Borrowing it
Nothing to install: this file belongs to nerdai/llm-agents-from-scratch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nerdai/llm-agents-from-scratch/main/CLAUDE.mdgit clone --depth 1 https://github.com/nerdai/llm-agents-from-scratchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/nerdai/llm-agents-from-scratch/claude-md)<a href="https://agentmods.dev/instructions/nerdai/llm-agents-from-scratch/claude-md"><img src="https://agentmods.dev/badge/instructions/nerdai/llm-agents-from-scratch/claude-md.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00921 | $0.00921 |
| Opus 5 | $0.00461 | $0.00461 |
| Sonnet 5 | $0.00184 | $0.00184 |
| Haiku 4.5 | $0.00092 | $0.00092 |
Grade A, and why
llm-agents-from-scratch CLAUDE.md 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 7d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
llm-agents-from-scratch is a Python library for building LLM agents from the ground up, designed to accompany a technical book on creating intelligent agents with Model Context Protocol (MCP) integration. It provides abstract base classes and concrete implementations for LLMs, tools, and agents.
Commands
# Install dependencies
uv sync --all-extras --dev
# Run all tests
make test # or: pytest tests -v --capture=no
# Run a single test file
pytest tests/test_file.py -v --capture=no
# Run a specific test
pytest tests/test_file.py::test_function_name -v
# Lint and format (runs pre-commit hooks: ruff, mypy)
make lint
# Format only (ruff)
make format
# Coverage
make coverage # Generate XML report
make coverage-report # Terminal summary
make coverage-html # HTML report in htmlcov/
# Generate UML diagrams
make diagrams
Architecture
Core Abstractions
The library uses abstract base classes with type aliases for flexibility:
-
BaseLLM(base/llm.py) → Type aliasLLM- Abstract methods:
complete(),structured_output(),chat(),continue_chat_with_tool_results() - Implementation:
OllamaLLM(primary),OpenAILLM(optional extra)
- Abstract methods:
-
BaseTool/AsyncBaseTool(base/tool.py) → Type aliasTool = BaseTool | AsyncBaseTool- Required properties:
name,description,parameters_json_schema - Implementations:
SimpleFunctionTool/AsyncSimpleFunctionTool- Direct function wrapping with JSON schema generationPydanticFunctionTool/AsyncPydanticFunctionTool- Type-safe functions using Pydantic modelsMCPTool/MCPToolProvider- Model Context Protocol integration
- Required properties:
Agent Layer
LLMAgent(agent/llm_agent.py) - Main agent class managing LLM and tool registry- Contains nested
TaskHandlerclass extendingasyncio.Futurefor async task execution - Step-based execution with rollout tracking
- Contains nested
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.
- 7d ago First seen · 102 lines · 921 tokens per session scan A d3dd6fa0deed
llm-agents-from-scratch CLAUDE.md is an instructions file published in the GitHub repository nerdai/llm-agents-from-scratch (185 stars, last pushed yesterday), licensed Apache-2.0. It adds 921 tokens to every session, about $0.0046 per session on Opus 5. 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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