Anthropic’s “Skills” Feature and the Rise of Persistent Context in AI
- Synergy Team
- 21 minutes ago
- 3 min read
A Quiet Release With Big Implications
Amid the steady stream of AI announcements, one recent update from Anthropic went mostly unnoticed, but could prove to be one of the most impactful of the year.
Anthropic’s new Skills feature, part of the Claude AI ecosystem, allows users to create Markdown files containing reusable instructions, tone preferences, and domain expertise. In essence, it gives Claude a form of persistent, portable context, which is a quiet step toward AI that remembers how you work.
But while the feature itself belongs to Claude, the idea behind it isn’t exclusive. The same approach can be applied across today’s leading AI chat platforms, from ChatGPT to Gemini, by treating structured Markdown files as reusable context that informs every interaction.
What “Skills” Really Are
At their core, Skills are super-prompts written as Markdown (.md) files, each one defining a specific task or area of expertise the AI can draw from.
A typical Skill file might include:
A clear task definition
Tone and formatting rules
Step-by-step workflows
Dos and don’ts
Reference examples or templates
Once uploaded, Claude automatically applies the contents of that file, effectively onboarding your AI for a role. And because the concept is platform-agnostic, any AI that supports file uploads or custom instructions can do the same.
Why Persistent Context Matters
Anyone who works with AI knows the friction of re-explaining context in every new chat. Persistent context changes that dynamic. It transforms AI from a one-time assistant into a repeat collaborator: one that already understands your tone, process, and expectations.
For teams using AI in writing, research, data, or reporting, reusable context files deliver measurable gains in speed, consistency, and reliability. Rather than fine-tuning models or crafting complex prompts, teams can simply load text-based frameworks that replicate those same advantages.
How to Recreate “Skills” in Any AI Chat
You don’t need Claude Pro to put this idea into practice. Any AI that supports file uploads or context references can replicate the effect.
Step 1: Create a Skill File
Start with a simple Markdown document that outlines how you want the AI to behave.
Example
File name: Data_Cleanup_Skill.md Goal: Standardize and clean CSV data for reporting. Rules:
Tone: Professional and concise |
Upload it to ChatGPT, Claude, or Gemini, and the model immediately applies the defined parameters.
Step 2: Combine Multiple Skills
For complex workflows, layer multiple Skill files together—like building blocks.
Example
Preparing a client report might involve:
Data_Cleanup_Skill.md
Chart_Formatting_Skill.md
Executive_Summary_Tone.md
Together, they define how the AI formats, analyzes, and summarizes without needing to re-prompt from scratch.
Step 3: Iterate and Reuse
As your workflow evolves, your Skill library will too. Over time, these files become a reusable framework for maintaining consistency across projects and teams.
Practical Use Cases
Research and Analysis
Define structure and citation expectations to ensure every AI-generated summary meets your data standards.
Content and Copywriting
Embed brand voice, tone, and formatting preferences so drafts remain consistent across writers and platforms.
Data and Reporting
Standardize KPI definitions, dashboard layouts, and summary formats.
Project Management
Predefine templates for retrospectives, meeting notes, or documentation, so structure never varies.
Customer Communication
Ensure professional, compliant tone in every client message with reusable Skills that define language, escalation paths, and disclaimers.
A Bridge Between Prompting and Fine-Tuning
Skills fill a critical gap between casual prompting and full fine-tuning. They allow teams to shape AI behavior with precision without developer resources or retraining.
This approach shifts the focus from model capability to context control—a far more accessible way to achieve reliable, consistent AI performance.
Looking Ahead
As organizations integrate AI more deeply into operations, context standardization will become central to responsible AI use.
Imagine a company repository of shared Skills, each version-controlled, documented, and approved for use across departments. That’s a blueprint for lightweight AI governance: defining how AI behaves, ensuring consistency, and maintaining accountability without limiting creativity.
The Takeaway
Anthropic’s Skills feature might have debuted quietly, but the underlying concept—persistent, portable context—marks a real turning point in AI adoption.
You don’t need to wait for proprietary integration to start. With a few well-written Markdown files, you can define how your AI collaborates, communicates, and learns, making every interaction faster, more predictable, and aligned with your team’s best practices.
At Synergy, we see this evolution as more than a convenience. It’s the beginning of AI systems that truly understand your business workflows—because context isn’t just what you provide. It’s what the AI remembers.