media-judge@1.0.8
CAUTION· static onlyPublisher claims: "Judge a finished AI-made video, image or track by measuring it, then rank takes on the defects found. One command reads every frame and writes facts with timestamps and evidence: broken frames, banding after a simulated X or YouTube upload, numbers that jiggle, spelling, too much on screen, nothing leading the eye, motion with no cause, pinned graphics, sliding labels, a face, outfit or light that changed, sound after the cut, loudness, voice against the script, and distance to references against the template. A fresh agent settles only what eyes can settle. No score out of 10. Use when someone says "review this video", "check my render", "self-review", "QA", "judge", "critique", "score these takes", "pick the best take", "does it match the reference", "it looks like a template", "is it ready to post", "reverse engineer this reel", or after any agent finishes a film, short drama, reel or ad. Not for making or repairing media (varg-ai, varg-video-editing) or ad law (ai-ad-compliance-check)." Observed in sandbox: static analysis only — behavior not yet observed.
Coverage
Static only; nothing was run yet.The label rests on the code, the instruction files and the publisher. The honeypot run replaces this verdict when it completes.
Is [email protected] safe to install?
KillerSkills analysed it statically (a sandbox run is pending) on 2026-11-13 and scored it 61/100 (Caution). It is not known to be malicious, but it has behaviour or code a security policy should decide on before install. Notable findings: HTML comment carrying imperative instructions to the agent. Summary: static analysis only — behavior not yet observed.
Behavior trace
Queued for honeypot run · static analysis onlyThis version has not been detonated yet. The score above counts behavior as unknown (50/100), so it cannot read Verified until the honeypot run completes and replaces this verdict.
Static findings
Generated by .github/readme/build.ts from SKILL.md files and .github/readme/*.json. Edit those and run `bun .github/readme/build.ts`; CI fails when this file isFix: Keep SKILL.md to visible, descriptive text. Remove comments and encoded text that address the agent.proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.proc = subprocess.run(cmd, capture_output=True)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.raw = subprocess.run(["ffmpeg", "-v", "error", "-i", path, "-f", "f32le", "-ac", "1", "-ar", str(sr), "-"], capture_output=True).stdoutFix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.proc = subprocess.run(cmd, capture_output=True)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.p = subprocess.run(cmd, capture_output=True, text=True)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.return subprocess.run(cmd, check=check, capture_output=True, text=True, **kw)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.raw = subprocess.run(cmd, capture_output=True).stdoutFix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.data = subprocess.run(cmd, capture_output=True).stdoutFix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.proc = subprocess.run(["tesseract", "stdin", "stdout", "--psm", "11", "tsv"], input=buf.tobytes(), capture_output=True)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.p = subprocess.Popen(cmd, stdin=subprocess.PIPE)Fix: Build commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.Cost to the agent
Tokens estimated at four characters each. Skills load their instruction text when triggered; the figure is what that costs.
Known vulnerabilities
Vulnerability advisories (OSV) cover npm and PyPI packages; this registry has no advisory database.
What would make this Verified
19 points to 80 · weights: behavior 45%, static 25%, prompt 15%, publisher 15%- Unclaimed publisher≈ +8 on the totalClaim vargai: a verified publisher gets +50 on publisher trust. It is free and usually instant with GitHub sign-in.
- Hidden instructions≈ +6 on the totalRemove HTML comments, zero-width characters and white-on-white text that carry instructions. Everything the agent is meant to read should be visible to the person reading the file.
- Python subprocess execution detected≈ +2 on the totalBuild commands from fixed arguments, never from interpolated strings; prefer an API client over shelling out.