GitHub’s repository API can provide data for a custom trend tracker, but it does not make a star-sorted search equivalent to GitHub Trending. In an Oct. 5, 2026, account, developer Felixwang007 describes building a digest from timestamped star counts after a request to GitHub Trending timed out in one network test. The author says the approach surfaced four suspicious repositories whose READMEs allegedly directed readers to a shared PowerShell loader. Those are the author’s reported findings, not independently verified malware detections.
What “no GitHub Trending API” means
GitHub’s REST API documentation covers repository information and statistics, which can feed a custom tracker. A GitHub Community discussion says there are no official REST endpoints for the Trending or Explore pages; that is a community answer, not a formal product guarantee. The practical distinction is that you can collect repository data and build your own ranking, but should not describe a custom query as a reproduction of GitHub’s Trending algorithm.
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Community-maintained services also exist. For example, github-trending-api documents an unofficial API sourced from Trending data. It is not a GitHub-endorsed service, and its existence alone does not establish present availability or reliability.
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Felixwang007’s initial approach searched repositories created within a chosen date window and sorted the results by stars. That can highlight popular new repositories, but stars are cumulative: the ordering favors repositories that have already accumulated attention, rather than measuring how quickly attention is arriving. A creation-date filter also excludes older repositories that may be gaining momentum. Star totals alone say little about project quality.
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As the author puts it, “stars are the cheapest thing to fake.” That is a useful caution about treating a popularity signal as proof of genuine interest, not a measured claim that any particular star count is fraudulent.
Use snapshots to estimate star velocity
A tracker can estimate a change rate by recording each candidate repository’s star count with a timestamp, then comparing observations. For example, the change between two snapshots is the later star count minus the earlier count; dividing by the elapsed time gives an approximate rate. This is a locally computed measure, not a GitHub-provided Trending score.
- Keep the interval visible: A count difference over an hour is not comparable to the same difference over a day. Missing a snapshot makes the next estimate cover a longer, less precise interval.
- Keep the candidate set in mind: A tracker can only measure repositories it discovers and continues to observe. Search filters, collection cadence, API limits, and deleted or inaccessible repositories affect coverage.
- Do not equate change with endorsement: Stars are cumulative and can change for reasons other than sustained user interest. A velocity ranking is a lead for further inspection, not a quality or safety verdict.
GitHub’s REST documentation describes repository data and activity statistics that can support this kind of collection. Exact endpoint behavior and rate limits depend on the API and current GitHub policies; a custom collector should be designed against the current documentation rather than assuming a particular request budget.
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What the four-repository report says—and does not establish
In the Oct. 5, 2026, article, Felixwang007 says four of ten fast-rising repositories in a 72-hour new-repository window had one README file and no code. The article names BlackCrewmanFringe/AutoCad, Blockadezoshack/Microsoft-Project, AxeEradicate/Fps-Booster-for-Windows, and beachbladesmanloop/Hwid-Spoofer. It reports that the READMEs linked to ps-ps.cc/powershell/Loader.ps1 and urged readers to bypass PowerShell execution-policy protections.
The author reports that a HEAD request to the loader returned HTTP 200 and resolved to 196.251.107.72, while stating they did not fetch or execute the script. A successful response or IP resolution does not prove that a payload is malicious. The repositories, domain, and behavior described here were not independently inspected or validated, so the incident should be read as an attributed warning from the author’s account, not a confirmed malware finding.
The article also reports 1,349 stars across the four repositories in under 48 hours, and says one repository generated 100 WatchEvents in under five seconds, all in a single burst, alongside zero forks. Felixwang007 interprets the pattern as suggestive of farmed stars; those event counts do not prove who caused them or why. The four-of-ten figure is an observation from the author’s sample, not an estimate of how prevalent README-only malicious repositories are.
How to assess a README-only repository safely
A README can be a delivery mechanism even when a repository contains little or no source code: it can direct a visitor to download and run a remote script. Treat those directions as untrusted input. Do not run a command merely because a repository has many stars, and do not pipe a remote script directly into a shell.
- Inspect the repository without executing anything. Review the file tree, commit history, creation and push timing, and whether the stated project has inspectable code or artifacts.
- Read commands as code. Look for instructions to download remote files, launch scripts, disable antivirus, or bypass execution controls. Do not follow them on your everyday machine.
- Check links and reuse. Compare domains and download paths across suspicious repositories. Repeated infrastructure can be a useful connection between cases, but it is a signal to investigate, not proof of common ownership or maliciousness.
- Corroborate before concluding. Use reputable endpoint protection or, if qualified, an isolated analysis environment. A README, star burst, low file count, or clustered timestamps alone cannot establish that a project is malware.
Felixwang007 says the screening gate combined file inventory, stars relative to forks, event timing, README instructions, creation and push times, and shared download domains. The article says it would have rejected all four examples, but does not provide independent evaluation or false-positive and false-negative testing. Its control example, wy51ai/floorplan-3d, is described as a small legitimate project with a working HTML application and substantial forks; it was not independently inspected in the reporting summarized here. The example illustrates why a low file count is not a verdict.
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What broader security research can—and cannot—add
A 2024 arXiv preprint studied 35.2K GitHub repositories labeled as educational and reports that ChatGPT labeled 9,294 as malicious, with categories including DDoS, keylogger, and ransomware. Those are the paper’s dataset and model-labeling results, not a verified count of malicious repositories across GitHub. They do not establish how common README-only malware is or validate the four repositories in Felixwang007’s account.
Choosing a collection approach
| Approach | What it can do | Main limitation |
|---|---|---|
| Official repository API plus search | Collect documented repository data and build a custom candidate list or ranking. | A star-sorted search is not the Trending page’s ranking; cumulative stars are not velocity. |
| Scraping Trending and saving snapshots | Follow the visible page and retain observations over time. | Depends on page access and scraping behavior; availability and operational reliability are not established here. |
| Unofficial community wrapper | Use a third-party interface intended to expose Trending-sourced data. | Not an official GitHub service; ongoing availability and coverage are not guaranteed. |
Felixwang007 reports that GitHub Trending timed out and Trendshift returned an empty response during the same minute in one test. That is a single network observation, not evidence that either source is generally unavailable. The useful design choice is to make collection resilient enough for the intended use and retain timestamped observations if change over time matters.
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