Official agent skill

Docs Code Samples

by langchain-ai in langchain-ai/docs

A skill your agent uses when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify…

OfficialMITAuto-check passedAI & LLM Engineering

Install Docs Code Samples

skills CLI
$ npx skills add langchain-ai/docs --skill docs-code-samples -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install langchain-ai/docs docs-code-samples --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/langchain-ai/docs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/docs-code-samples .claude/skills/docs-code-samples && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
docs-code-samples
GitHub stars
424
Token cost
~4.6k tokens
SKILL.md length
1,837 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify…

  • Works in 6 steps: Create the code sample file → Add snippet delineators → Add runnable test code in remove blocks → …
  • Migrating inline code samples from LangChain docs (MDX files) into external
  • SKILL.md covers Overview, When to use, Directory structure and Step-by-step instructions, plus 3 more sections
  • Calls make, go and curl; reaches api.smith.langchain.com; needs LANGSMITH_API_KEY and OPENAI_API_KEY

What it does

Docs Code Samples is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets. Applies when extracting code blocks from documentation, creating runnable code samples, using snippet delineators, or wiring snippet output into MDX includes.

Its SKILL.md is about 4.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: LangChain docs monorepo with Mintlify. Requires Python and Make (Node.js is also required for TypeScript samples).

It sits in AI & LLM Engineering, covering Building AI agents and Markdown. It works with LangChain, LangGraph, LangSmith and TypeScript. The repository describes itself as: Unified LangChain documentation. The licence is MIT.

When your agent uses it

  • Migrating inline code samples from LangChain docs (MDX files) into external
  • Testable code files that are extracted by this repos snippet scripts and used as Mintlify snippets

Example prompts

  • “/docs-code-samples”

Requirements

  • Python 3
  • Node.js
  • A credential in LANGSMITH_API_KEY
  • A credential in OPENAI_API_KEY
  • Compatibility (from SKILL.md): LangChain docs monorepo with Mintlify. Requires Python and Make (Node.js is also required for TypeScript samples).

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Create the code sample file
  2. Add snippet delineators
  3. Add runnable test code in remove blocks
  4. Test the code sample
  5. Run snippet extraction
  6. Update the MDX file to use the snippet

What it can do on your machine

Read from SKILL.md and the folder at commit 85713b3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • make
    • go
    • curl
    • bash
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.smith.langchain.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LANGSMITH_API_KEY
    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    LangChain docs monorepo with Mintlify. Requires Python and Make (Node.js is also required for TypeScript samples).

    From compatibility in the SKILL.md frontmatter.

Context cost

Docs Code Samples loads about 4.6k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 1,837 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~4.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from langchain-ai/docs at commit 85713b3, republished under its MIT licence (© langchain-ai). 1,837 words, ~4,584 tokens.

Download SKILL.mdSave it as .claude/skills/docs-code-samples/SKILL.md (or your agent's skills folder).
name
docs-code-samples
description
Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets. Applies when extracting code blocks from documentation, creating runnable code samples, using snippet delineators, or wiring snippet output into MDX includes.
compatibility
LangChain docs monorepo with Mintlify. Requires Python and Make (Node.js is also required for TypeScript samples).
license
MIT
metadata.author
langchain
metadata.version
1.0

docs-code-samples

Overview

This skill documents the workflow for moving inline code samples from LangChain documentation into standalone, testable files that this repo extracts into snippets for use in MDX using Mintlify.

When to use

  • Migrating inline Python, TypeScript/JavaScript, or Java code blocks from MDX to external files
  • Creating runnable, testable code samples for documentation
  • Setting up snippet extraction and Mintlify snippet includes

Directory structure

Code samples live under src/code-samples/ in folders that match the product:

  • langchain/ — LangChain docs
  • langgraph/ — LangGraph docs
  • deepagents/ — Deep Agents docs
  • langsmith/ — LangSmith docs

Example:

src/
├── code-samples/              # Source: testable code with snippet tags
│   ├── langchain/
│   │   ├── return-a-string.py
│   │   └── return-a-string.ts
│   ├── langgraph/
│   │   ├── langgraph-sql-agent.py
│   │   └── langgraph-sql-agent.ts
│   ├── deepagents/
│   │   └── example-skill.py
│   └── langsmith/
│       ├── trace-example.py
│       └── trace-example.java
├── code-samples-generated/    # Snippet output (gitignored)
│   ├── return-a-string.snippet.tool-return-values.py
│   ├── return-a-string.snippet.tool-return-values.ts
│   └── ...
└── snippets/
    └── code-samples/          # MDX snippets for docs (all products)
        ├── tool-return-values-py.mdx
        ├── tool-return-values-js.mdx
        └── ...

Prefer one file per doc page or topic: Collocate related snippets in a single code sample file whenever they belong to the same MDX page, tutorial flow, or feature (for example, setup plus invocation, or a do/don't pair). Use multiple :snippet-start: / :snippet-end: pairs in that file rather than splitting into several .py or .ts files. Reserve separate files for unrelated samples or when a page genuinely needs independent test entry points.

More than one snippet in one file: A single code sample file can contain more than one named snippet using different :snippet-start: snippet-name and :snippet-end: pairs. Each snippet must have a unique name. Shared imports, helpers, and :remove-start: test harness code live once in the file; only the fenced regions between snippet tags appear in the generated MDX snippets.

When to split TypeScript samples into separate files: Python samples can usually keep multiple snippets in one file because later definitions overwrite earlier ones at module scope. TypeScript and JavaScript cannot: make test-code-samples runs the entire .ts file, and every snippet's code executes in the same module scope. Split into separate .ts files when snippets would collide, for example:

  • Duplicate import bindings (for example two snippets both import { interrupt } from "@langchain/langgraph")
  • Duplicate const / let / class / function declarations with the same name (for example two snippets both declare const State = ...)
  • Two self-contained snippets that each need their own imports and top-level setup

Keep related snippets in one Python file when possible. For TypeScript, use one file per independently runnable snippet when imports or top-level bindings would conflict. Name sibling files clearly, for example langgraph-interrupts-validate-conditional-edge-pattern.ts and langgraph-interrupts-validate-conditional-edge.ts. Put shared test-only setup in :remove-start: blocks inside each file rather than importing between sample files.

Within a single TypeScript file, :remove-start: blocks and snippet regions share the same module scope when make test-code-samples runs the file. Do not import the same binding in both places. Keep imports that appear in the docs snippet inside the snippet; limit :remove-start: imports to symbols used only by the test harness (for example Command, MemorySaver) that the snippet does not import.

Step-by-step instructions

1. Create the code sample file

Place the file under src/code-samples/ in the folder for the product: langchain/, langgraph/, deepagents/, or langsmith/ (for example, src/code-samples/langgraph/langgraph-sql-agent.py for LangGraph docs).

Use a descriptive filename, for example, return-a-string.py, return-a-string.ts, or traceable-pipeline.java. When a doc page needs several code blocks for the same feature, add them as multiple snippets in one Python file (for example, rubric-configure.py with rubric-configure-py and rubric-invoke-py) instead of creating rubric-configure.py and rubric-invoke.py. For TypeScript, use one file per snippet when module-scope imports or bindings would conflict (see When to split TypeScript samples into separate files above).

2. Add snippet delineators

Wrap the code that should appear in the docs with snippet tags:

Python:

python
# :snippet-start: snippet-name-py
from langchain.tools import tool

@tool
def get_weather(city: str) -> str:
    """Get weather for a city."""
    return f"It is currently sunny in {city}."

# :snippet-end:

TypeScript/JavaScript:

ts
// :snippet-start: snippet-name-js
import { tool } from "langchain";
// ... tool definition ...
// :snippet-end:

Java:

java
// :snippet-start: snippet-name-java
public class Example {
  public static void main(String[] args) {
    System.out.println("hello");
  }
}
// :snippet-end:

Go:

go
// :snippet-start: snippet-name-go
package main

import "fmt"

func main() {
	fmt.Println("hello")
}
// :snippet-end:

Bash (cURL):

bash
# :snippet-start: snippet-name-sh
curl "https://api.smith.langchain.com/api/v1/runs" \
  -H "x-api-key: $LANGSMITH_API_KEY"
# :snippet-end:

Choose a unique snippet-name in kebab-case. All snippet names must include a language suffix: -py for Python files, -js for TypeScript/JavaScript files, -java for Java files, -kt for Kotlin files, -go for Go files, and -sh for bash/cURL files (for example, tool-return-values-py, tool-return-values-js, traceable-pipeline-java, traceable-pipeline-kt, traceable-pipeline-go, traceable-pipeline-sh). This becomes the base of the output filename.

3. Add runnable test code in remove blocks

Wrap any code that makes the sample executable but should not appear in docs.

Run snippet code before exiting. make test-code-samples must execute the snippet body, not skip it. Do not put raise SystemExit(0), process.exit(0), or exit 0 at the top of a file (or before :snippet-start:) so the test passes without running imports, constructors, or API calls. That only checks that the file parses; it does not validate function signatures, option shapes, or import paths.

Place :remove-start: blocks after the snippet when you can, so the harness runs assertions on values the snippet created:

Python (preferred):

python
# :snippet-start: example-py
from deepagents import create_deep_agent

agent = create_deep_agent(model="google_genai:gemini-3.6-flash")
# :snippet-end:

# :remove-start:
assert agent is not None
print("✓ example validated")
# :remove-end:

TypeScript (preferred):

ts
// :snippet-start: example-js
import { DeepAgentsServer } from "deepagents-acp";

const server = new DeepAgentsServer({
  agents: { name: "careful-agent", interruptOn: { write_file: true } },
});
// :snippet-end:

// :remove-start:
if (!server) throw new Error("server not created");
console.log("✓ example validated");
// :remove-end:

For samples whose docs show a blocking tail (for example await server.start(), asyncio.run(main()), or agent.invoke() with a live model), keep setup and construction in the snippet so types and signatures are checked, then move only the blocking call into a trailing :remove-start: block—or omit it when construction alone is enough:

python
# :snippet-start: server-example-py
server = AgentServerACP(agent)
# :snippet-end:

# :remove-start:
# Do not call await run_agent(server) here — it blocks on stdio.
assert server is not None
print("✓ server-example validated")
# :remove-end:

Do not short-circuit before the snippet:

python
# :remove-start:
raise SystemExit(0)  # BAD: snippet below never runs
# :remove-end:

# :snippet-start: example-py
...

The examples below show harness code that invokes behavior when the snippet defines callable helpers:

Python:

python
# :remove-start:
if __name__ == "__main__":
    result = get_weather.invoke({"city": "San Francisco"})
    assert result == "It is currently sunny in San Francisco."
    print("✓ Tool works as expected")
# :remove-end:

TypeScript:

ts
// :remove-start:
async function main() {
  const result = await getWeather.invoke({ city: "San Francisco" });
  if (result !== "It is currently sunny in San Francisco.") {
    throw new Error(`Expected "...", got "${result}"`);
  }
  console.log("✓ Tool works as expected");
}
main();
// :remove-end:

The extraction script strips :remove-start: / :remove-end: content when extracting snippets.

4. Test the code sample

Before extracting snippets, verify the code sample runs correctly:

bash
# Test the file(s) you added (faster)
make test-code-samples FILES="src/code-samples/langchain/return-a-string.py"

# Or run all code samples
make test-code-samples

For multiple files: FILES="path1 path2". Fix any failures before proceeding—do not extract snippets until the samples pass.

Java files (.java) under src/code-samples/ are run using jbang. To keep CI green, Java samples must:

  • Print at least one line of output so it's obvious the sample ran
  • Exit successfully (code 0) when optional API keys are not set, for example:
    • OPENAI_API_KEY for LLM calls
  • Fail fast (non-zero exit) when a key is required for the sample to run, for example manage-prompts-0-push.java without LANGSMITH_API_KEY

make test-code-samples runs every .java file under src/code-samples/ in lexical path order (after all Python and TypeScript samples). That order is unrelated to section order in the docs. If one sample must run before another (for example creating a hub prompt before pulling it), name the source files so they sort correctly. For example, manage-prompts-pull.java runs before manage-prompts-push.java because pull sorts before push; use prefixes such as manage-prompts-0-push.java and manage-prompts-1-pull.java when you need push to run first.

Go files (.go) under src/code-samples/ are run with go run from src/code-samples/, which shares a single go.mod/go.sum at that directory (add new dependencies there with go get, then go mod tidy, similar to how .ts samples share src/code-samples/package.json). go run <file>.go only compiles that one file, not its sibling files in the same directory, so — like Kotlin — put each snippet variant in its own file (topic-before.go, topic-after.go) rather than multiple snippets sharing one file: two files in the same package cannot both declare func main(). Go samples do not guard on missing keys — let the SDK call fail fast (matching Python's behavior) rather than skipping with a printed message. make test-code-samples runs .go files last, after Kotlin, in lexical path order.

Bash/cURL files (.sh) under src/code-samples/ are run with bash <file>.sh from src/code-samples/. Like Go, put each snippet variant in its own file (topic-before.sh, topic-after.sh) rather than sharing one file. Hide test-only setup (#!/usr/bin/env bash, set -euo pipefail, resolving a real ID/value for a <placeholder> shown in the docs) in # :remove-start:/# :remove-end: blocks so the visible snippet is exactly the illustrative curl command a reader would copy — including no shebang or set -e line. curl does not exit non-zero on an HTTP error status by itself, so when a script pipes a response into jq to extract a value used by a later request (for example resolving a project ID), add a hidden check that the resolved value is non-empty and not the literal string null before continuing, so a bad API response fails the test loudly instead of silently propagating into later requests. make test-code-samples runs .sh files last, after Go, in lexical path order.

Check formatting with:

bash
make lint

Fix any ruff or mypy issues before proceeding. Run make format to auto-fix formatting.

Show full SKILL.md (574 more words)Show less
5. Run snippet extraction

From the repo root:

bash
make code-snippets

To limit extraction to specific samples (faster while iterating), set CODE_SNIPPET_SOURCES to space-separated repo-relative paths under src/code-samples/:

bash
CODE_SNIPPET_SOURCES="src/code-samples/langsmith/trace.java" make code-snippets

This command:

  1. Runs python scripts/extract_code_snippets.py (line-based, Bluehawk-compatible; handles /** in TS strings). Optional env CODE_SNIPPET_SOURCES limits extraction to specific paths under src/code-samples/; other stems already in src/code-samples-generated/ are left alone.
  2. Runs scripts/generate_code_snippet_mdx.py to produce MDX snippets in src/snippets/code-samples/ (always regenerates MDX from everything under src/code-samples-generated/)

Output files:

  • return-a-string.snippet.tool-return-values.py → tool-return-values-py.mdx
  • return-a-string.snippet.tool-return-values.ts → tool-return-values-js.mdx
6. Update the MDX file to use the snippet

Add an import at the top of the MDX file (after frontmatter):

mdx
import ToolReturnValuesPy from '/snippets/code-samples/tool-return-values-py.mdx';
import ToolReturnValuesJs from '/snippets/code-samples/tool-return-values-js.mdx';

Replace the inline code blocks with the snippet components:

mdx
:::python

<ToolReturnValuesPy />

:::

:::js

<ToolReturnValuesJs />

:::

Naming conventions

ElementConventionExample
Code fileDescriptive, kebab-casereturn-a-string.py, return-a-string.ts, traceable-pipeline.java, traceable-pipeline.kt, traceable-pipeline.go, traceable-pipeline.sh
Snippet nameKebab-case with language suffix: -py for Python, -js for JS/TS, -java for Java, -kt for Kotlin, -go for Go, -sh for bash/cURLtool-return-values-py, tool-return-values-js, traceable-pipeline-java, traceable-pipeline-kt, traceable-pipeline-go, traceable-pipeline-sh
MDX snippet (Python){snippet-name}.mdx (snippet name ends in -py)tool-return-values-py.mdx
MDX snippet (JS){snippet-name}.mdx (snippet name ends in -js)tool-return-values-js.mdx
Component namePascalCaseToolReturnValuesPy, ToolReturnValuesJs

Script behavior

scripts/generate_code_snippet_mdx.py:

  • Reads *.snippet.*.py and *.snippet.*.ts from src/code-samples-generated/
  • Wraps content in fenced code blocks (```python or ```ts)
  • When a snippet contains a model string, expands it into a Mintlify <CodeGroup> with the seven quickstart provider tabs (Google, OpenAI, Anthropic, OpenRouter, Fireworks, Baseten, Ollama):
    • Python: model="…" or model = "…"
    • TypeScript: model: "…" or model = "…" (including let model = "…")
    • Only the quoted model ID is swapped per tab, so assignment vs property syntax is preserved
    • Put # KEEP MODEL / // KEEP MODEL on the line before a model string to leave that occurrence unchanged
  • Writes to src/snippets/code-samples/{snippet-name}-py.mdx or -js.mdx

To support additional languages, add config entries in that script.

Guidelines

  • Run snippet code in tests — :remove-start: harnesses must not exit before the snippet runs. Let imports, constructors, and configuration execute so make test-code-samples catches wrong signatures, renamed options, and broken import paths. Put SystemExit / process.exit only after the snippet (or use them to skip a trailing blocking call such as server.start(), not the whole sample).
  • Collocate related snippets in one code sample file per doc page or feature when possible (Python and Java). Import each generated MDX snippet separately in the MDX file (for example, <RubricConfigurePy /> then <RubricInvokePy /> from the same source file). For TypeScript, split into separate .ts files when snippets duplicate imports or top-level bindings; still import each generated MDX snippet in the MDX file the same way.
  • Do not mock LangChain internals (for example unittest.mock.patch on init_chat_model helpers) so that imports resolve real chat model instances. Do not use fake chat models in docs code samples (for example GenericFakeChatModel, FakeListChatModel, or other langchain_core testing fakes). Wire a real chat model (for example ChatOpenAI) so snippets match what readers run; make test-code-samples requires a valid API key when the sample calls the model.
  • Do not change pyproject.toml when making code sample changes.
  • Always run make test-code-samples FILES="path/to/your/file.py" before make code-snippets to ensure new samples pass.
  • Run make lint once the code sample is written; fix any issues (or run make format to auto-fix).
  • Do not add code samples to linting ignore rules when making lint-related changes—fix the code instead.
  • src/code-samples-generated/ is gitignored; regenerate with make code-snippets, narrowing to specific files with CODE_SNIPPET_SOURCES while iterating.
  • Reference AGENTS.md for docs style and rules.
  • Use :::python and :::js fences for language-specific content; the build produces separate Python and JavaScript doc versions.
  • For python tests, try to correct the type rather than adding # type: ignore[arg-type]

© langchain-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/docs-code-samples of langchain-ai/docs.

Open the folder on GitHubat commit 85713b3

Compare with similar skills

Docs Code Samples next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Langgraph Testing Evaluationsoba-labs/langchain-agent-skills107—~2.3kAutomated safety check: PassMIT
Langgraph Typescript Quickstartlangchain-ai/langchain-skills1.3k—~455Automated safety check: NotesMIT
LangSmith Trace DebuggingComposioHQ/awesome-claude-skills77k9 repos~2.7kAutomated safety check: PassNone
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Questions about Docs Code Samples

What does Docs Code Samples do?

A skill your agent uses when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify…. Docs Code Samples is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Use this skill when migrating inline code samples from LangChain docs (MDX files) into external, testable code files that are extracted by this repo’s snippet scripts and used as Mintlify snippets.

When should I use Docs Code Samples?

Docs Code Samples fits situations like: migrating inline code samples from LangChain docs (MDX files) into external; testable code files that are extracted by this repos snippet scripts and used as Mintlify snippets.

How do I install Docs Code Samples in Claude Code?

Run `npx skills add langchain-ai/docs --skill docs-code-samples -a claude-code`. Or copy the skill folder (.agents/skills/docs-code-samples in langchain-ai/docs) into .claude/skills/docs-code-samples in your project. Claude Code loads it when a task matches its description.

How do I install Docs Code Samples in Codex?

Run `npx skills add langchain-ai/docs --skill docs-code-samples -a codex`. Or copy the skill folder (.agents/skills/docs-code-samples in langchain-ai/docs) into .agents/skills/docs-code-samples in your project. Codex loads it when a task matches its description.

Can I use Docs Code Samples in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add langchain-ai/docs --skill docs-code-samples -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/docs-code-samples, .gemini/skills/docs-code-samples, .github/skills/docs-code-samples and .opencode/skills/docs-code-samples in your project.

What does Docs Code Samples need to run?

Going by SKILL.md and its folder, Docs Code Samples needs the command-line tools its instructions call (make, go, curl, bash and python) and credentials named LANGSMITH_API_KEY and OPENAI_API_KEY. Our summary lists: Python 3; Node.js; A credential in LANGSMITH_API_KEY; A credential in OPENAI_API_KEY. Compatibility (from SKILL.md): LangChain docs monorepo with Mintlify. Requires Python and Make (Node.js is also required for TypeScript samples)..

Does Docs Code Samples access the network?

SKILL.md names 1 domain. In commands or code: api.smith.langchain.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Docs Code Samples safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Docs Code Samples use?

Docs Code Samples is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Docs Code Samples use?

About 4.6k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Docs Code Samples?

Skills that share tags, products or a category with Docs Code Samples: Langchain Dependencies (langchain-ai/langchain-skills, 1.3k stars), Langgraph Testing Evaluation (soba-labs/langchain-agent-skills, 107 stars), Langgraph Typescript Quickstart (langchain-ai/langchain-skills, 1.3k stars) and LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Docs Code Samples?

langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/docs, which has 424 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 7, 2026.

Source: langchain-ai/docs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.