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 40+ quality 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. 40+ quality 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. Quality 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 40+ quality 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 — 40+ gates, 200 checked minutes a month. Add the AI gates when you're ready, from $99/mo.