code-explain-patterns

A guide for explaining code, algorithms, and programming patterns with visuals and step-by-step learning. It also describes ways to estimate code difficulty and identify the concepts used.

In plain words
What is it for?
Creating visual algorithm explanations, breaking down code progressively, and identifying patterns or programming concepts in a code sample.
Why use it?
It helps turn unfamiliar or complex code into explanations suited to the reader's level.

Command for Claude Code

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/thapaliyabikendra/ai-artifacts/code-explain-patterns
Clone the repo
git clone --depth 1 https://github.com/thapaliyabikendra/ai-artifacts

Made for: Claude Code.

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,794 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01794
Opus 5 $0.00000 $0.00897
Sonnet 5 $0.00000 $0.00359
Haiku 4.5 $0.00000 $0.00179

Measured 2d ago against content hash ebc220dbdf10, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-explain-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/commands/references/code-explain-patterns.md · 289 lines

How it starts

The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Explanation Patterns Reference

Visual explanations, algorithm visualization, and progressive learning techniques.

Complexity Assessment

class CodeAnalyzer:
    def analyze_complexity(self, code: str) -> Dict:
        """Analyze code complexity and structure"""
        analysis = {
            'complexity_score': 0,
            'concepts': [],
            'patterns': [],
            'difficulty_level': 'beginner'
        }
        tree = ast.parse(code)
        analysis['metrics'] = {
            'lines_of_code': len(code.splitlines()),
            'cyclomatic_complexity': self._calculate_cyclomatic_complexity(tree),
            'nesting_depth': self._calculate_max_nesting(tree),
            'function_count': len([n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)])
        }
        analysis['concepts'] = self._identify_concepts(tree)
        return analysis

    def _identify_concepts(self, tree) -> List[str]:
        """Identify programming concepts used"""
        concepts = []
        for node in ast.walk(tree):
            if isinstance(node, (ast.AsyncFunctionDef, ast.AsyncWith)):
                concepts.append('asynchronous programming')
            elif isinstance(node, ast.FunctionDef) and node.decorator_list:
                concepts.append('decorators')
            elif isinstance(node, ast.With):
                concepts.append('context managers')
            elif isinstance(node, ast.Yield):
                concepts.append('generators')
            elif isinstance(node, (ast.ListComp, ast.DictComp)):
                concepts.append('comprehensions')
        return list(set(concepts))

Visual Diagrams

Flow Diagram Generation (Mermaid)

def generate_flow_diagram(self, code_structure):
    """Generate Mermaid flowchart"""
    diagram = "```mermaid\nflowchart TD\n"
    for i, func in enumerate(code_structure['functions']):
        node_id = f"F{i}"
        diagram += f"    {node_id}[{func['name']}]\n"
        if func.get('parameters'):
            diagram += f"    {node_id}_params[/{', '.join(func['parameters'])}/]\n"
            diagram += f"    {node_id}_params --> {node_id}\n"
    diagram += "```"
    return diagram

Read the full file on GitHub · 289 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 289 lines · 0 tokens per session scan A ebc220dbdf10

Subscribe to this mod's changes

code-explain-patterns is a command published in the GitHub repository thapaliyabikendra/ai-artifacts (24 stars, last pushed 5mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,794 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.