Building Modular LLM Prompts in Obsidian Copilot with Reusable Skills

[WetHat]

Motivation: Anthropic’s Introduction of Skills to Claude LLMs

October 2025 Anthropic introduced the concept of skills, called Claude Skills on a large scale for their Claude line of large language models. The motivation was to enable modular, composable, and maintainable prompt engineering. By allowing prompt designers to define and reference discrete "skills" (self-contained instructions or capabilities) Anthropic made it easier to build complex workflows without rewriting or duplicating prompt logic. This approach aligns with best practices in software engineering and knowledge management (DRY: Don't Repeat Yourself). See also: 🔗Claude Skills—From TOY to TOOL: Grab My Tutorial + Custom Skills To Help You Build Skills Fast

⚠️ When and Why?

• When: October 2025 for Pro, Max, Team, and Enterprise releases.
• Why: Support libraries of reusable prompt components to improve efficiency, maintainability, and collaboration in LLM-workflows

Why Obsidian Copilot Is Perfect for Modular Prompting with Skills

Obsidian Copilot’s architecture is uniquely suited for leveraging reusable skills in prompt design to eliminate boilerplate directives being pasted across prompts. Here is why:

• Context Priming in Commands:
  You can prime the LLM’s context by explicitly referencing skills within a command. For example:

  ## Context
  Skill: [[MagicSkill]]

  This ensures the LLM is aware of the specific skill to apply, making prompts modular and easier to maintain.
• Context Priming in Projects via "File Context":
  Obsidian Copilot Projects can include relevant skills by referencing them in "File Context" (see also 🔗 The AI Environment for Thinkers and Writers - Our 3 milestones).

⚠️ Best Practice
Organize your skills as individual notes or templates in Obsidian. Use internal links ([[SkillName]]) to include them in prompts or add them to project contexts.

By jerry-rigging Anthropic’s skill-based approach into Obsidian Copilot’s context management, you can create highly modular, maintainable, and powerful LLM workflows.

Monolithic Prompts vs. Skill-Based Prompts

Approach	Description	Pros	Cons
Monolithic	A single, large prompt containing all instructions for a specific task	Simple for one-off tasks; quick setup	Hard to maintain; not reusable
Skill-Based	Prompts built from smaller, reusable skill components	Modular; easy to update and combine	Requires initial setup and planning

When to Use Each Approach

• Monolithic Prompts:
  • Use for unique, one-time tasks where reusability is not a concern.
  • Suitable for simple workflows with minimal variation.
• Skill-Based Prompts:
  • Ideal for recurring tasks or workflows that share common elements.
  • Essential when you want to maintain consistency and reduce duplication across prompts.
  • Enables rapid iteration and easier debugging.

How to Build and Use Reusable Skills in Obsidian Copilot

1. Identify Common Tasks:
   List out the actions you frequently ask the LLM to perform (e.g., summarizing, extracting, formatting).
2. Define Each Skill Clearly:
   Write concise, standalone instructions for each skill.

   ⚠️ Keep skills focused; They should do one thing well.

Example:

Here's "MakeHeadline". A reusable skill that is invoked in a prompt to extract headlines from input text.

There a many ways to invoke skills.
Here I'm using pipeline and function notation. This allows skills to be pipelined (chained) like so:

"Lorem ipsum dolor sit amet, consectetur adipiscing elit sed" |> Skill1 |> Skill2

Skill: MakeHeadline

## Task
Generate a single, objective, one-sentence headline from a block of Markdown text.

## Signature
- In: Markdown.
- Invocation: 
	- Inline: `{{input}} |> MakeHeadline`
	- Stored: `Set foo: {{input}} |> MakeHeadline`
- Out:  Single line of plain text (string).

## Constraints
- Output must be a single sentence, ≤ 30 tokens, plain text, no period at end
- Use sentence case: capitalize first word and proper nouns only
- Headline must be objective, factual, and free of speculation, bias, or sensationalism
- Only include information explicitly present in the input
- Match formality, style, and register of the input without amplifying bias or subjectivity

## Edge Cases
- Input is empty, whitespace, or lacks a clear main idea ➔ "_Unable to generate a headline due to insufficient content_"

## Output
Single line of plain text containing the generated headline, formatted per the constraints above.

## Example 
Output: "Machine learning breakthroughs drive major progress in natural language processing capabilities"

Obsidian Command Using this Skill **

---
copilot-command-context-menu-enabled: true
copilot-command-slash-enabled: true
copilot-command-context-menu-order: 160
copilot-command-model-key: gpt-4.1|openai
copilot-command-last-used: 1761641874012
---
## System Role
You are a Senior Content Analyst for an academic or professional audience.

## Context
Skill: [[MakeHeadline]]

## Output
- Present without any additional commentary or formatting: `headline: "{activeNote} |> MakeHeadline"`

Applying the Prompt to this Post

The simple prompt shown above, when applied to this post, returns:

headline: "Modular prompt engineering in Obsidian Copilot using reusable Skills for efficient and maintainable LLM workflows"

Ready for frontmatter inclusion.

---

⚠ Start small: Identify one or two common skills in your workflow and refactor your prompts to use them. Over time, you'll build a powerful library for advanced prompt enginnering in Obsidian Copilot.

[logancyang]

Thanks for the detailed suggestion! I was looking into this as well. Will get back to you.