A Long Read: After Making Some Viral Skills, Here’s What I Think About Skills

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In my recent discussions about Skills, I’ve formed an increasingly clear judgment:

Everyone is talking about Agent right now, but most people still don’t truly understand what Agent means.

In the public’s mind, Agent is often just a chat box.

You type a sentence, it replies with text; you type another, it continues.

From this perspective, AI seems to naturally bring about democratization: people who couldn’t code before can now code, those who couldn’t make PPT can now make PPT, those who couldn’t edit videos can now edit videos.

As long as the model is powerful enough, capability gaps between people will be leveled.

But I increasingly believe this judgment is wrong. Agent doesn’t simply level capability gaps — it amplifies them.

Top-tier users already understand the composition of Agent by default:

Documents, rules, memory, loop, MCP, CLI, tool calling, permissions, security sandbox, context engineering, scheduled tasks, heartbeat, file system, code execution, and Skill.

But ordinary users only know that “Agent can write code” or “Agent can call Skills” — they don’t know where Agent’s upper limit comes from, nor how they should organize goals, materials, and workflows to make Agent actually work.

Agent: Here it refers not just to a chatbot, but to an AI system that can understand goals, plan steps, call tools, and continuously execute tasks.

Memory: External memory used by Agent to store long-term preferences, project status, and historical decisions — not equivalent to model training memory.

Loop: The execution loop where Agent repeatedly “thinks, calls tools, observes results, then decides the next step.”

Here emerges a significant cognitive divide: top-tier users are already building systems, while ordinary users are still asking questions in a chat box.

People with clear goals, good context, strong taste and judgment will be amplified by Agent;

People with chaotic goals, no documentation, and poor judgment will also have their chaos amplified by Agent.

So users will experience a K-shaped divergence. Last year, we could still lower the barrier through product design, interaction design, and user education. This year, I think it’s very difficult to bridge this gap with simple UX alone.

Skill is the key to bridging the capability gap in using Agent.

Skill is a Commodity of Capability, Not Just a Prompt

My current one-sentence definition of Skill is:

Skill is a distributable, reusable, and iterable Agent capability unit that encapsulates expert experience, workflows, taste, and tool calls.

Skill: A reusable capability unit that packages prompts, workflows, tool calls, templates, scripts, boundaries, and experience.

It is not a simple prompt, nor is it an App in the traditional sense.

It is more like a “capability commodity” in the Agent era. Users don’t need to understand the underlying MCP, CLI, workflow, memory, loop, model selection, code execution, or context engineering — they only need to know:

What problem does it solve? What output does it produce? How to use it? How well have others used it?

Prompts themselves are hard to be a product. They are easily copied, difficult to distribute, lack version management, and have no semantics for installation or invocation.

Skill packages prompts, rules, examples, tool calls, file structures, scripts, dependencies, and usage instructions into an installable, invocable, iterable, and shareable capability bundle.

So Skill and Prompt are not fundamentally different, but Skill has higher invocation efficiency, lower distribution and understanding costs, and can carry more engineering content.

More importantly, many tasks cannot be solved by a single prompt.

They are a stable workflow: read materials, analyze requirements, select templates, call tools, generate output, verify results, fix issues, export files.

Skill extracts this workflow from one-off conversations into a repeatedly callable process.

For example, the PPT Skill workflow is not simply “generate PPT”.

It involves reading articles or outlines, asking about topic, page count, and images, selecting themes, colors, and layouts, generating HTML PPT, automatically post-checking common issues, then fixing missing attributes, misalignment, overflow, image cropping, repetitive pacing, etc. If necessary, it calls image models to generate illustrations, and finally outputs a presentable, shareable file.

What is truly valuable behind this is that Skill externalizes human presentation experience.

The Core of Skill is Externalizing Human Experience

The design Skills I’ve made illustrate this clearly.

The truly valuable part is solidifying human aesthetic judgment, layout decisions, design system experience, template selection, image cropping rules, light/dark overlay rules, font and color rules into the Skill.

This requires the creator to understand three things simultaneously: traditional domain expertise, AI’s capabilities and limitations, and product thinking.

Traditional domain expertise determines whether you know what a good result looks like. For design, editing, writing, fitness, law, commercial advertising — each industry has a large amount of tacit judgment. AI’s capabilities and limitations determine whether you know what the model can do, what it does unstably, and what must be engineered as a fallback.

Product thinking determines whether you understand user scenarios, usage thresholds, feedback paths, and stability requirements.

This is also my deepest insight from making several Skills.

The PPT Skill wasn’t originally designed “to make a Skill” — it was because I really needed to give a talk.

After the first version was basically formed, I adjusted spacing, font sizes, fonts, colors, images, repetitive content, WebGL backgrounds, etc. through five or six rounds of dialogue.

After the talk, I found that people cared most not about the talk itself, but about how the PPT was made. So I then solidified that template and workflow into a Skill.

The Social Media Card Skill was not abstracted out of thin air either. It came from very specific content distribution needs:

3:4 vertical graphic cards, suitable for Xiaohongshu, WeChat Official Accounts, Twitter, and other platforms. It needed to handle 11 content categories, two visual systems, 28 layout skeletons, real images + code-based layout, while also avoiding issues like AI image throttling, unsharp text, and platform style mismatches.

The Logo Generator Skill follows the same logic. It didn’t directly let the image model generate the logo in one shot, because the text, structure, and editability of image models are unstable.

It chose to generate SVG logo variants first, then generate display images and WebGL backgrounds, splitting the logo itself, display scene, and interactive background into different layers, each processed with the most suitable technology.

The AI Desk Card further illustrates that Skill’s boundaries can extend to the physical environment.

It allows Agent to take over the physical information display on the edge of the screen: firmware flashing, Wi-Fi configuration, information push, scheduled tasks, memory, todo, calendar, GitHub display, e-ink screen refresh — all can be encapsulated into one Skill.

These cases together illustrate: The core of Skill is “what experience does a person turn into a callable capability.”

Users Don’t Care About Concepts; They Care About Results

For ordinary users, what Skill, MCP, CLI, or Plugin are called doesn’t matter.

What they care about is: What problem does this feature solve? What scenarios is it suitable for? Can I use it with one click? What input materials do I need? What does the output look like? How well have others used it?

MCP: Model Context Protocol — a protocol that allows AI to connect to external tools, data sources, and services in a unified way.

CLI: Command Line Interface — for Agent, it’s often a more stable and easier-to-automate operation entry than graphical interfaces.

Therefore, the user-facing product layer should not pile up jargon. Codex calling many things “plugins” is, I think, the right direction: downplay concepts, emphasize functionality.

Under the hood, it can be Skill, MCP, CLI, or native Plugin; the user only needs to know what it can do.

But for product makers and creators, the differences between these underlying forms are important.

Skill is suitable for carrying relatively vertical, describable, and reusable work — such as PPT, social media cards, article illustrations, writing polish, video packaging, resume optimization, data visualization, or industry SOPs.

MCP is more suitable for atomic services and context connections in an Agent architecture — such as maps, browsers, cloud storage, design files, databases, enterprise APIs.

CLI is currently a very practical universal Plugin form: commands, code, and Skill can all be encapsulated into it, and it is not tied to a single Agent platform.

Feishu CLI is a good example. Users don’t need to understand over 200 commands or know which API is behind them.

They just say, “Help me pull today’s meeting notes into my notes.” Behind the scenes, Agent can search cloud documents, read transcripts, download word-by-word recordings, write to local Markdown, and create backlinks.

Users see the result; Agent uses the tools; Skill encapsulates the workflow.

This is also why the relationship between Skill, CLI, and MCP cannot be understood purely from technical concepts.

They all ultimately boil down to one question: how to let ordinary users use the capabilities that top-tier users have already validated.

Architecture of a Good Skill: Short Center, Thick Radiation

Many people understand Skill as simply a SKILL.md file — that’s only half true.

SKILL.md: The entry description file for many Skills, used to tell Agent when to load this capability, what workflow to follow, and what pitfalls to avoid.

A good Skill is often a directory. SKILL.md is just the entry point; alongside it there can be scripts/, references/, assets/, templates, schemas, config files, sub-Skills, and special cases.

Complex Skills should not be afraid of having complex content; they should be afraid of stuffing complex content into the model at once. The file system itself is a form of context engineering.

Context window: The range of information an AI can “see” and process at one time — documents, code, chat history, and tool descriptions all consume it.

The information architecture of a good Skill should be “short center, thick radiation.”

SKILL.md only contains high-signal workflow and judgment; references/ hold heavy documentation and domain materials, loaded conditionally; scripts/ hold deterministic logic, called by Agent rather than rewritten; assets/ hold templates, schemas, examples, fonts, themes, and layout skeletons; config files or stable data directories hold first-time configuration, preferences, and history.

A key point here: Skill’s description is not a marketing slogan or a feature summary — it is a routing trigger.

A good description should describe when the user needs this Skill, preferably derived from real user expressions; a bad description merely explains “what this Skill does.”

For example, a PPT Skill should not say “This Skill can generate beautiful PPT.”

It should say: “Load when the user needs to convert an article, outline, or speech content into a presentable HTML PPT.” The first is advertising; the second is Agent’s judgment condition.

This explains why “putting all capabilities into one giant Agent” is not a good direction.

A big, all-in-one harness will stuff tool definitions, protocol details, and long documents into the context, leading to higher latency, higher token costs, and more misuse.

Conversely, a thin harness only provides a minimal runtime environment, with Skills as on-demand capability packages — this allows the system to compound over time.

Harness: The outer program that runs Agent, responsible for model loop, file read/write, context management, and security boundaries.

A more stable architecture is Thin Harness, Fat Skills: the harness stays thin, responsible for running the model loop, reading/writing files, managing context, executing permissions, and security boundaries;

Skill becomes thick, carrying workflow, judgment, domain knowledge, templates, scripts, assets, gotchas, and evals;

Deterministic tools are delegated to CLI, scripts, or APIs; the model stays in the parts it’s better suited for — understanding, judging, synthesizing, making trade-offs, and expressing.

Thin Harness, Fat Skills: Keep the Agent runtime environment light, and put specific workflows, domain knowledge, templates, scripts, and failure experiences into on-demand Skills.

Skill Quality Should Be Maintained Like Code Quality

A good Skill is not written once. It needs maintenance, and maintenance should be treated like code quality.

A relatively reliable lifecycle:

• First, run real tasks with an Agent without the Skill, find where it makes mistakes;

• Write evals based on real queries, including positive examples, negative examples, and forbidden loads;

• First tune the description to ensure it loads when it should, and doesn’t load when it shouldn’t;

• When writing the main body, delete anything obvious — only keep judgments that change model behavior;

• Append failure cases to gotchas instead of continuously lengthening the main workflow; when changing description or routing boundaries, supplement evals;

• Then test across models, to see the differences in Skill triggering and execution across different orchestration models.

Eval: Using a set of real or simulated tasks to test whether Skill triggers, executes, and delivers results as expected.

Gotchas: A “don’t do this” list summarized from real failures — often more effective than positive instructions at improving Skill stability.

An important principle: Every Skill is a tax.

Once it enters the index, every session and every user pays the context cost for its name and description;

Once it is loaded, every subsequent conversation pays the cost for its main content.

So every sentence must ask: Without this sentence, would Agent make a mistake? If not, delete it.

Gotchas are the highest-value content because they come from real failures.

Positive principles are often already known by the model; negative boundaries are where expert experience lies.

In Design Skills, “don’t use pure white or pure black,” “three consecutive pages with the same rhythm is a P0 error,” “text must not overlap faces,” “AI images are only used when no suitable real image exists” — all belong to gotchas or strong constraints.

This also explains why fully automatic Skill generation can only produce a first draft.

Models can help you draft the structure, but they cannot magically possess your failure samples, aesthetic judgments, industry boundaries, and user feedback.

What is truly valuable is human experience injected into it, then continuously thickened through evals and gotchas.

The Essence of Designing Skills: Turning Taste into Constraints

Design Skills are not simply “AI can draw pictures.”

They need to solve problems like model instability, image throttling, unsharp text, uncontrollable layout, and difficulty judging style consistency.

I increasingly feel that the core of design Skills is turning professional taste into constraints that models can execute.

Models tend to converge to some mediocre patterns by default:

Tailwind-style large color blocks, purple gradients, emoji stacking, Inter font, glow effects, excessive rounded corners, meaningless animations, uncontrolled information density. This is not because the model lacks aesthetic material, but because it lacks stable trade-off principles.

So the most valuable part of a design Skill is subjective but clear constraints:

• Don’t use pure white or pure black to reduce harshness and cheapness;

• Don’t let users arbitrarily input hex colors — only provide validated theme palettes;

• Don’t use purple multi-color gradients, glow effects, or large-area blur as visual shortcuts;

• Use animation only when necessary, and only animate transform and opacity;

• Prefer real photography and screenshot beautification for graphic cards; AI-generated images are only a fallback;

• Layout skeletons are manually validated; AI is responsible for filling, combining, and fine-tuning;

• Text must adapt its position, color, overlay, and line breaks based on the image subject, brightness, and readable area.

These rules seem to restrict freedom, but they actually protect the output floor.

The quality of design Skills comes from “eliminating the vast majority of ugly options for the user.”

This is also an experience I’ve repeatedly mentioned in my Skill articles:

Good design is not mysterious — it’s decomposable, encodable, and checkable industry common sense.

The value of Skill is compressing this common sense into SKILL.md, templates, checklists, theme variables, and post-validation checks.

A common method for both PPT Skill and Social Media Card Skill is to downgrade AI’s task from “free design” to “filling within high-quality skeletons.”

In PPT Skill, 10 page layouts, 5 theme color sets, three-level font hierarchy, 7:5 / 6:6 / 8:4 grids, and alternating hero/non-hero pacing form a stable presentation system. AI doesn’t need to invent layouts from scratch; it only needs to select the appropriate page type based on content and fill it in.

Social Media Card Skill further calibrates the scenario to mobile news feeds:

3:4 is the main battlefield — 1 second to decide whether to stop. It’s not just cropping PPT to a vertical image; it redefines graphic categories, layout ratios, line-breaking rules, and material priorities.

11 content categories, two visual systems, 28 layout skeletons, screenshot beautification, map components, real image libraries, and restrained AI image generation together form a “content platform visual Skill.”

The Logo Generator Skill follows the same logic:

It doesn’t directly let the image model generate the logo in one shot, because the text, structure, and editability of image models are unstable;

It generates SVG variants first, then creates display images and WebGL backgrounds. Here, the logo itself, display scene, and interactive background are split into different layers, each processed with the most suitable technology.

So the general formula for design Skills is:

Humanly precipitated aesthetic system, models handle content and semantics, code handles stable layout and export, image models only handle the visual parts suitable for them.

This is slower than simply “let AI draw a picture,” but it’s controllable, modifiable, reusable, and more suitable for long-term use by content creators.

Skill Ecosystem Cannot Be Just a Repository List

If a Skill can be backlinked through images, cases, reviews, usage data, author, and application scenarios, it becomes more than a tool — it becomes a community node.

Backlink: A link from usage cases, articles, images, or project pages back to a Skill, allowing people to see who uses it, how they use it, and what results they achieve.

Current problems with many Skill showcases:

Long lists that look like GitHub repository names; identical icons; no result display; no evaluation metrics;

Multimodal Skills are only displayed with text; users don’t know which one suits them.

Recommending 10 or 20 curated Skills with clear explanations of how to use them is far better than giving users a thousand-item list.

Every Skill should look like a software feature page. The page should explain:

What problem does it solve? What scenarios is it suitable for? What input is needed? What does the output look like? What are typical prompts? Show generated result screenshots or videos. Who has used it and what do they think? What are common failures? How to install and modify.

This essentially requires strong curation.

It’s not about listing names — it’s about picking each one, writing introductions, showing results, and preferably having video explanations.

GitHub is a natural hosting platform for code-based Skills, because Skills often contain code and need version management;

GitHub has ecosystem, copyright statements, and distribution foundations; AI is also familiar with Git and GitHub operations; open source also covers all Agent platforms without being tied to a single product.

But Xiaohongshu is suitable for visual content and use case distribution.

Xiaohongshu’s strengths are content awareness, visual display, user aesthetics, and comment systems.

PPT Skill and Social Media Card Skill have already spread beyond the AI circle — for example, café owners, digital product reviewers, event planners, restaurants, and sharing scenarios in third-tier cities. This shows that Skills can cross out of the AI community.

App-store-style Skill distribution also has potential: more precise recommendations, lower usage barriers, and possibly creator revenue sharing.

But for creators, if you only list on one platform, you are betting that platform will do a great job with product, ecosystem, distribution, and market capture.

A more stable strategy might be: GitHub for basic distribution and cross-platform coverage; platform Skillhub / app store for experience optimization, curated recommendations, and commercial conversion.

The future Skill platform will essentially be an App Store, GitHub, community seeding page, review system, and Agent tool layer all at once.

Where Ordinary Users Are Really Stuck

People outside the AI circle are not unable to use Skills.

In actual observations, café owners, digital product reviewers, event planners, fitness coaches, etc., can all achieve good results.

The real sticking point is the interaction mindset.

Many people still think in traditional software terms, believing that a one-time generation should be complete:

They are not used to continuous adjustments via chat; they don’t know they can ask AI to change colors, change fonts, fix overflow, or swap images; they don’t know how to provide context and materials; they don’t know how to extract Skills from their own workflows.

Therefore, Skill products not only need to provide installation but also usage education.

Industry Skills will be a very important direction. Many industries have excellent experience and customer insights:

Fitness, law, food & beverage, event planning, education, commercial advertising, etc. But industry experts may not know how to make Skills, and they worry about sharing and being copied.

The key here is not to treat Skill as an add-on service.

Fitness coaches can use Agent to manage member diet, training, cardio, reminders, and feedback, increasing customer retention and service efficiency.

Legal professionals can turn tedious text processing, clause review, and format checking into auxiliary Skills, while core judgment remains human.

Food & beverage and event industries can use graphic Skills to package real images and stories into shareable content.

AI cannot replace offline service delivery, but it can improve customer acquisition, communication, maintenance, and reusability.

These industry users only need basic onboarding: take them through a requirements analysis, implement it as a Skill, and they will understand its boundaries.

Every industry has pioneer users: creative, curious, wanting to gain a competitive advantage with AI. Serve these people first.

Content Skills: Articles, Products, and Cases Feed Each Other

Looking at my existing articles, I’m forming a very clear content Skill roadmap:

Not writing articles for some abstract AI concept — first make a usable Skill, then write about the production process, design judgments, and use cases as distribution content.

This type of content has several characteristics.

The PPT Skill originally came from a sharing session on “AI and Organization.” The audience asked most about how the PPT was made, so it evolved from a one-time deliverable into an open-source Skill. This is a by-product turning into a main product.

The article itself is like a manual, but not a README.

It needs to explain why it’s designed this way, who it’s for, where the boundaries are, and what real results look like — lowering the user’s understanding threshold.

The product demo itself is content assets. PPT screenshots, graphic cards, logo display images, Desk Card scene images — all can become distribution material.

Skills, in turn, improve writing efficiency. The Social Card Skill can directly convert article paragraphs into visual cards suitable for Xiaohongshu, WeChat Official Account, or Twitter.

Every article expands the semantic boundary of Skills.

PPT is for presentation, Social Card is for content distribution, Logo is for project brand assets, Desk Card is for hardware and environment UI, and Night Patrol Log points to game demo workflows.

This shows that Skill is not just a “tool product” but also an expression infrastructure for content creators.

In the past, articles and products were separate: first make the product, then write promotion. Now, Skills, articles, cases, open-source repos, and social feedback feed each other.

A mature path might be: Use Agent to complete a real task, solidify the process into a Skill, use the visual results from the Skill to write an article, the article brings users and feedback, feedback is turned into gotchas, templates, and the next version of the Skill, and the new Skill generates the next round of content.

This is the compounding flywheel for a personal product in the Agent era.

The Boundaries of Skill Will Continue to Expand

In the past, “plugin” usually meant a button within software. Now, the boundaries of Skill can be significantly larger.

Browser Skill will be a consumer entry point. Products like Tabbit Browser show that Skills can enter the browser scenario, becoming the entry point for ordinary users to interact with web pages, materials, scripts, and automation.

The browser is the most familiar working environment for the masses. If Skills appear as “ready-made scripts / use cases / one-click execution,” they will be much easier to understand than bare CLI or GitHub repos.

Hardware Skill shows that AI can take over environment UI.

The value of AI Desk Card lies in extending Agent’s capabilities into the physical environment:

Installing firmware, configuring Wi-Fi, writing cron jobs, reading Memory, selecting widgets, refreshing e-ink screens — the entire process is guided by AI. Users no longer face an App settings page; AI itself is the settings page.

Game Skill represents even longer chain creative workflows.

The “indie game demo Skill” mentioned in the Night Patrol Log development notes covers gameplay themes, prototypes, asset collection, green screen keying, contact sheets, video generation, music, Electron packaging, GitHub Actions, and Release.

It encapsulates a production pipeline that spans programming, art, animation, composition, and operations. Its value is reducing the wall between “making a prototype” and “independently delivering a complete work.”

These cases together illustrate:

The future of Skill is not limited to the chat box. It will expand into the browser, desktop, local files, hardware, content platforms, game engines, and real work environments.

Skill vs. Gene: The Boundary Between Handwritten Experience and Automatic Evolution

There is another contrast worth preserving but using with caution: Agent Skill vs. GEP Gene.

Skill is more like a pre-settled capability package created by humans: with a clear creator, clear boundaries, clear workflow, and version.

Concepts like Gene / Capsule emphasize automatically growing capabilities from successful experiences during execution: with success rates, mutation history, applicable contexts, and auto-fix mechanisms.

Gene / Capsule: Here refers to reusable experience units precipitated from repeated successful execution paths — emphasizing automatic evolution rather than manual writing.

These are not simple substitutes; they are different layers.

Skill is suitable for carrying human expert experience, aesthetics, industry SOPs, tool invariants, and clear delivery standards;

Gene is suitable for capturing successful paths from repeated execution, turning temporary trial-and-error into reusable experience; Capsule is like combining multiple Genes into longer workflows.

From current product reality, Skill is still a more practical unit because it can be written, reviewed, published, explained, and distributed.

But in the long run, automatic precipitation of Skill/Gene-like experience will become a direction: Agent first tries with general tools, and after success, writes the path back into Skill or generates new sub-capabilities.

This also addresses the discussion of “automatically precipitating Skills.” Systems can automatically discover repeated workflows, but whether to precipitate, how to name, where the boundaries are, and which failures to write into gotchas — still require human judgment.

The truly ideal form is not fully automatic or fully manual — it’s humans defining taste and boundaries, while Agent is responsible for collecting evidence, proposing changes, supplementing evals, and maintaining long-tail experience.

Theft Cannot Be Prevented by Hiding; The Defense is Continuous Distribution

Skills are hard to protect from theft by being closed-source. Even if you don’t open-source, as long as people see the output results and try it a few times, they may replicate it.

So the defense is not “hiding it away” but:

Open-source to cover more platforms; use influence to deter excessive plagiarists; run self-media so users know who the original source is; establish a lead through continuous iteration; use community cases and evaluation systems to build brand assets.

In an era where product barriers are lowering, if a personal product lacks channels, resources, and marketing, the creator must do promotion themselves. In the past, self-media was optional; now it’s infrastructure.

What Platforms Should Really Do

If you want to build a Skill platform, you cannot just bet on Skills. The reason users download a standalone client is first that the basic Agent experience is good enough:

A beautiful, usable client; multi-model support, especially domestic models; file, project, memory, CLI, MCP, and Skill management;

Permissions and security sandbox; long-running tasks and state continuation; multi-device flow — mobile controlling desktop, desktop controlling mobile; high-quality official plugins that work out of the box.

The insight from Workbody is that it didn’t do anything particularly unique — it just did the basic experience that should be there. Many domestic products haven’t even achieved that.

Some high-frequency, necessary, and common capabilities should be built-in and polished — don’t let users figure out installation themselves.

Strong official plugins create a barrier. Multi-device, cloud and local mutual control also create a barrier.

When Skills are strongly tied to the local environment, mobile needs to remotely control PC.

Skills can be cross-platform, but those relying on local files, scripts, browsers, and CLI are hard to run directly on mobile.

Mobile is suitable for lightweight creation from 0 to 1; desktop is suitable for heavy tasks and local environment calls.

Automatic precipitation of Skills is a long-term direction, but good Skills still need humans. Products like Dumate propose “auto-precipitation of Skills”: automatically summarizing workflows from users’ repetitive work.

This direction is valid, but good Skills still need business SOPs, taste, testing, and iteration. Auto-generation can produce the first draft; true stable delivery requires polishing.

A Complete Skill Lifecycle

If we consolidate the preceding judgments into one path, a complete Skill lifecycle looks roughly like this:

First, discover a real need — start from your own or industry users’ repetitive work.

Then, produce a high-quality output — don’t abstract first; first use Agent to solve a real task.

Then, abstract the workflow — identify reusable steps, inputs, outputs, constraints, and tools.

Then, engineer templates — solidify aesthetics, layout, calling, validation, and fix mechanisms.

Then, test across models — good models test the upper limit, weak models ensure the lower bound.

Only then, package and release — host on GitHub with README, examples, and installation instructions.

Then, content distribution — use Xiaohongshu, Twitter, WeChat Official Account, videos to showcase results.

Then, collect feedback — from issues, comments, user cases, and platform data, find real problems.

Filter feedback — only absorb parts that improve generalization and stability.

This path looks long, but its essence is simple:

Every real task is not just about completing the task — it’s about accumulating a capability asset that can be called next time.

The most scarce resource in the Agent era is a reusable way to organize capabilities.

Skill is important because it is the first time that human experience, workflows, and taste have the opportunity to become a commodity that can be distributed, invoked, evaluated, and iterated.

This may be the real big opportunity in the Agent ecosystem.

Alright, that’s it for today. If you found this helpful, please feel free to give it a like or share it with someone who needs it.