Claude Skill Dependency Injection Patterns

Dependency injection isn’t just a software engineering concept. When you structure Claude skills to compose and chain together, you unlock modular AI workflows that scale. This guide shows you how to design skills that delegate to other skills, share common logic, and build composable automation pipelines. For foundational skill composition patterns, see Claude skill inheritance and composition patterns.

What Dependency Injection Means for Claude Skills

In traditional software, dependency injection means passing dependencies into a function rather than having the function create them For Claude skills, the equivalent is designing one skill to invoke another skill as part of its workflow. Instead of building monolithic skills that handle everything, you create focused skills that delegate to specialized skills.

Consider a workflow where you need to extract data from a PDF, transform it into a spreadsheet, and then run analysis. Without dependency injection patterns, you might create one massive skill that tries to handle all three steps. With dependency injection, you compose three focused skills: one using the pdf skill for extraction, one using xlsx for spreadsheet operations, and a third skill that orchestrates the pipeline.

The Delegation Pattern

The simplest dependency injection pattern is delegation. A skill invokes another skill to handle a specific subtask, then continues processing the results.

# Delegation Example: invoice-processor.md

You process incoming invoices and extract line items.

When given an invoice PDF:
1. First invoke /pdf extract text and tables from {filename}
2. Parse the extracted content to identify: vendor name, invoice number, date, line items (description, quantity, unit price, total)
3. Format the extracted data as structured JSON
4. Return the structured result with confidence scores for each field

This skill delegates the PDF parsing to the pdf skill, then handles the business logic of mapping raw text to invoice fields. The pdf skill doesn’t need to know about invoices; it just extracts content. The invoice-processor skill doesn’t need to understand PDF internals; it just receives extracted text.

The Chaining Pattern

Chaining connects multiple skills in sequence, where each skill’s output becomes the next skill’s input. This pattern excels at multi-step data pipelines.

# Chaining Example: contract-review-pipeline.md

You run contracts through a review pipeline.

Pipeline steps:
1. /pdf extract all text from {contract_file}
2. /supermemory Are there similar contracts in the knowledge base?
3. Compare extracted clauses against known risky patterns
4. Generate a risk assessment summary

Output format:
- Risk level: LOW | MEDIUM | HIGH
- Key concerns: [list]
- Recommended actions: [list]

The chain flows naturally: extract → retrieve context → analyze → summarize. Each skill handles one domain. The orchestrating skill holds the workflow logic but delegates actual work to specialized skills.

The Configuration Pattern

Dependency injection becomes powerful when you pass configuration between skills. Instead of hardcoding behavior, skills accept parameters that control how dependent skills behave.

# Configuration Example: data-extractor.md

You extract structured data from documents based on user-specified schema.

Parameters:
- source_file: path to the document
- schema: JSON object defining fields to extract
- confidence_threshold: minimum extraction confidence (default: 0.8)

Workflow:
1. /pdf extract all tables and key-value pairs from {source_file}
2. Map extracted content to the fields defined in {schema}
3. Filter out extractions with confidence below {confidence_threshold}
4. Return structured data matching the schema

Users invoke this with specific schemas:

/data-extractor source_file=report.pdf schema={"fields": ["revenue", "expenses", "profit"]}

The skill configures the pdf skill indirectly by specifying what to extract, rather than telling the pdf skill exactly how to behave.

Shared Service Skills

A more advanced pattern involves creating shared service skills that other skills depend on. These are utility skills designed specifically to be invoked by other skills, not directly by users.

# Shared Service Example: json-formatter.md

You format and validate JSON data.

When invoked by another skill:
- Accept raw JSON or JSON strings as input
- Pretty-print with 2-space indentation
- Validate syntax and report any errors
- Optionally compact minified JSON

Always return valid JSON output or clear error messages.

Now other skills can delegate JSON handling:

/tdd generate tests for auth.py, then /json-formatter format the test output
/supermemory store {result}, then /json-formatter validate the stored JSON

Practical Workflow Examples

Automated Reporting Pipeline

Combine pdf, xlsx, and docx skills in a reporting workflow:

1. pdf extracts data from source documents
2. xlsx creates formatted spreadsheets with charts
3. docx generates the final report document
4. A master skill orchestrates the entire pipeline

# report-generator.md

Generate monthly reports from raw data.

Steps:
1. /pdf extract tables from monthly-data.pdf
2. /xlsx create report.xlsx with: raw data sheet, summary sheet, charts
3. /docx create monthly-report.docx embedding report.xlsx and adding analysis
4. Return paths to all generated files

Test Coverage Analysis

Chain tdd with analysis skills:

1. tdd generates initial tests
2. A coverage analysis skill measures test completeness
3. supermemory retrieves similar test patterns from past projects

Frontend Documentation Generator

Use frontend-design with document generation:

1. frontend-design analyzes a component and describes its structure
2. pdf generates a styled component documentation PDF
3. docx creates editable specification documents

Best Practices

Keep dependency injection patterns clean by following these guidelines:

Single responsibility per skill. Each skill should do one thing well. If a skill feels like it’s doing too much, break it into smaller skills and delegate.

Document expected inputs and outputs. When your skill invokes another skill, clearly specify what format you expect in and what format you’ll receive. This makes debugging much easier.

Handle failures gracefully. If a delegated skill fails, your skill should provide useful error messages rather than propagating raw exceptions.

Test the pipeline incrementally. Verify each skill works independently before chaining them together. Debugging a chain is harder than debugging individual skills.

Conclusion

Claude skills become significantly more powerful when you compose them rather than building isolated super-skills. The dependency injection patterns shown here—delegation, chaining, configuration, and shared services—give you a framework for building modular, maintainable AI workflows.

Start by identifying repetitive tasks in your Claude workflows. Extract the common operations into focused skills, then build orchestrating skills that compose them. Skills like pdf, xlsx, tdd, supermemory, and frontend-design become building blocks you can recombine for new use cases without rewriting logic.

Related Reading

• Claude Skill Inheritance and Composition Patterns — Learn skill inheritance as the foundation for dependency injection and composition.
• When to Split One Claude Skill Into Multiple Files — Structure skills for dependency injection by splitting them into focused modules.
• What Is the Best File Structure for a Complex Claude Skill — Design file structures that enable clean dependency injection between skill components.
• Claude Skills Advanced Hub — Explore advanced skill architecture patterns including composition and dependency injection.
• Claude Code FastAPI Dependency Injection Patterns Guide — If you are building Python APIs and want to use FastAPI’s built-in Depends() system, see this companion guide for framework-level DI patterns.

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