U UploadCheck Scan a GitHub Copilot export free →
UploadCheck for GitHub Copilot

Your GitHub Copilot export, checked before your audience checks it.

You ship demos with the code — screen recordings, feature walkthroughs, launch clips. But an OBS export or a Loom screencast hides defects your eye skims past: a frozen frame where the recorder stalled, dead air after a cut, audio drifting out of sync with the cursor, clipped mic peaks, an IDE panel cropped out of the safe area. UploadCheck runs 35 deterministic gates on the export before you publish and hands your Copilot agent an exact, timestamped fix list — so the demo lands as clean as the code.

0

broken videos published. Every GitHub Copilot export gets one final pass, so a frozen frame or dropped word never reaches your audience.

1 pass

instead of re-watching a 20-minute export. UploadCheck finds the one bad moment with its exact timestamp — no more scrubbing to hunt for it.

hours

saved off every delivery. No re-export → re-upload → "why is there a black frame at 0:14" from a client after it went live.

The workflow

UploadCheck is the last step after GitHub Copilot

It doesn't replace GitHub Copilot — it's the automated final QC pass on the finished export, the thing an editor can't reliably do by eye.

  1. 1
    Build and record with CopilotYou pair with Copilot in your IDE to build the feature, then capture the demo — an OBS/Loom/QuickTime screencast, a talking-head walkthrough, or a Copilot-narrated feature clip for a release or README.
  2. 2
    Export the demoRender the recording to an MP4/MOV (or the audio track for a voiceover). This is the file that would otherwise go straight to YouTube, a release page, or docs — untested.
  3. 3
    Run /check on the exportPoint UploadCheck at the file via the /check command over the API or MCP server — the same agentic surface Copilot already speaks. 35 deterministic gates run free: loop_freeze (frozen/stalled frames + loop seams), dead_air, loudness, clipping, black_frames, av_sync (cursor/audio desync), canvas_fill (IDE cropped out of frame), caption safe-area, text_contrast, format_spec. Optional AI oracle gates add garble (garbled TTS/mic), narration_match, and omni_watch/gemini_watch multimodal 'looks-wrong' watchers.
  4. 4
    Fix and re-checkUploadCheck returns a machine-readable report — each failure with a timestamp and the fix. Hand it back to your Copilot agent to trim the dead air, re-align audio, re-crop the frame, or re-export, then /check again until it's green. Deterministic gates are free; you only pay for AI oracle passes.
Three ways to check

However you work with GitHub Copilot, there's a way in

Same engine every way. The free scan runs 35 automated checks; a paid plan adds the AI gates (garbled speech, on-screen continuity, narration match) and lifts the hourly limit.

1

Through your AI

Use Claude Code, Codex, Cursor, or any LLM for production? Paste our one-time setup prompt and it wires a /check command. Then just say "check my GitHub Copilot export" — the AI runs UploadCheck and reads back the findings. Free needs no key.

Get the setup prompt →
2

Paste a link or drop the file

Export your video or audio from GitHub Copilot, then drop the file into the free scan. No account, no tools — a real PASS / BLOCK verdict with timestamps in seconds.

Start free →
3

Upload in your dashboard

On a paid plan, upload the GitHub Copilot export straight into your UploadCheck dashboard — same engine as the free scan, with every AI gate on and no hourly throttle. Paste a link or choose a file.

See paid plans →
FAQ

UploadCheck & GitHub Copilot

How does UploadCheck work with GitHub Copilot?

You produce your export in GitHub Copilot as usual, then run the finished file through UploadCheck — from the web, the CLI, or your AI assistant. UploadCheck scans it for problems GitHub Copilot can't check, returns timestamped flags and fixes, and you re-check until it passes.

What problems does UploadCheck catch on a GitHub Copilot export?

Frozen or black frames, looped/reused footage, dead air and audio dropouts, garbled or unintelligible speech, captions outside the platform-safe area, low-contrast text, loudness off the platform target, and wrong resolution/codec/frame-rate — each with the exact timestamp and a fix.

Do I need to change my GitHub Copilot workflow?

No. UploadCheck is the last step after your GitHub Copilot export — one check before you publish. It doesn't touch your GitHub Copilot project; it inspects the finished file and hands you (or your AI) a repair list.

Can I run UploadCheck from GitHub Copilot?

Does UploadCheck integrate with my Copilot agent workflow? Yes — /check runs over the same API and MCP surface your Copilot agent already speaks, so it drops into an agentic pipeline without leaving your tooling. The 35 deterministic gates run free and return a timestamped, machine-readable fix list your agent can act on directly; the 6 AI oracle gates (garble, twins, narration_match, cheap_broll, omni_watch, gemini_watch) are the only paid passes.

How much time does it save?

Instead of re-watching a clip to hunt for the one bad moment — or worse, finding it after you've published — UploadCheck flags it in one pass with the exact timestamp. Catching one bad export before publish saves the re-generate/re-export, re-upload, and the reputational hit of a broken clip going live.

Never publish a broken GitHub Copilot export again.

Scan your export free

Free with your email — 35 gates, unlimited. Add the AI gates when you're ready, from $0.025/min.