A tracking plan can say an event is implemented while the code never sends it—or the code can send events the plan never approved. The plan-drift CLI described by sunnydachs compares a JSON tracking plan with Python source using static AST inspection, flagging both kinds of mismatch along with property-key differences and event names it cannot resolve.
What plan-to-code drift looks like
Imagine a team checks its dashboard for authentication events and finds none. The tracking plan may list an event that was never added to the application. The reverse can happen too: developers may add tracking calls without updating the plan. A one-way check would miss one of those cases.
In an article dated September 18, 2026, sunnydachs describes plan-drift as a command-line tool for comparing a JSON tracking plan against Python code. Its central idea is a bidirectional check: look for planned events missing from the code and implemented events missing from the plan. The article’s feature descriptions and examples are the author’s account; the repository and current release were not independently verified.
What the CLI reports
The article shows four finding labels. They identify different kinds of work rather than proving that an event reaches an analytics destination or appears in a dashboard.
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| Finding | Meaning |
|---|---|
| UNEXPECTED EVENT | An event appears in the implementation but not in the tracking plan. |
| UNIMPLEMENTED EVENT | An event is in the plan, but the scanner finds no matching call. |
| PROPERTY MISMATCH | The event’s property keys differ from the plan—for example, code supplies an undeclared key. |
| DYNAMIC | The event name is expressed dynamically and cannot be resolved statically, so a person must review it. |
The sample command output in the source article includes counts and file-and-line findings. Those counts are illustrative output, not measured industry statistics.
How to run the described check
The article gives these example commands:
plan-drift --plan tracking-plan.json— compare the plan with the default repository scope described by the tool.plan-drift --plan tracking-plan.json ./src --json— scan the specified source directory and request JSON output.
The source says test files such as tests.py and test_*.py are excluded so test fixtures are not mistaken for production instrumentation. Treat this as the behavior described in the article, not as independently confirmed behavior of a current installation. The article does not establish the present installation procedure, supported release, or repository license.
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Why use static AST inspection?
According to sunnydachs, the scanner reads Python syntax trees, is read-only and deterministic, and does not use an LLM. The author’s rationale is that repeatable checks are easier to rely on in CI for work with clear, rule-based answers. The author puts the principle this way: “Use deterministic tools for deterministic work.” This is a design argument, not an independently tested comparison with other QA methods.
In a team workflow, the check can be useful after a tracking plan is drafted, when reviewing code changes that may add or remove instrumentation, or as a CI warning that prompts someone to reconcile code and plan. A finding is a signal to investigate: static source inspection cannot by itself establish that an analytics request succeeds at runtime or arrives in a downstream dashboard.
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What it does not establish
- Language coverage: The described version targets Python
.pyfiles. JavaScript and other languages are not directly supported in the account provided. - Dynamic names: Expressions the scanner cannot resolve are marked for manual review rather than inferred automatically.
- Schema depth: The described property check concerns keys. It does not validate property values or complete type matches.
- Runtime delivery: The comparison is against source code; the article does not describe runtime or event-pipeline validation.
These boundaries matter when interpreting a clean report: it means the described static comparison found no listed discrepancies within its scope, not that every event is correctly emitted, typed, delivered, or represented on a dashboard.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When this approach fits
plan-drift is most relevant when a team’s instrumentation is in Python, its plan is represented in JSON, and it wants a repeatable check for obvious plan/code divergence. Teams using other languages, heavily dynamic event naming, or requirements for validating values, types, and actual event delivery need additional checks. The source article does not provide empirical comparisons with runtime validators or other tracking QA tools.
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