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.
npx agentmods add commands/thapaliyabikendra/ai-artifacts/code-explain-patternsgit clone --depth 1 https://github.com/thapaliyabikendra/ai-artifactsWhat 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.
| Model | Per session | Once 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 |
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.
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
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.
- 2d ago First seen · 289 lines · 0 tokens per session scan A ebc220dbdf10
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.
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