Manthan Gupta Discussion: The Future of AI Coding Assistants Lies in the "Skills" Ecosystem

In the field of AI coding assistants, simple “conversational” capabilities are gradually becoming standard, while real competition is shifting towards “execution” and “extensibility”.

A recent tweet by renowned developer Manthan Gupta on X (Twitter) has attracted widespread attention in the community. He pointed out that the next major leap for AI Agents will no longer just be an increase in model parameters, but a modular ecosystem built around “Skills”.

From “General Chat” to “Specialized Skills”

As Manthan Gupta implied, current AI coding assistants (like GitHub Copilot, Claude Code, Cursor), while powerful, are often limited by preset capabilities. Every time a user starts a new task, they often need to repeatedly input a large amount of context and prompts.

The concept of “Skills” is designed to solve precisely this problem.

What are AI Skills?

In next-generation tools like Context7 and Claude Code, a “Skill” is defined as a reusable, distributable unit of capability. It typically includes:

1. System-Level Prompt: Defines the AI’s behavior pattern in a specific scenario (e.g., “Act as a senior React expert for code review”).
2. Tool Set (Tools): External scripts or APIs that AI can call (e.g., read PDF, search docs, execute SQL).
3. Context: Preset knowledge base or file references.

Interpreting Manthan Gupta’s View: The Power of Ecosystem

The core of Manthan Gupta’s argument is: Developers shouldn’t have to start from zero training or prompting AI every time.

Just as we use npm to install code libraries, we should be able to install “skills” for our AI assistants via ctx7 install or similar commands.

• Scenario A: You need to write a Next.js 15 project.

  • Traditional Way: You check docs, then copy docs to AI, telling it “Please use Next.js 15 new features…”.
  • Skills Way: You install the @context7/nextjs-15 skill. Your AI instantly masters all new features, best practices, and scaffolding commands.

• Scenario B: The team needs a unified code commit standard.

  • Traditional Way: Write it in the docs, verify manually.
  • Skills Way: The team shares a git-commit-skill. All members’ AI assistants will automatically follow Angular conventions and even link Jira IDs when generating commit messages.

Practice: How to Build Skills as Suggested by Manthan

Currently, Context7 (ctx7) is one of the pioneers realizing this vision.

1. Discover Skills

Search for community-contributed skills via CLI:

npx ctx7 skills search code-review

2. Inject Capabilities

Inject skills into your development environment (supports Claude Code, Cursor, etc.):

npx ctx7 skills install /anthropics/skills code-review

3. Create & Share

You can also package your best practices into a Skill and publish it to the registry for developers worldwide to use.

Conclusion

Manthan Gupta’s tweet is more than just a simple share; it points out the inevitable direction of AI development tool evolution through a unique perspective.

As tools like Moltbot (formerly Clawdbot), OpenCode, and Claude Code continue to mature, “how many parameters” will no longer be the sole standard for measuring AI assistants. Instead, “how many high-value Skills installed” will determine their productivity ceiling in actual engineering.

For developers, now is the best time to start building and accumulating your own exclusive “Skills Library”.

Reference Links:

• Manthan Gupta on X
• Context7 Skills Guide