AI Code Detector

Estimates how likely it is that a repository, or parts of it, was written with AI assistance, and always shows the reasons behind the estimate.

Status: experimental. Methodology v1 combines heuristic signals with a local LLM judge, with weights fitted on 44 repositories. On 13 held-out repositories it ranked every AI repository it scored above 0.5 higher than every human one (AUC 1.00, no false positives at 0.5), but that test set is small and the scores are not calibrated, so confidence never goes above medium. Without a model it falls back to much weaker signals (test AUC 0.79, 29% false positives). Read how it scores before relying on any number.

What it does

Given a GitHub repository URL or a local path, the detector looks for evidence of AI assistance in the code and its history. It returns a range with a confidence level, plus the evidence for and against, so a person can judge the result for themselves.

How it works

It combines two kinds of evidence. The first is a set of quick checks on the repository itself, such as:

• config files left behind by AI coding tools, such as CLAUDE.md, .cursorrules or .github/copilot-instructions.md;
• Co-authored-by trailers that name an AI assistant;
• a whole project arriving in a single commit;
• comments that only restate the line below them.

Some signals point the other way: a long, messy history, personal TODOs and leftover debug code are all evidence of human authorship, and they count too.

The second is a language model acting as a judge. A local model (qwen3.5:9b through Ollama by default) reads up to 20 files, points at the lines behind its opinion, and lists what argues against it. The quotes in the report come from the file, so evidence cannot be invented. It never sees the signal scores. It runs automatically when a model is available, because in evaluation it was the only evidence that worked on recent code.

The two are combined into one estimate for the repository, with weights fitted on data. Commit history and README style are shown as context but not scored: in evaluation they told old projects from new ones, not people from AI.

Input

margin info ai-code-detector shows the rest, including the judge's limits on files, time and cost.

Output

The headline is an estimate with a range and a confidence level, for example "0.55 to 0.80, medium confidence", never a bare percentage. When confidence is low, the report says why: few signals fired, few files could be read, or the evidence pulled both ways.

If the repository's own metadata names an AI tool (a Co-authored-by trailer, say), the report says so separately. That proves the tool was used, not how much of the code it wrote, so it doesn't move the estimate.

Then comes the evidence: what points toward AI and what points toward a person, each item tied to a file, a line range or a commit. Every file gets its own score with the signals that fired and, if the judge read it, the lines it quoted. The report also includes short excerpts of the code around each marked line (at most 80 lines per file and 800 overall), so you can check it without opening the repository.

Finally there's the history: each commit's date, subject, the number of files it touched, any AI tool it names, and whether it was unusually large. Author names are left out on purpose. The report is about code, not about people. It also lists every file that wasn't scored and why (another language, a test file, too small, a lockfile and so on).

Limits

No tool can prove that code was written by AI. Clean human code can look machine-made, and edited AI code can look human. The detector is tuned to avoid false accusations, even if that means it misses some AI-written code.

It reads Python, JavaScript and TypeScript; other languages lower the confidence. Projects with large generated-looking data files (such as locale tables) can score high. It has not been tested on code written with light AI help or on AI code edited by people.

Use it to inform a conversation. It must not be the sole basis for accusing a student, a contractor or a job candidate.

Usage

pip install margin-cli margin-ai-code-detector
margin run ai-code-detector https://github.com/owner/repo --format markdown
margin run ai-code-detector ./path/to/project --format json --out report.json

For the web UI, install margin-api as well and run margin-api. The detector has its own results page there, with the evidence side by side and the flagged lines marked in the code. To use Anthropic or OpenAI instead of a local model, pick it on the Settings page, or pass --provider and --model to margin run.

Development

This agent lives in the margin monorepo, in agents/ai_code_detector. It is mirrored, read-only, to Waddah0-0/margin-ai-code-detector; open issues and pull requests in the main repository.

The evaluation harness lives in eval/: eval/run.py scores a dataset manifest, eval/fit.py fits and compares methodologies, eval/tools/compare_judges.py compares judge models, and eval/datasets/*/README.md describe each dataset and its biases.

Run its tests with uv run pytest agents/ai_code_detector.