Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add maystudios/claude-skills --skill meta-muse-video-analysisgit clone --depth 1 https://github.com/maystudios/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maystudios/claude-skills/meta-muse-video-analysis)<a href="https://agentmods.dev/skills/maystudios/claude-skills/meta-muse-video-analysis"><img src="https://agentmods.dev/badge/skills/maystudios/claude-skills/meta-muse-video-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maystudios/claude-skills/meta-muse-video-analysis"><img src="https://agentmods.dev/badge/skills/maystudios/claude-skills/meta-muse-video-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 32 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.00653 |
| Opus 5 | $0.00036 | $0.00327 |
| Sonnet 5 | $0.00014 | $0.00131 |
| Haiku 4.5 | $0.00007 | $0.00065 |
Grade A, and why
meta-muse-video-analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta Muse Video Analysis
Analyze one local video with scripts/analyze_video.py. Keep the model fixed; never add or substitute a model selector.
Workflow
- Resolve the user's video to an absolute local path.
- Preserve the user's analysis prompt verbatim unless they ask for prompt improvement.
- Confirm that
META_MUSE_KEYexists without printing its value. - Run:
python "<skill-directory>\scripts\analyze_video.py" "C:\absolute\path\video.mp4" --prompt "Describe the requested analysis"
For long or multiline prompts, write the prompt to a temporary UTF-8 text file and use --prompt-file. Use --output when the user requests a saved result:
python "<skill-directory>\scripts\analyze_video.py" "C:\absolute\path\video.mp4" --prompt-file "C:\absolute\path\prompt.txt" --output "C:\absolute\path\analysis.md"
- Return the analysis or link the saved output. Surface API errors exactly enough to diagnose access, quota, format, or region issues, but never expose the API key.
Operational rules
- Read the API key only from
META_MUSE_KEY. - Use only
muse-spark-1.2-contributorthroughhttps://api.meta.ai/v1. - In Codex, run the script with network approval. The key may be visible only in the approved outside-sandbox process; never copy it into command arguments or output.
- Check the model catalog before upload. If Contributor access is absent, stop without uploading and never fall back to standard
muse-spark-1.2. - Upload the video through the Files API and attach it to a Responses API request.
- Delete the remote file after the response, including after failures. Use
--keep-uploadonly when the user explicitly asks to retain it. - Do not silently preprocess, transcode, shorten, or split the video. If Meta rejects its format or size, report that limitation and ask before transforming the source.
- Treat Contributor uploads as externally shared data. Warn before sending secrets, private customer material, unreleased footage, or other sensitive content unless the user has already confirmed that the Contributor data terms are acceptable.
- Do not print, log, persist, or copy the API key.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 50 lines · 71 tokens per session scan A f93501a925f0
meta-muse-video-analysis is a skill published in the GitHub repository maystudios/claude-skills (22 stars, last pushed 26d ago), licensed MIT. It adds 71 tokens to every session and 653 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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