify safety conditions before executing operations. The domain-specific intelligence mode embeds these constraints into the Skill’s execution logic rather than adding them afterward.

This also reveals a deeper principle of Skill design: capability is not just about “can do” but also about “whether the way it is done complies with regulations.” A payment processing Skill lacking pre-emptive compliance checks is incomplete.

Three Most Common Traps

Skill design has three high-frequency failure points. Each has clear diagnostic methods and repair paths.

Trap 1: Skill Exists but Is Never Triggered

Diagnosis: After uploading and testing, you find that Claude never automatically loads this Skill and always requires manual specification from the user.

Root Cause: The description field is too vague or lacks trigger words.

“Helps users handle Figma-related tasks”—the problem with this statement is that “handling Figma-related tasks” is too broad a semantic space. When Claude judges whether to load this Skill, it needs to find sufficiently specific matching signals in the description.

Repair Plan:

1. First, validate using Anthropic’s suggested method: directly ask Claude, “When would you use the [Skill Name] Skill?” Claude will reference its description content. Based on the quoted content, determine whether it includes sufficiently specific triggering scenarios.
2. Add trigger words. Anthropic explicitly suggests that descriptions should include “what users might say”—including specific file types (.fig, .sketch), specific action verbs (“export,” “handoff,” “generate specifications”), and specific product names (Linear tasks, Figma designs).
3. If the description is already specific but still does not trigger, check if other Skills’ descriptions cover a broader range, leading to competition. The usual solution is to make the description narrower but more precise rather than broader but vaguer.

Trap 2: Skill Loaded but AI Does Not Follow Instructions

Diagnosis: The Skill is triggered, but Claude’s output does not align with the steps described in SKILL.md. Claude ignores certain steps or reorganizes the process based on its understanding.

Root Cause: Instructions are either too vague, too lengthy, or key instructions are buried deep in the file.

Claude’s attention has priorities. Content at the beginning of the file carries more weight than content buried at the end. If key instructions are in the middle of the document, Claude may “forget” them in a long context—this is not an AI flaw but a characteristic of the attention mechanism.

Repair Plan:

• Prioritize key instructions. Use titles like ## Critical or ## Important to clearly mark the most essential steps. Anthropic’s documentation even suggests, “If necessary, repeat key points.”
• Avoid lengthiness. If SKILL.md exceeds 5000 words, Claude’s execution quality typically declines. Move detailed documentation to the references/ directory, keeping the body concise with core process descriptions.
• Use scripts instead of natural language for critical validations. When validation logic is complex, write a scripts/validate.py and call it in SKILL.md with “Run python scripts/validate.py –input {filename} to check data format.” Code provides a deterministic execution path, not relying on the model’s probabilistic interpretation.
• Incorporate positive reinforcement. Anthropic’s documentation suggests a counterintuitive yet effective technique: explicitly encourage Claude to slow down and prioritize quality over speed. “Take your time to do this thoroughly. Quality is more important than speed."—this phrase is more effective when written in the user’s prompt than in SKILL.md.

Trap 3: Skill Itself Is Small, but the Entire System Slows Down

Diagnosis: The performance of a single Skill is normal, but enabling more than 10 Skills noticeably slows down response times, increases delays, and decreases output quality.

Root Cause: Multiple Skills loaded simultaneously lead to context bloat, or the SKILL.md design did not utilize the hierarchical structure of progressive disclosure.

Repair Plan:

• Reduce the number of Skills enabled simultaneously. Anthropic recommends evaluating whether more than 20-50 Skills are enabled at the same time. If so, consider using a “Skill Pack” strategy—pack related capabilities into fewer, larger Skills rather than maintaining dozens of fine-grained Skills.
• Fully utilize the three-tier loading structure. Frontmatter should only contain trigger metadata (<1024 characters), the SKILL.md body should hold core instructions (<5000 words), and references/ should contain deep documentation (referenced on demand in SKILL.md).
• Also use progressive disclosure within SKILL.md. In the SKILL.md body, do not lay out all details at once. Use “## Overview” to introduce the overall process and delve into “### Detailed Steps” only when necessary. This structure itself is also a form of progressive disclosure—Claude can first gain a high-level understanding and then load details on demand.

Deep Logic of Skill Design

By intersecting the longitudinal evolution context and the horizontal platform comparison, we can see some insights that cannot be observed from any single dimension.

Insight 1: The Essence of Skills is “Capability Compilation”

In the prompt era, the transmission of AI capabilities relied on “text”—humans wrote a paragraph, and AI read a paragraph. Text naturally has ambiguity; the same sentence can have different interpretations in different contexts, leading to instability in prompts.

Skills are not text; they are structured knowledge modules. YAML frontmatter provides metadata, the SKILL.md body provides execution instructions, references provide deep support, and scripts offer deterministic execution paths. This structure compiles human tacit knowledge—knowing when to do what and how to do it—into explicit instructions that AI can parse, execute, and verify.

The metaphor of “compilation” is worth pondering. A compiler processes source code and outputs machine instructions. A good compiler optimizes—removing redundant code, rearranging execution order, and caching intermediate results. A good Skill designer does the same—removing redundant instruction descriptions, optimizing the precision of trigger conditions, and compiling vague “do your best” into precise “execute action Y under condition X.”

From this perspective, the quality of Skill writing is fundamentally not about “writing well” but about the quality of compilation.

Insight 2: Skills Anchor the Evolution of the AI Agent Paradigm

From a broader perspective, the AI Agent field is undergoing a core transformation: from “model output” to “system execution.”

Early Agents (like AutoGPT, April 2023) attempted to let models autonomously decide the entire action plan, at the cost of extreme unreliability—models would get stuck in loops, call the wrong tools, and fail to recover from errors.

LangChain’s ReAct model (Reasoning + Acting) advanced this by clearly alternating between “thinking” and “acting” for the model, allowing it to review results after each tool call to decide the next step. This increased reliability but also introduced new problems: the model had to “think about what to do” in each loop, which itself wastes tokens and time.

Skills provide a different solution: structuring the knowledge of “what to do.” The model does not need to reason from scratch each time it executes; Skills have already encoded the path. The model only needs to process the current specific input within the framework of the Skill.

This means Skills are not “controlling” AI but “offloading” the burden of repetitive reasoning, allowing the model to focus its attention on areas that truly require judgment.

Insight 3: The Maturity of Skills Determines the Upper Limit of Agent Capabilities

The core contradiction currently faced by AI Agents is: the models are getting smarter, but their ability to reliably execute complex tasks remains limited. The reason lies not in the models themselves but in the engineering problem of “how to organize capabilities” that has not been well resolved.