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Component Identification Sizing

OrganizationPopular
tech-leads-club
component-identification-sizing

Maps architectural components in a codebase and measures their size to identify what should be extracted first. Use when asking "how big is each module?", "what components do I have?", "which service is too large?", "analyze codebase structure", "size my monolith", or planning where to start decomposing. Do NOT use for runtime performance sizing or infrastructure capacity planning.

Overview

Publishertech-leads-club
Repositoryagent-skills
Skill namecomponent-identification-sizing
Stars
6.3K
Forks
530
Bundled files
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tech-leads-club on GitHub. Read the source before you install it.

Installation

Install the Component Identification Sizing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Component Identification Sizing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Component Identification Sizing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Component Identification Sizing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Component Identification and Sizing

This skill identifies architectural components (logical building blocks) in a codebase and calculates size metrics to assess decomposition feasibility and identify oversized components.

How to Use

Quick Start

Request analysis of your codebase:

  • "Identify and size all components in this codebase"
  • "Find oversized components that need splitting"
  • "Create a component inventory for decomposition planning"
  • "Analyze component size distribution"

Usage Examples

Example 1: Complete Analysis

User: "Identify and size all components in this codebase"

The skill will:
1. Map directory/namespace structures
2. Identify all components (leaf nodes)
3. Calculate size metrics (statements, files, percentages)
4. Generate component inventory table
5. Flag oversized/undersized components
6. Provide recommendations

Example 2: Find Oversized Components

User: "Which components are too large?"

The skill will:
1. Calculate mean and standard deviation
2. Identify components >2 std dev or >10% threshold
3. Analyze functional areas within large components
4. Suggest specific splits with estimated sizes

Example 3: Component Size Analysis

User: "Analyze component sizes and distribution"

The skill will:
1. Calculate all size metrics
2. Generate size distribution summary
3. Identify outliers
4. Provide statistics and recommendations

Step-by-Step Process

  1. Initial Analysis: Start with complete component inventory
  2. Identify Issues: Find components that need attention
  3. Get Recommendations: Request actionable split/consolidation suggestions
  4. Monitor Progress: Track component growth over time

When to Use

Apply this skill when:

  • Starting a monolithic decomposition effort
  • Assessing codebase structure and organization
  • Identifying components that are too large or too small
  • Creating component inventory for migration planning
  • Analyzing code distribution across components
  • Preparing for component-based decomposition patterns

Core Concepts

Component Definition

A component is an architectural building block that:

  • Has a well-defined role and responsibility
  • Is identified by a namespace, package structure, or directory path
  • Contains source code files (classes, functions, modules) grouped together
  • Performs specific business or infrastructure functionality

Key Rule: Components are identified by leaf nodes in directory/namespace structures. If a namespace is extended (e.g., services/billing extended to services/billing/payment), the parent becomes a subdomain, not a component.

Size Metrics

Statements (not lines of code):

  • Count executable statements terminated by semicolons or newlines
  • More accurate than lines of code for size comparison
  • Accounts for code complexity, not formatting

Component Size Indicators:

  • Percent of codebase: Component statements / Total statements
  • File count: Number of source files in component
  • Standard deviation: Distance from mean component size

Analysis Process

Phase 1: Identify Components

Scan the codebase directory structure:

  1. Map directory/namespace structure

    • For Node.js: services/, routes/, models/, utils/
    • For Java: Package structure (e.g., com.company.domain.service)
    • For Python: Module paths (e.g., app/billing/payment)
  2. Identify leaf nodes

    • Components are the deepest directories containing source files
    • Example: services/BillingService/ is a component
    • Example: services/BillingService/payment/ extends it, making BillingService a subdomain
  3. Create component inventory

    • List each component with its namespace/path
    • Note any parent namespaces (subdomains)

Phase 2: Calculate Size Metrics

For each component:

  1. Count statements

    • Parse source files in component directory
    • Count executable statements (not comments, blank lines, or declarations alone)
    • Sum across all files in component
  2. Count files

    • Total source files (.js, .ts, .java, .py, etc.)
    • Exclude test files, config files, documentation
  3. Calculate percentage

    component_percent = (component_statements / total_statements) * 100
  4. Calculate statistics

    • Mean component size: total_statements / number_of_components
    • Standard deviation: sqrt(sum((size - mean)^2) / (n - 1))
    • Component's deviation: (component_size - mean) / std_dev

Phase 3: Identify Size Issues

Oversized Components (candidates for splitting):

  • Exceeds 30% of total codebase (for small apps with <10 components)
  • Exceeds 10% of total codebase (for large apps with >20 components)
  • More than 2 standard deviations above mean
  • Contains multiple distinct functional areas

Undersized Components (candidates for consolidation):

  • Less than 1% of codebase (may be too granular)
  • Less than 1 standard deviation below mean
  • Contains only a few files with minimal functionality

Well-Sized Components:

  • Between 1-2 standard deviations from mean
  • Represents a single, cohesive functional area
  • Appropriate percentage for application size

Output Format

Component Inventory Table

markdown
## Component Inventory

| Component Name  | Namespace/Path               | Statements | Files | Percent | Status       |
| --------------- | ---------------------------- | ---------- | ----- | ------- | ------------ |
| Billing Payment | services/BillingService      | 4,312      | 23    | 5%      | ✅ OK        |
| Reporting       | services/ReportingService    | 27,765     | 162   | 33%     | ⚠️ Too Large |
| Notification    | services/NotificationService | 1,433      | 7     | 2%      | ✅ OK        |

Status Legend:

  • ✅ OK: Well-sized (within 1-2 std dev from mean)
  • ⚠️ Too Large: Exceeds size threshold or >2 std dev above mean
  • 🔍 Too Small: <1% of codebase or <1 std dev below mean

Size Analysis Summary

markdown
## Size Analysis Summary

**Total Components**: 18
**Total Statements**: 82,931
**Mean Component Size**: 4,607 statements
**Standard Deviation**: 5,234 statements

**Oversized Components** (>2 std dev or >10%):

- Reporting (33% - 27,765 statements) - Consider splitting into:
  - Ticket Reports
  - Expert Reports
  - Financial Reports

**Well-Sized Components** (within 1-2 std dev):

- Billing Payment (5%)
- Customer Profile (5%)
- Ticket Assignment (9%)

**Undersized Components** (<1 std dev):

- Login (2% - 1,865 statements) - Consider consolidating with Authentication

Component Size Distribution

markdown
## Component Size Distribution

Component Size Distribution (by percent of codebase)

[Visual representation or histogram if possible]

Largest: ████████████████████████████████████ 33% (Reporting) ████████ 9% (Ticket Assign) ██████ 8% (Ticket) ██████ 6% (Expert Profile) █████ 5% (Billing Payment) ████ 4% (Billing History) ...


### Recommendations

```markdown
## Recommendations

### High Priority: Split Large Components

**Reporting Component** (33% of codebase):
- **Current**: Single component with 27,765 statements
- **Issue**: Too large, contains multiple functional areas
- **Recommendation**: Split into:
  1. Reporting Shared (common utilities)
  2. Ticket Reports (ticket-related reports)
  3. Expert Reports (expert-related reports)
  4. Financial Reports (financial reports)
- **Expected Result**: Each component ~7-9% of codebase

### Medium Priority: Review Small Components

**Login Component** (2% of codebase):
- **Current**: 1,865 statements, 3 files
- **Consideration**: May be too granular if related to broader authentication
- **Recommendation**: Evaluate if should be consolidated with Authentication/User components

### Low Priority: Monitor Well-Sized Components

Most components are appropriately sized. Continue monitoring during decomposition.

Analysis Checklist

Component Identification:

  • Mapped all directory/namespace structures
  • Identified leaf nodes (components) vs parent nodes (subdomains)
  • Created complete component inventory
  • Documented namespace/path for each component

Size Calculation:

  • Counted statements (not lines) for each component
  • Counted source files (excluding tests/configs)
  • Calculated percentage of total codebase
  • Calculated mean and standard deviation

Size Assessment:

  • Identified oversized components (>threshold or >2 std dev)
  • Identified undersized components (<1% or <1 std dev)
  • Flagged components for splitting or consolidation
  • Documented size distribution

Recommendations:

  • Suggested splits for oversized components
  • Suggested consolidations for undersized components
  • Prioritized recommendations by impact
  • Created architecture stories for refactoring

Implementation Notes

For Node.js/Express Applications

Components typically found in:

  • services/ - Business logic components
  • routes/ - API endpoint components
  • models/ - Data model components
  • utils/ - Utility components
  • middleware/ - Middleware components

Example Component Identification:

services/
├── BillingService/          ← Component (leaf node)
│   ├── index.js
│   └── BillingService.js
├── CustomerService/          ← Component (leaf node)
│   └── CustomerService.js
└── NotificationService/      ← Component (leaf node)
    └── NotificationService.js

For Java Applications

Components identified by package structure:

  • com.company.domain.service - Service components
  • com.company.domain.model - Model components
  • com.company.domain.repository - Repository components

Example Component Identification:

com.company.billing.payment   ← Component (leaf package)
com.company.billing.history   ← Component (leaf package)
com.company.billing           ← Subdomain (parent of payment/history)

Statement Counting

JavaScript/TypeScript:

  • Count statements terminated by ; or newline
  • Include: assignments, function calls, returns, conditionals, loops
  • Exclude: comments, blank lines, declarations without assignment

Java:

  • Count statements terminated by ;
  • Include: method calls, assignments, returns, conditionals
  • Exclude: class/interface declarations, comments, blank lines

Python:

  • Count executable statements (not comments or blank lines)
  • Include: assignments, function calls, returns, conditionals
  • Exclude: docstrings, comments, blank lines

Fitness Functions

After identifying and sizing components, create automated checks:

Component Size Threshold

javascript
// Alert if any component exceeds 10% of codebase
function checkComponentSize(components, threshold = 0.1) {
  const totalStatements = components.reduce((sum, c) => sum + c.statements, 0)
  return components
    .filter((c) => c.statements / totalStatements > threshold)
    .map((c) => ({
      component: c.name,
      percent: ((c.statements / totalStatements) * 100).toFixed(1),
      issue: 'Exceeds size threshold',
    }))
}

Standard Deviation Check

javascript
// Alert if component is >2 standard deviations from mean
function checkStandardDeviation(components) {
  const sizes = components.map((c) => c.statements)
  const mean = sizes.reduce((a, b) => a + b, 0) / sizes.length
  const stdDev = Math.sqrt(sizes.reduce((sum, size) => sum + Math.pow(size - mean, 2), 0) / (sizes.length - 1))

  return components
    .filter((c) => Math.abs(c.statements - mean) > 2 * stdDev)
    .map((c) => ({
      component: c.name,
      deviation: ((c.statements - mean) / stdDev).toFixed(2),
      issue: 'More than 2 standard deviations from mean',
    }))
}

Best Practices

Do's ✅

  • Use statements, not lines of code
  • Identify components as leaf nodes only
  • Calculate both percentage and standard deviation
  • Consider application size when setting thresholds
  • Document namespace/path for each component
  • Create visual size distribution if possible

Don'ts ❌

  • Don't count test files in component size
  • Don't treat parent directories as components
  • Don't use fixed thresholds without considering app size
  • Don't ignore small components (may need consolidation)
  • Don't skip standard deviation calculation
  • Don't mix infrastructure and domain components in same analysis

Next Steps

After completing component identification and sizing:

  1. Apply Gather Common Domain Components Pattern - Identify duplicate functionality
  2. Apply Flatten Components Pattern - Remove orphaned classes from root namespaces
  3. Apply Determine Component Dependencies Pattern - Analyze coupling between components
  4. Create Component Domains - Group components into logical domains

Notes

  • Component size thresholds vary by application size
  • Small apps (<10 components): 30% threshold may be appropriate
  • Large apps (>20 components): 10% threshold is more appropriate
  • Standard deviation is more reliable than fixed percentages
  • Well-sized components are 1-2 standard deviations from mean
  • Oversized components often contain multiple functional areas that can be split

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Component Identification Sizing AI skill do?

Maps architectural components in a codebase and measures their size to identify what should be extracted first. Use when asking "how big is each module?", "what components do I have?", "which service is too large?", "analyze codebase structure", "size my monolith", or planning where to start decomposing. Do NOT use for runtime performance sizing or infrastructure capacity planning.

Why use Component Identification Sizing on TypingMind?

Because you install it once and use it with any model. Component Identification Sizing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Component Identification Sizing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(architecture)/component-identification-sizing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Component Identification Sizing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Component Identification Sizing?

As many as you like. As long as a model supports skills, you can use Component Identification Sizing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Component Identification Sizing AI skill free?

It is published on GitHub by tech-leads-club. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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