Composable Skills: Building Modular AI Systems

The most powerful AI systems are not monolithic giants—they are elegantly composed of smaller, focused components that work together seamlessly. Just as Unix philosophy advocates for programs that do one thing well and combine through pipes, composable AI skills create value through thoughtful combination.

In this guide, we will explore the principles and patterns of composable skill design. You will learn how to build skills that are modular by design, easy to combine, and greater than the sum of their parts when working together.

The Case for Composability

Before diving into patterns, let us understand why composability matters for AI skills.

The Monolith Problem

Consider a traditional approach to building a code review skill. A monolithic design might:

# Monolithic Code Reviewer

You are a comprehensive code reviewer. When given code:
1. Check for syntax errors
2. Analyze security vulnerabilities
3. Evaluate performance implications
4. Assess code style
5. Suggest improvements
6. Generate a summary report
7. Create GitHub comments
8. Update tracking systems

This monolithic skill works, but it has problems:

Hard to maintain: Changes to one part affect everything
Difficult to test: You cannot test security analysis in isolation
Limited reuse: Cannot use the security check for other purposes
All or nothing: Users cannot run just the parts they need
Scaling issues: Cannot optimize different parts independently

The Composable Solution

A composable approach breaks this into focused skills:

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Syntax    │     │  Security   │     │ Performance │
│   Checker   │     │  Analyzer   │     │  Profiler   │
└─────────────┘     └─────────────┘     └─────────────┘
       │                   │                   │
       └───────────────────┼───────────────────┘
                           │
                    ┌──────┴──────┐
                    │   Review    │
                    │ Aggregator  │
                    └──────┬──────┘
                           │
                    ┌──────┴──────┐
                    │   Report    │
                    │  Generator  │
                    └─────────────┘

Each component can be:

Developed independently
Tested in isolation
Reused in other contexts
Replaced without affecting others
Scaled based on need

Principles of Composable Design

Effective composability follows several key principles.

Single Responsibility

Each skill should have one clear purpose. This is the foundation of composability.

# Good: Single Responsibility

---
name: extract-functions
description: Extracts function signatures from source code
---

# Extract Functions

You are a code parser that extracts function signatures.

Given source code, output a JSON array of functions:
- name: function name
- parameters: array of parameter names and types
- returnType: return type if specified
- lineNumber: starting line

Do not analyze the functions. Do not suggest improvements.
Just extract and structure the data.

# Bad: Multiple Responsibilities

---
name: function-everything
description: Extracts, analyzes, and refactors functions
---

# Function Everything

Extract functions, analyze their complexity,
suggest refactoring, and update documentation...

Well-Defined Interfaces

Composable skills need clear input/output contracts that other skills can rely on.

## Interface Contract

### Input
```typescript
interface FunctionExtractorInput {
  code: string;           // Source code to parse
  language: string;       // Programming language
  includePrivate: boolean; // Include private functions
}

Output

interface FunctionExtractorOutput {
  functions: Array<{
    name: string;
    parameters: Parameter[];
    returnType: string | null;
    lineNumber: number;
    visibility: 'public' | 'private' | 'protected';
  }>;
  metadata: {
    totalFunctions: number;
    parseTime: number;
    language: string;
  };
}

This interface is guaranteed and will not change without version update.


### Statelessness

Composable skills should be stateless whenever possible. State makes composition harder.

```markdown
# Stateless Design

This skill:
- Takes all required information as input
- Produces complete output without side effects
- Does not remember previous invocations
- Does not depend on external state

If context is needed, it must be passed in:
```json
{
  "currentFile": "path/to/file.ts",
  "projectContext": { ... },
  "previousAnalysis": { ... }
}


### Idempotency

Running a skill multiple times with the same input should produce the same output.

```markdown
## Idempotency Guarantee

Given identical inputs, this skill will always produce identical outputs.

This enables:
- Safe retries on failure
- Caching of results
- Parallel execution
- Predictable testing

Composition Patterns

Let us explore specific patterns for combining skills effectively.

Pipeline Pattern

Skills connected in sequence, each transforming the output of the previous.

Input → [Skill A] → [Skill B] → [Skill C] → Output

---
name: code-quality-pipeline
description: Runs code through quality checks in sequence
---

# Code Quality Pipeline

Execute this pipeline in sequence:

## Stage 1: Parse
Use `extract-ast` skill to parse code into AST

## Stage 2: Analyze
Use `complexity-analyzer` skill on the AST

## Stage 3: Check Style
Use `style-checker` skill on original code with AST context

## Stage 4: Generate Report
Use `report-generator` with all previous outputs

Pass outputs forward:
- Stage 2 receives Stage 1 output
- Stage 3 receives both Stage 1 and 2 outputs
- Stage 4 receives all previous outputs

Implementation considerations:

Each stage must complete before the next begins
Errors in early stages prevent later stages
Include error handling between stages
Consider checkpointing for long pipelines

Fan-Out/Fan-In Pattern

Distribute work to multiple skills, then aggregate results.

              ┌─→ [Skill A] ─┐
              │              │
Input ──→ Fan-Out ─→ [Skill B] ─→ Fan-In ──→ Output
              │              │
              └─→ [Skill C] ─┘

---
name: multi-language-analyzer
description: Analyzes code across multiple languages in parallel
---

# Multi-Language Analyzer

## Fan-Out Phase

For each file in the input:
1. Detect the language
2. Route to appropriate analyzer:
   - Python files → `python-analyzer`
   - JavaScript files → `javascript-analyzer`
   - TypeScript files → `typescript-analyzer`
   - Go files → `go-analyzer`

Run analyzers in parallel when possible.

## Fan-In Phase

Collect all analyzer outputs and:
1. Normalize to common format
2. Aggregate metrics
3. Identify cross-language issues
4. Generate unified report

Output includes per-language and aggregate results.

Decorator Pattern

Wrap a skill to add capabilities without modifying it.

---
name: cached-analyzer
description: Adds caching to any analyzer skill
---

# Cached Analyzer

Wraps another analyzer with caching capability.

## Before Calling Wrapped Skill

1. Generate cache key from input hash
2. Check if result exists in cache
3. If cached and not expired, return cached result
4. Otherwise, proceed to wrapped skill

## After Calling Wrapped Skill

1. Receive result from wrapped skill
2. Store in cache with TTL
3. Return result to caller

## Configuration

```json
{
  "wrappedSkill": "complexity-analyzer",
  "cacheTTL": 3600,
  "cacheInvalidateOn": ["file_modified", "manual"]
}


### Fallback Pattern

Try multiple skills in order until one succeeds.

```markdown
---
name: resilient-parser
description: Attempts multiple parsing strategies
---

# Resilient Parser

Try parsing strategies in order until success:

## Strategy 1: Native Parser
Use language-specific parser for best accuracy.
If fails, proceed to Strategy 2.

## Strategy 2: Tree-sitter Parser
Use generic tree-sitter grammar.
If fails, proceed to Strategy 3.

## Strategy 3: Regex Fallback
Use pattern matching for basic extraction.
If fails, proceed to Strategy 4.

## Strategy 4: LLM Parse
Ask LLM to extract structure from code.
This should almost always succeed.

Return result from first successful strategy.
Include metadata about which strategy was used.

Filter Pattern

Remove or transform elements before passing to next skill.

---
name: code-filter
description: Filters code elements before analysis
---

# Code Filter

Apply filters before passing to downstream skills:

## Available Filters

1. **Size Filter**: Remove files over N lines
2. **Type Filter**: Include only specified file types
3. **Pattern Filter**: Exclude files matching patterns
4. **Content Filter**: Exclude generated/minified code
5. **Recency Filter**: Only include recently modified

## Usage

```json
{
  "input": [...files],
  "filters": [
    { "type": "size", "maxLines": 1000 },
    { "type": "pattern", "exclude": ["*.min.js", "*.generated.*"] },
    { "type": "content", "excludeGenerated": true }
  ],
  "downstream": "complexity-analyzer"
}

Output filtered set to downstream skill.


## Building Composable Skills

Now let us look at practical techniques for building skills that compose well.

### Standard Data Formats

Use consistent data formats across skills to enable easy composition.

```markdown
## Standard Formats

### Code Location
```json
{
  "file": "path/to/file.ts",
  "line": 42,
  "column": 10,
  "endLine": 45,
  "endColumn": 3
}

Code Issue

{
  "id": "unique-issue-id",
  "severity": "error" | "warning" | "info",
  "category": "security" | "performance" | "style" | "bug",
  "message": "Human readable message",
  "location": { ...CodeLocation },
  "suggestion": "How to fix",
  "references": ["url1", "url2"]
}

Analysis Result

{
  "success": boolean,
  "issues": [...CodeIssue],
  "metrics": { ...arbitrary metrics },
  "metadata": {
    "analyzer": "skill-name",
    "version": "1.0.0",
    "duration": 1234,
    "timestamp": "ISO-8601"
  }
}


### Transformation Functions

Build skills that transform between formats to enable composition.

```markdown
---
name: eslint-to-standard
description: Transforms ESLint output to standard issue format
---

# ESLint to Standard Transformer

Transform ESLint JSON output to standard issue format.

## Input Format (ESLint)
```json
{
  "filePath": "/path/to/file.js",
  "messages": [{
    "ruleId": "no-unused-vars",
    "severity": 2,
    "message": "...",
    "line": 10,
    "column": 5
  }]
}

Output Format (Standard)

{
  "issues": [{
    "id": "eslint-no-unused-vars-file.js-10-5",
    "severity": "error",
    "category": "style",
    "message": "...",
    "location": {
      "file": "/path/to/file.js",
      "line": 10,
      "column": 5
    }
  }]
}

Mapping Rules

severity 2 → "error"
severity 1 → "warning"
Category inferred from rule prefix


### Metadata Propagation

Ensure metadata flows through skill chains for traceability.

```markdown
## Metadata Handling

Each skill must:

1. **Accept metadata**: Read incoming metadata object
2. **Preserve chain**: Keep previous skill metadata
3. **Add own metadata**: Append this skill's metadata
4. **Pass forward**: Include full chain in output

Example chain:
```json
{
  "result": { ... },
  "metadata": {
    "chain": [
      { "skill": "file-reader", "version": "1.0.0", "duration": 50 },
      { "skill": "parser", "version": "2.1.0", "duration": 120 },
      { "skill": "analyzer", "version": "1.5.0", "duration": 340 }
    ],
    "totalDuration": 510,
    "initiatedAt": "2025-01-15T10:30:00Z"
  }
}


### Error Propagation

Handle errors in ways that preserve composability.

```markdown
## Error Handling for Composition

When an error occurs:

1. **Wrap, don't throw**: Return structured error
2. **Include context**: Where in the chain did it fail
3. **Preserve partial**: Return any successful results
4. **Enable recovery**: Provide enough info to retry

Error format:
```json
{
  "success": false,
  "error": {
    "skill": "parser",
    "type": "ParseError",
    "message": "Unexpected token at line 42",
    "recoverable": true,
    "partialResult": { ... }
  },
  "metadata": { ... chain so far ... }
}

Downstream skills should:

Check success flag
Handle gracefully if possible
Propagate if not


## Advanced Composition Techniques

For complex systems, these advanced techniques enable sophisticated composition.

### Dynamic Composition

Select and combine skills at runtime based on input.

```markdown
---
name: dynamic-analyzer
description: Selects appropriate analyzers based on input
---

# Dynamic Analyzer

Analyze input to determine optimal skill combination.

## Analysis Phase

Examine input for:
- Languages present
- Project structure
- Configuration files
- Existing analysis results

## Skill Selection

Based on analysis, select from:

| Condition | Skills to Use |
|-----------|---------------|
| Has package.json | npm-audit, dependency-check |
| Has Dockerfile | container-security, layer-analyzer |
| Has CI config | ci-integration, pipeline-validator |
| Large codebase | sampled-analysis, parallel-dispatch |

## Composition

Compose selected skills using appropriate pattern:
- Independent skills → parallel fan-out
- Dependent skills → sequential pipeline
- Similar skills → best-of with voting

Return unified result with skill selection rationale.

Recursive Composition

Skills that can invoke themselves on subproblems.

---
name: recursive-summarizer
description: Summarizes content of any length through recursion
---

# Recursive Summarizer

Summarize content regardless of length.

## Base Case
If content is under 1000 tokens:
- Summarize directly
- Return summary

## Recursive Case
If content exceeds 1000 tokens:
1. Split into chunks of ~800 tokens
2. Summarize each chunk (recursive call)
3. Combine chunk summaries
4. If combined summaries exceed 1000 tokens, recurse
5. Return final summary

## Termination Guarantee
- Maximum recursion depth: 5
- Chunk size reduces each level
- Always converges to base case

Track recursion depth in metadata.

Conditional Composition

Compose different skills based on conditions.

---
name: conditional-processor
description: Different processing based on content type
---

# Conditional Processor

Route to different processing chains based on input.

## Detection Phase

Analyze input to determine type:
- Code: Has syntax, programming patterns
- Prose: Natural language, paragraphs
- Data: JSON, CSV, structured data
- Mixed: Combination of above

## Processing Chains

### Code Path

code-parser → linter → type-checker → summary


### Prose Path

grammar-check → style-analyzer → readability → summary


### Data Path

schema-validator → data-profiler → anomaly-detector → summary


### Mixed Path

splitter → [route each part] → aggregator → summary


Output includes which path was taken and why.

Testing Composable Skills

Composable skills need specific testing approaches.

Unit Testing Individual Skills

Test each skill in isolation with mocked inputs.

## Unit Test Suite

### Test: extract-functions

#### Happy Path
Input: Valid TypeScript file with 3 functions
Expected: Array of 3 function objects with correct metadata

#### Edge Cases
- Empty file: Should return empty array
- No functions: Should return empty array
- Arrow functions: Should extract correctly
- Nested functions: Should handle appropriately

#### Error Cases
- Invalid syntax: Should return partial results with error
- Binary file: Should reject with clear error

Integration Testing Compositions

Test skill combinations work correctly together.

## Integration Test Suite

### Test: analysis-pipeline

Setup: Create temp directory with test files

#### Pipeline Test
1. Run full pipeline on test files
2. Verify each stage produces expected output
3. Confirm final result includes all stage data
4. Check metadata chain is complete

#### Failure Handling
1. Inject failure at stage 2
2. Verify error propagates correctly
3. Confirm partial results from stage 1 preserved
4. Check recovery suggestions present

Contract Testing

Verify skills honor their interface contracts.

## Contract Tests

For each skill, verify:

### Input Contract
- Accepts all documented input types
- Rejects invalid inputs with clear errors
- Handles optional fields correctly

### Output Contract
- Output matches documented schema
- All required fields present
- Types are correct
- Metadata is complete

### Behavioral Contract
- Idempotent (same input = same output)
- Stateless (no side effects)
- Respects resource limits

Real-World Composition Example

Let us build a complete composable system for code quality.

---
name: code-quality-system
description: Composable code quality system
---

# Code Quality System

A fully composable code quality analysis system.

## Component Skills

### 1. file-collector
Collects files matching patterns
Output: Array of file paths with metadata

### 2. language-detector
Detects programming language of files
Output: Map of file path to language

### 3. syntax-validator
Validates syntax for detected language
Output: Syntax errors or "valid" per file

### 4. complexity-measurer
Measures cyclomatic complexity
Output: Complexity scores per function

### 5. security-scanner
Scans for security issues
Output: Security findings with severity

### 6. style-checker
Checks code style
Output: Style violations

### 7. report-generator
Generates unified report
Output: Formatted report

## Composition Configuration

```yaml
system: code-quality
version: 1.0.0

pipeline:
  - stage: collect
    skill: file-collector
    config:
      patterns: ["**/*.{js,ts,py}"]
      exclude: ["**/node_modules/**"]

  - stage: detect
    skill: language-detector
    input: collect.output

  - stage: validate
    skill: syntax-validator
    input:
      files: collect.output
      languages: detect.output
    parallel: true

  - stage: analyze
    skills:
      - complexity-measurer
      - security-scanner
      - style-checker
    input: validate.valid_files
    parallel: true
    fan_in: true

  - stage: report
    skill: report-generator
    input:
      all_stages: true
    output: final

Execution Engine

The system executes based on configuration:

Stages run in order unless parallel specified
Parallel stages run concurrently
Fan-in collects parallel results
Errors short-circuit or continue based on config
Final output includes full trace


## Conclusion

Composability transforms how we build AI systems. Instead of monolithic skills that try to do everything, we create focused components that excel at specific tasks and combine elegantly.

Key takeaways for building composable skills:

1. **Single Responsibility**: One skill, one job, done well
2. **Clear Interfaces**: Documented input/output contracts
3. **Standard Formats**: Consistent data structures across skills
4. **Statelessness**: No hidden dependencies or side effects
5. **Error Handling**: Graceful degradation with partial results

The patterns we explored—pipeline, fan-out/fan-in, decorator, fallback, and filter—provide building blocks for complex systems. Choose patterns based on your specific needs and combine them as needed.

As you build your skill library, think about composition from the start. A skill that works well alone but cannot combine with others provides limited value. A skill designed for composition becomes increasingly valuable as your library grows.

The future of AI development is modular, composable, and built on skills that work together. Start building that future today.

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Growing Your AI Developer Following

AI-Powered Release Automation

Why I Started Blogging About AI Skills

Composable Skills: Building Modular AI Systems

The Case for Composability

The Monolith Problem

The Composable Solution

Principles of Composable Design

Single Responsibility

Well-Defined Interfaces

Output

Composition Patterns

Pipeline Pattern

Fan-Out/Fan-In Pattern

Decorator Pattern

Filter Pattern

Code Issue

Analysis Result

Output Format (Standard)

Mapping Rules

Recursive Composition

Conditional Composition

Testing Composable Skills

Unit Testing Individual Skills

Integration Testing Compositions

Contract Testing

Real-World Composition Example

Execution Engine

Multi-Agent Patterns

Memory Systems Design

Grill Me (Code Review)