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doc-drift is a command-line checker for finding certain kinds of drift between Python code examples in Markdown and the code in a repository. It parses snippets with Python’s abstract syntax tree (AST), then reports missing functions or classes and changes to function argument names. Its author says it does not import or execute the inspected code. That makes it a focused static check—not proof that an example runs or behaves as documented.

What doc-drift checks

In a DEV Community article posted September 16, 2026, author and builder sunnydachs describes doc-drift as a CLI that scans repository Markdown files for fenced code blocks, extracts Python functions and classes from those blocks, and compares them with constructs in the codebase.

The author describes three finding types:

  • SIGNATURE DRIFT: A documented function exists in the code, but its argument names differ.
  • MISSING: A documented function or class was not found in the repository.
  • UNPARSEABLE: A code block is not valid Python—for example, because it contains pseudocode or a placeholder. The author presents this as informational, not as a confirmed code mismatch.

The matching rule allows examples to be simplified: they may leave out arguments or class methods, but should not add functions or methods that the implementation does not have. In the author’s words, “Omit arguments — allowed. Invent arguments or functions that don’t exist in the code — forbidden.” This is doc-drift’s stated design rule, not a universal standard for documentation checks.

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How to run it, according to the author

The article shows a repository scan using the doc-drift command and a scan of a specified repository with JSON output:

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doc-drift /path/to/repo --json

The first form is shown for scanning a repository; the second specifies a repository path and requests machine-readable output. The article does not establish current installation steps, release details, or a complete list of command options. It states Python 3.11 or later is required.

Although the JSON option could be useful in automation, the article does not document a maintained GitHub Action or a particular CI setup. Treat integration into a workflow as something to configure and verify, rather than a ready-made feature established by the source.

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What static AST checking means here

Python’s AST represents source code as a structured syntax tree. The author says doc-drift uses Python’s standard ast module and compares code at that syntax level. Sunnydachs summarizes the safety boundary this way: “It never imports or executes your code — it compares at the syntax-tree level.”

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That distinction matters when scanning a repository: according to the author, the checker does not run the inspected project’s code to perform its comparison. Static inspection also limits what the tool can conclude. Finding a function name and comparing argument names does not demonstrate that a snippet executes successfully, produces the right output, or describes the implementation’s behavior correctly.

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Where it fits—and where it can mislead

Good fit: examples intended to track the implementation

doc-drift is aimed at documentation trees whose Python examples are expected to mirror functions and classes in the repository. In that setting, a missing name or changed signature can point to documentation that needs attention.

Potential false positives: illustrative snippets

A README may include a hypothetical example that is intentionally not part of the project. The checker cannot infer that intent, so it may report the example as MISSING. Teams would need to review such findings and decide whether the snippet should be excluded, revised, or treated as documentation that ought to match real code; the article does not establish a specific exclusion mechanism.

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Limited coverage: Python and names, not meaning

The author says other-language code blocks may be counted but are not checked. The comparison is name-based: default values and type annotations are ignored. It targets names and argument-name or arity differences, but does not establish semantic correctness. A passing check therefore cannot certify non-Python examples, defaults, annotations, runtime behavior, or whether the prose accurately explains what code does.

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What the reported scan does—and does not—show

Sunnydachs reports scanning 1,692 Markdown files and 4,451 code blocks in a repository in 2026. The author says that run found one genuine drift: documentation showed a function with two arguments after its implementation had changed to one. The author also says the scan exposed an over-eager default exclusion that caused false positives, which was then corrected.

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Those counts and outcomes are the author’s report, not independently verified results or a benchmark. They describe one repository and do not establish how often README examples drift across projects generally.

How to decide whether it suits your project

Consider doc-drift if the codebase is Python 3.11 or later and you want a static check for selected Python functions and classes shown in Markdown. Before relying on it, assess how your documentation labels illustrative snippets and how your team will triage findings. For any checker, useful decision points include language coverage, whether it executes examples or analyzes them statically, how deeply it validates behavior, how it handles intentional examples and false positives, available report formats and CI integration, and whether the project is maintained.

The available article identifies a GitHub repository, but its code and current release information were not independently confirmed. It therefore does not establish present installation details, licensing, or maintenance status. Verify those directly before adopting the tool.

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