openskycam: an MIT sky camera that Claude Code runs through MCP

On 26 October 2026 a developer posted openskycam to r/ClaudeAI as an open-source sky camera system that Claude Code can operate. According to the author, the system caught 17 meteors on Saturday night and had them sorted before they woke. The code is on GitHub under the MIT licence. MCP (Model Context Protocol) is the standard that lets an AI assistant call outside tools.

What it does and what it runs on

Per the README, an IP camera streams to a relay on a Linux host (a Raspberry Pi 4 or 5 is enough) that cuts it into one-minute MP4 files without re-encoding. The files stay on a disk you own, about 43 GB a day at 4 Mbps, with a 40-day default rolling window. Cloudflare Workers catalogue the recordings and serve the website and API, and a processor on your own machine runs YOLO detection models over frames and links detections into tracks, so moving planes can be told from fixed lights. The README says one camera stays inside or near Cloudflare's free tiers. You need your own domain and a Cloudflare account.

What Claude is reported to do

The author says Claude built the night workflow that now runs each night, reviews each detection and accepts or rejects it, and texts the good ones each morning. The author also says that when the model kept calling stars flying objects, Claude built a dataset from the approved detections and retrained it, cutting star false alarms from about half to 7% on nights it had not trained on while still catching 90% of meteors.

The repository's model card gives the measured version. On seven test nights, at a minimum score of 0.3, it reports plane recall of 0.955 (previous model 0.925), meteor recall of 0.904 (0.866) and stationary lights flagged at 0.074 (0.485). It also says the test labels are frames a person reviewed among the previous model's detections, so recall measures keeping what that model found, not finding what both miss. Training used 4,036 images from one 4K camera, all taken between 22:00 and 05:00 local time.

The part worth copying

The README lists the guardrails as server-enforced: no key can create keys, deletes show a preview and need confirmation, edits fail rather than overwrite a newer revision, and every tool call is logged. On connect the server also hands the agent its own guides and five skills, such as build-a-workflow and verify-changes. Architecture checks in make check fail when the agent puts code in the wrong layer. Three steps still need a person: the first Cloudflare login, creating the tunnel and sign-in application, and creating API keys.

Limits the sources state or leave out

• The model card says the detector is trained on one camera and one site, has not seen daylight, and can label a satellite as a plane.
• The weights are AGPL-3.0, not MIT, because they derive from Ultralytics YOLO26s; the card says using them in a service you offer to others carries AGPL obligations or needs an Ultralytics Enterprise licence.
• Neither source gives what the Claude usage cost, how often a human overrode its accept or reject calls, or an independent check of the 17-meteor count.
• I could not read the replies on the Reddit post, so I cannot report pushback from it.

What to do

• Before giving an agent an API, write down what it must never do (create credentials, delete without preview, overwrite newer work) and enforce it in the service, as this project says it does.
• If you reuse the detector, review its output on your own footage first and check the AGPL terms with whoever owns your licences.
• Ask of any agent pipeline that retrains a model: who reviews the labels it trains on, and on which nights was it tested.