skills-benchmarks is a test suite that measures how the design of skill documentation affects Claude Code's adherence to recommended coding patterns. It is used to compare documentation approaches across LangChain-related tasks and other agent workflows. Its catalogue entries represent skills, hooks, instructions, and a plugin used in the benchmark project.
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 langchain-ai/skills-benchmarks --skill langchain-fundamentalsgit clone --depth 1 https://github.com/langchain-ai/skills-benchmarksWrote 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/langchain-ai/skills-benchmarks/langchain-fundamentals)<a href="https://agentmods.dev/skills/langchain-ai/skills-benchmarks/langchain-fundamentals"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/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/langchain-ai/skills-benchmarks/langchain-fundamentals"><img src="https://agentmods.dev/badge/skills/langchain-ai/skills-benchmarks/langchain-fundamentals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 342 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.03113 |
| Opus 5 | $0.00015 | $0.01556 |
| Sonnet 5 | $0.00006 | $0.00623 |
| 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- langchain-fundamentals — 100% identical, 0 lines differ
- langchain-fundamentals — 100% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 393 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 · 393 lines · 30 tokens per session scan A d4dc29350e0d
langchain-fundamentals is a skill published in the GitHub repository langchain-ai/skills-benchmarks (116 stars, last pushed 24d ago), licensed MIT. It adds 30 tokens to every session and 3,113 once invoked, about $0.0002 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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