You may have had this experience: asking a general-purpose AI assistant to write an industry research report, only to get three pages of surface-level content that reads like assembled search results. Or asking it to review code -- it catches syntax issues but can't offer architectural insights.
The problem isn't that AI isn't smart enough -- it's that it lacks methodology. Human experts are effective not just because of their extensive knowledge, but because they've mastered systematic workflows and domain best practices. Tokeny's Skill system is designed to give AI these very methodologies.
From "General Assistant" to "Domain Expert"
Each Skill is essentially a standardized instruction file (SKILL.md) that defines a skill's name, applicable scenarios, and professional execution instructions. When a Skill is activated, the system injects its instructions into the AI's working context -- the AI will strictly follow the methodology, workflow, and best practices defined in the instructions, rather than improvising based on general knowledge.
Think of it this way: the AI is like a smart new employee without a professional manual. Without a Skill activated, it can only rely on general common sense, with inconsistent quality. With a Skill activated, it's as if you've handed it an operations manual for the relevant domain -- now it knows what workflow to follow, what to do at each step, and what standards the output should meet.
Take the "Deep Research" skill as an example: without the Skill, the AI might search once or twice and start writing a report with limited coverage. With the Deep Research Skill activated, the AI follows a standardized workflow: "develop a research plan -- conduct multiple rounds of searching (at least 5) -- cross-verify information sources -- write chapter by chapter according to the outline -- generate a structured report." The difference in methodology directly reflects in output quality.
SKILL.md: A Concise Skill Description Format
The Skill file structure is very straightforward, consisting of two parts: a YAML metadata header + a Markdown instruction body.
--- name: Deep Research trigger: /deep-research description: Conduct multidimensional systematic research on any topic tags: [research, report] --- # Execution Instructions You are a professional research analyst. Upon receiving a research topic, follow this workflow: ## Step 1: Develop a Research Plan Analyze the core dimensions of the topic, formulate 5-8 research directions... ## Step 2: Multiple Rounds of Web Search Conduct at least 2 rounds of search for each direction... ## Step 3: Write the Report Write chapter by chapter according to the outline, citing sources for each chapter...
The top section (between ---) contains YAML-formatted metadata: skill name, trigger command (starting with /), description, and tags. The bottom section contains Markdown-formatted execution instructions -- this is the core of the Skill, defining the specific workflow, execution standards, and output format that the AI should follow.
The design philosophy behind this format is: anyone can read, write, and modify it. You don't need to write code, you don't need to call APIs -- a single plain text file defines a complete AI skill.
Two Activation Methods: Manual Precision, Automatic Intelligence
Skills can be activated in two ways, suited for different scenarios:
Manual trigger -- type /skill-name (e.g., /deep-research) in the input box to directly activate the corresponding skill. Ideal when you know exactly which skill you want. After typing /, a skill list pops up for selection, and you can continue typing keywords to filter.
Automatic routing -- you don't need to remember any commands. The AI analyzes your conversation content and automatically determines whether to activate a skill. For example, if you say "Help me do a competitive analysis of the new energy vehicle industry," the AI recognizes this as a research task and automatically activates the Deep Research skill. If you say "Take a look at this code for issues," the AI automatically activates the Code Review skill.
The principle behind automatic routing: the system injects the names and descriptions of all available skills into the AI's context in a structured format. The AI matches them against conversational intent. When a high-confidence match is found, the AI explains which skill it plans to use and why -- you always know what's happening.
Automatic routing doesn't secretly activate skills. The AI explicitly tells you which skill it intends to use before starting -- if you think it's wrong, you can directly correct it.
Compatible with the Anthropic Agent Skills Open Standard
Tokeny's SKILL.md format is based on the Agent Skills open standard published by Anthropic (agentskills.io). This isn't an isolated technical decision -- it's a bet on the ecosystem.
Adopting an open standard means:
- Skills written for Claude Code, Cursor, and other tools can be used directly in Tokeny
- Skills you create in Tokeny can be shared with users of other platforms
- High-quality community-created Skills can be shared across tools, benefiting everyone through a larger ecosystem
- No lock-in to any single platform -- your knowledge assets (SKILL.md files) are portable
We firmly believe that the future of the AI skill ecosystem should be open, not siloed across platforms. Choosing an open standard is the best way to ensure our users have maximum choice in the skill ecosystem.
Create Your Own Skill
Tokeny's Skill system is completely open -- you can create custom skills for any domain. The process is very simple:
- 1In the app's "Skill Management" page, click "Custom Skills"
- 2Create a new SKILL.md file, defining metadata and execution instructions
- 3After saving, your custom skill immediately appears in the skill list and automatic routing
- 4Trigger your custom skill via the / command or natural language
The key to creating a Skill is writing effective execution instructions. Some practical tips: clearly define each step of the workflow, specify output format and quality standards, list the tools the AI should use (e.g., search, file operations), and provide concrete examples rather than abstract descriptions. A good Skill instruction should be detailed enough that "someone completely unfamiliar with it would know exactly what to do after reading it."
Built-in Skills and the Extension Marketplace
Tokeny comes with built-in skills covering multiple domains, ready to use out of the box:
Built-in Skills:
- Web Search -- search the internet for the latest information; serves as the foundation for other skills
- Deep Research -- multiple rounds of search + cross-verification + structured reports, ideal for systematic research
- Code Review -- analyze code quality across architecture, security, performance, and readability dimensions
- Visual Design -- generate Canvas-based visual design mockups, ideal for prototyping
- Frontend Design -- generate complete frontend page code (HTML/CSS/JS) with live preview
- Data Analysis -- data processing, statistical computation, and chart visualization
- Infographic Report -- generate visual infographic HTML reports, ideal for presentations
- Project Init -- scaffold project structures and directory layouts for quick project starts
Extension Marketplace (one-click install):
- Word Document Processing -- read and generate .docx files
- Excel Spreadsheet Processing -- data import/export, formula calculation and analysis
- PDF Tools -- PDF file reading, parsing, and content extraction
Skills in the extension marketplace come from community and third-party contributors. In the Skill Management page, you can browse, install, and manage these extension skills. Installation takes just one step, and the skill is automatically downloaded locally.
Full Transparency: All Source Code Is Auditable
Every Skill in Tokeny -- whether built-in or from the extension marketplace -- is a plain text SKILL.md file. You can view the full source code directly in the app's "Skill Management" page. No compiled binaries, no encrypted instructions, no hidden logic.
This fully transparent design is intentional: you should know exactly what the AI is asked to do under a specific skill, what workflow it follows, and what constraints it has. This also means you can modify or create your own version based on any existing Skill -- think of it as a starting point, not a black box.
Want to quickly create a custom Skill? The easiest approach: find a built-in Skill with similar functionality, view its source code, make a copy, and modify it to suit your needs.