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Official agent skill

Verify Against Source

by langchain-ai in langchain-ai/docs

Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source.

OfficialMITAuto-check passedAI & LLM Engineering

Install Verify Against Source

skills CLI
$ npx skills add langchain-ai/docs --skill verify-against-source -a claude-code

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

GitHub CLI
$ gh skill install langchain-ai/docs verify-against-source --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/verify-against-source .claude/skills/verify-against-source && 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
verify-against-source
GitHub stars
426
Token cost
~1.6k tokens
SKILL.md length
838 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source.

  • Works in 4 steps: Pick the strongest evidence available → Know which repository owns the claim → Verify the claim, not the sentence → …
  • Reviewing a page that asserts how LangChain
  • SKILL.md covers Step 1. Pick the strongest…, Step 2. Know which repository…, Step 3. Verify the claim, not… and Step 4. Record what you…, plus 1 more section
  • Calls make, uv and gh

What it does

Verify Against Source is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source. Covers which repository owns each product, how to reach the private ones, running the part of a sample that needs no API key, and what to say about a claim you could not verify. Use when writing or reviewing a page that asserts how LangChain, LangGraph, Deep Agents, or LangSmith behaves, and when a PR describes behavior without citing where it was checked.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

When your agent uses it

  • Reviewing a page that asserts how LangChain
  • LangSmith behaves
  • When a PR describes behavior without citing where it was checked

Example prompts

  • “/verify-against-source”

Requirements

  • Python 3

Workflow steps

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

  1. Pick the strongest evidence available
  2. Know which repository owns the claim
  3. Verify the claim, not the sentence
  4. Record what you checked, and what you could not

What it can do on your machine

Read from SKILL.md and the folder at commit be3028f. 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
    • uv
    • gh

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

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Verify Against Source loads about 1.6k tokens when it runs. Until then it costs about 131 tokens; SKILL.md has 838 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~131
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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 be3028f, republished under its MIT licence (© langchain-ai). 838 words, ~1,635 tokens.

Download SKILL.mdSave it as .claude/skills/verify-against-source/SKILL.md (or your agent's skills folder).
name
verify-against-source
description
Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source. Covers which repository owns each product, how to reach the private ones, running the part of a sample that needs no API key, and what to say about a claim you could not verify. Use when writing or reviewing a page that asserts how LangChain, LangGraph, Deep Agents, or LangSmith behaves, and when a PR describes behavior without citing where it was checked.

Verify a claim against the source

The failure this skill prevents: a page states a default, a precedence order, or a field name that reads plausibly, passes Vale and the link checker, and is wrong. Nothing in CI checks whether a sentence about product behavior is true. A reviewer who knows the product catches some of it; the rest reaches readers.

Published docs, a README, and a model's recollection are all secondary sources. Prefer evidence you produced in this session.

Step 1. Pick the strongest evidence available

Work down this list and stop at the first rung you can actually reach. Each rung is weaker than the one above it.

  • Run the sample. make test-code-samples, or scope it with FILES="src/code-samples/langchain/foo.py". This is the only method that proves a sample works end to end. It needs provider API keys in the environment, so it often fails locally and runs in CI instead.
  • Run the half that needs no model. Most claims are about a library's behavior, not the model's. Stand up the real object in a scratch script and print what it returns. A local MCP server, an adapter, a parser, or a store needs no API key, and the answer it gives is exact rather than inferred.
  • Read the library source. gh api repos/langchain-ai/<repo>/contents/<path> --jq '.content' | base64 -d, or gh api "search/code?q=<symbol>+repo:langchain-ai/<repo>" to locate the file first. Settles what a type is, what a default is, and which branch actually runs.
  • Check the installed package. uv run python -c "from x import Y" confirms an import path resolves at the version this repo pins, which source on main cannot tell you.
  • Look up the signature. The reference-langchain MCP server exposes search_api and get_symbol over reference.langchain.com. Right for "what are the parameters of X", not for "what does X do at runtime".

Step 2. Know which repository owns the claim

ProductRepositoryWhere to look
LangChainlangchain-ai/langchainlibs/langchain_v1/langchain/ for v1, libs/core/ for core, libs/partners/ for provider packages
LangGraphlangchain-ai/langgraphlibs/langgraph/, libs/prebuilt/, libs/checkpoint*/, libs/cli/, libs/sdk-py/ and libs/sdk-js/
Deep Agentslangchain-ai/deepagentsAlso the source of the eval matrix the docs publish
LangSmith SDKlangchain-ai/langsmith-sdkThe client libraries, public
LangSmith platformlangchain-ai/langchainplus (private)smith-backend/ (Python API), smith-go/ (Go services), smith-frontend/ (UI labels and flows), host-backend/, lc_config/ for settings and their defaults
Agent Serverlangchain-ai/langgraph-api (private)Not in the OSS langgraph repo. Also the source of the Agent Server OpenAPI spec PRs
Helm chartslangchain-ai/helmcharts/langsmith/values.yaml for defaults, templates/_helpers.tpl for the value-to-environment-variable mapping
OpenEvalslangchain-ai/openevalsPrebuilt evaluators

Two traps worth naming:

  • A private repo is still readable through gh. gh api works on langchainplus and langgraph-api with the usual credentials. GitHub code search is unreliable on the larger ones, so fall back to the git trees API and fetch a path directly.
  • langchain-ai/deployments is archived. Its GitOps content moved into langchainplus. A skill, script, or note that still points at it is stale.

A self-hosted LangSmith claim usually spans two repositories: the chart sets a value, and the backend decides what to do with it. Check both before describing precedence, because the chart alone does not tell you which setting wins.

Show full SKILL.md (322 more words)Show less

Step 3. Verify the claim, not the sentence

Read the code path that produces the behavior, rather than a name that sounds like it. Three failures that recur:

  • A resolution order inferred from a settings table. Find the query or the conditional that picks a value. An ORDER BY, a COALESCE, or an or expression is the precedence; a list of configurable fields is not.
  • A defensive pattern copied from another sample. Check whether the guarded case can occur. If the constructor only builds an object when a field is present, a .get with a default is documenting a state that never exists.
  • A version or model identifier from memory. Fetch it. The repository's own internal consensus is evidence about the repository, not about the provider. For model IDs specifically, see the Model references section of AGENTS.md.

Step 4. Record what you checked, and what you could not

Name the file and symbol in the pull request body, not just the conclusion. A reviewer can then disagree with the evidence rather than with an assertion.

State the gaps in the same place. Common ones that no amount of source reading closes:

  • A UI string. Source gives you a field and a component; only the running product gives you the rendered label. Describe the behavior rather than quoting a label you have not seen.
  • The model-dependent half of a sample. Say that CI has yet to run it.
  • A beta surface. langchain.mcp and similar namespaces can move under a page that was accurate when written.

Writing "not verified: the visible label of this control" is a better outcome than a confident sentence a reader discovers is wrong.

AGENTS.md holds the rules that apply to every edit, including never fabricating an example and testing code before publishing it. Use docs-code-samples when the claim belongs in a runnable sample rather than in prose, and docs-review for the style pass once the facts are settled.

© 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/verify-against-source of langchain-ai/docs.

Open the folder on GitHubat commit be3028f

Compare with similar skills

Verify Against Source 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.

Verify Against Source compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Verify Against Source this skilllangchain-ai/docs426—~1.6kAutomated safety check: PassMIT
LangSmith Trace DebuggingComposioHQ/awesome-claude-skills77k8 repos~2.7kAutomated safety check: PassNone
Agentsop Observability Setupagentsope/SkillAlchemy466—~4.4kAutomated safety check: PassMIT
Langchain Dependencieslangchain-ai/langchain-skills1.3k—~3.6kAutomated safety check: PassMIT
Langgraph Testing Evaluationsoba-labs/langchain-agent-skills107—~2.3kAutomated safety check: PassMIT
Langsmith Deploymentsoba-labs/langchain-agent-skills107—~1.7kAutomated safety check: PassMIT

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Works with

Categories

Questions about Verify Against Source

What does Verify Against Source do?

Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source. Verify Against Source is an agent skill from langchain-ai/docs, published by the product's own GitHub organization. Check that a code sample, API signature, default, or behavior claim in the docs is actually true, by running it or reading the product source.

When should I use Verify Against Source?

Verify Against Source fits situations like: reviewing a page that asserts how LangChain; langSmith behaves; when a PR describes behavior without citing where it was checked.

How do I install Verify Against Source in Claude Code?

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

How do I install Verify Against Source in Codex?

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

Can I use Verify Against Source 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 verify-against-source -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/verify-against-source, .gemini/skills/verify-against-source, .github/skills/verify-against-source and .opencode/skills/verify-against-source in your project.

What does Verify Against Source need to run?

Going by SKILL.md and its folder, Verify Against Source needs the command-line tools its instructions call (make, uv and gh). Our summary lists: Python 3.

Does Verify Against Source access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Verify Against Source 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 Verify Against Source use?

Verify Against Source is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Verify Against Source use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 Verify Against Source?

Skills that share tags, products or a category with Verify Against Source: LangSmith Trace Debugging (ComposioHQ/awesome-claude-skills, 77k stars), Agentsop Observability Setup (agentsope/SkillAlchemy, 466 stars), Langchain Dependencies (langchain-ai/langchain-skills, 1.3k stars) and Langgraph Testing Evaluation (soba-labs/langchain-agent-skills, 107 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Verify Against Source?

langchain-ai (a GitHub organization, an official publisher) maintains it in langchain-ai/docs, which has 426 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 9, 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.