Nine-dimension drift analysis
Scans a repository across nine production-readiness dimensions so you can see drift as a structured set of issues instead of a single vague score.
ReWeaver AI DriftDetector
ReWeaver AI DriftDetector scans a GitHub repository for code drift across nine production-readiness dimensions. It shows where issues appear, how they change over commits, and includes a technical-debt estimate.
Overview
ReWeaver AI DriftDetector is a repository scanner that looks for drift in code that appears to work. The product frames drift as the silent accumulation of mistakes and evaluates a repo across nine dimensions of production readiness.
It is designed for users who want to understand not just whether a repository has problems, but where those problems are, when they were introduced, and how severe they are. The homepage says you can enter a GitHub repo and get a report that maps issue count, line-level locations, commit history, and an estimate of technical debt.
The site also emphasizes privacy for private repositories. According to the homepage, code moves from GitHub directly to the scanner, is processed in temporary memory, is not written to disk, and no cached clone, score, or report is retained after the scan ends.
What DriftDetector reports
Issue counting and classification
Shows how many issues exist and what kind they are, helping you understand whether a repo has a small number of concentrated problems or broader drift.
Line-level issue locations
Surfaces exactly where code has drifted from intent or standards, line by line, so you can move from detection to review faster.
Commit-by-commit history scoring
Scores every commit in the history so you can see when the gap opened and how drift changed over time.
Technical-debt estimate
Estimates the technical debt represented by the drift, framed as the time it would have taken to find the same issues manually.
Private-scan handling
Describes private scans as staying within temporary memory, with no cached clone, scores, name, or report left behind after the scan.
Practical ways to use DriftDetector
Check a repo that seems fine
Use it after a repository looks healthy but you suspect hidden drift. The scan helps reveal issues that are easy to miss when the code still runs.
Find where standards diverged
Use the line-level output to review specific files or sections instead of manually searching the codebase for mismatches between intent and implementation.
Trace when drift started
Use the commit history scoring to pinpoint the point at which drift increased, which is useful when tracking regressions or reviewing a risky change set.
Frame the cost of cleanup
Use the technical-debt estimate to communicate the cost of drift in a way that is easier to discuss with teammates than raw issue counts alone.
Scan private repositories
Use the private-scan flow when working with sensitive repositories and you want the source text’s stated no-retention handling for scan artifacts.
Pros and Cons
Pros
- Evaluates drift across nine production-readiness dimensions instead of relying on a single high-level signal.
- Identifies where drift occurs line by line, which is useful when the goal is to review specific code changes.
- Scores drift over commit history, making it easier to see when a problem emerged.
- Includes a technical-debt estimate that translates drift into a more operational frame of reference.
- States a clear privacy posture for private scans, including temporary in-memory processing and no retained report or cached clone.
Cons
- The pricing page provided in the source returns a page-not-found error, so pricing and plan structure are not available from the site evidence here.
- The homepage does not document integrations, supported environments, or workflow details beyond entering a GitHub repo.
- The source set is thin on setup guidance, so it is unclear how results are reviewed or shared beyond the on-page descriptions.
FAQ
How does DriftDetector work?
DriftDetector scans a GitHub repository for drift across nine production-readiness dimensions and reports where issues occur line by line and commit by commit. The homepage does not provide a more detailed setup guide or docs flow.
What do I need to use it?
The site says you can enter a GitHub repo to scan it. It does not document additional supported sources, runners, or CI/CD integrations on the pages provided.
What does the scan output show?
DriftDetector returns the number and type of issues, how far the code is from “done,” the exact locations of drift, drift over time by commit, and a technical-debt estimate.
Is private repository code stored?
The homepage states that private repository code goes directly from GitHub to the scanner, is processed in temporary memory, is not written to disk, and is not kept after the scan. The only stored item mentioned is an encrypted GitHub token until disconnect or revocation.
Quick Facts
- Product
- ReWeaver AI DriftDetector
- Category
- Developer Tool
- Primary input
- GitHub repository
- Scan focus
- Nine production-readiness dimensions
- Output style
- Issue counts, line-level locations, commit history, and technical debt
- Source domain
- drift.reweaver.ai
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