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 skills add joonlab/joonlab-claudecode-setting-for-share --skill langchain-fundamentalsgit clone --depth 1 https://github.com/joonlab/joonlab-claudecode-setting-for-shareWrote 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/skills/joonlab/joonlab-claudecode-setting-for-share/langchain-fundamentals)<a href="https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/langchain-fundamentals"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langchain-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/joonlab/joonlab-claudecode-setting-for-share/langchain-fundamentals"><img src="https://agentmods.dev/badge/skills/joonlab/joonlab-claudecode-setting-for-share/langchain-fundamentals.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00030 | $0.03107 |
| Opus 5 | $0.00015 | $0.01554 |
| Sonnet 5 | $0.00006 | $0.00621 |
| Haiku 4.5 | $0.00003 | $0.00311 |
Grade A, and why
langchain-fundamentals 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 12d 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.
This is a copy
100% identical to langchain-fundamentals — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<create_agent>
Creating Agents with create_agent
create_agent() is the recommended way to build agents. It handles the agent loop, tool execution, and state management.
Agent Configuration Options
| Parameter | Purpose | Example |
|---|---|---|
model |
LLM to use | "anthropic:claude-sonnet-4-5" or model instance |
tools |
List of tools | [search, calculator] |
system_prompt / systemPrompt |
Agent instructions | "You are a helpful assistant" |
checkpointer |
State persistence | MemorySaver() |
middleware |
Processing hooks | [HumanInTheLoopMiddleware] (Python) / [humanInTheLoopMiddleware({...})] (TypeScript) |
| </create_agent> |
@tool def get_weather(location: str) -> str: """Get current weather for a location.
Args:
location: City name
"""
return f"Weather in {location}: Sunny, 72F"
agent = create_agent( model="anthropic:claude-sonnet-4-5", tools=[get_weather], system_prompt="You are a helpful assistant." )
result = agent.invoke({ "messages": [{"role": "user", "content": "What's the weather in Paris?"}] }) print(result["messages"][-1].content)
</python>
<typescript>
```typescript
import { createAgent } from "langchain";
import { tool } from "@langchain/core/tools";
import { z } from "zod";
const getWeather = tool(
async ({ location }) => `Weather in ${location}: Sunny, 72F`,
{
name: "get_weather",
description: "Get current weather for a location.",
schema: z.object({ location: z.string().describe("City name") }),
}
);
const agent = createAgent({
model: "anthropic:claude-sonnet-4-5",
tools: [getWeather],
systemPrompt: "You are a helpful assistant.",
});
const result = await agent.invoke({
messages: [{ role: "user", content: "What's the weather in Paris?" }],
});
console.log(result.messages[result.messages.length - 1].content);
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.
- 12d ago First seen · 395 lines · 30 tokens per session scan A b5bd17f0c183
langchain-fundamentals is a skill published in the GitHub repository joonlab/joonlab-claudecode-setting-for-share (10 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 3,107 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langchain-fundamentals, differing in 20 lines, and is treated as a copy.
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