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 reymerekar7/rm-skills --skill video-performance-analyzergit clone --depth 1 https://github.com/reymerekar7/rm-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/reymerekar7/rm-skills/video-performance-analyzer)<a href="https://agentmods.dev/skills/reymerekar7/rm-skills/video-performance-analyzer"><img src="https://agentmods.dev/badge/skills/reymerekar7/rm-skills/video-performance-analyzer/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/reymerekar7/rm-skills/video-performance-analyzer"><img src="https://agentmods.dev/badge/skills/reymerekar7/rm-skills/video-performance-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Privilege Escalation · line 12 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00137 | $0.01891 |
| Opus 5.5 | $0.00055 | $0.00756 |
| Sonnet 5.5 | $0.00027 | $0.00378 |
| Haiku 4.5 | $0.00014 | $0.00189 |
Grade A, and why
video-performance-analyzer 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 2mo 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Performance Analyzer
What This Skill Does
Takes a short-form video (MP4 file or YouTube URL) and returns three things:
- Full transcript — every spoken word, on-screen text, audio cues, with timestamps
- Performance analysis — why it works, mapped against proven growth frameworks
- Repurposing playbook — concrete content ideas derived from the video
Setup
The analysis script uses Google's Gemini API (generative model for transcript + analysis).
API Key: Read from .env at repo root — GEMINI_API_KEY
Install dependency (one-time):
pip install google-genai --break-system-packages
Script location: <skill-directory>/scripts/analyze_video.py
Input Formats
| Format | How to pass it | Notes |
|---|---|---|
| Local MP4 file | File path as argument | Any size up to 2GB (free) / 20GB (paid) |
| YouTube URL | URL as argument | Public videos only |
| TikTok / Instagram | Download first, then pass MP4 path | Use browser extension or yt-dlp |
Running the Analysis
Quick run
python <skill-directory>/scripts/analyze_video.py /path/to/video.mp4
YouTube URL
python <skill-directory>/scripts/analyze_video.py "https://www.youtube.com/watch?v=VIDEO_ID"
With output saved to file
python <skill-directory>/scripts/analyze_video.py /path/to/video.mp4 --output /path/to/output.md
The script prints structured Markdown to stdout. Pipe or redirect as needed.
Analysis Workflow
Step 1: Run the script
Run analyze_video.py with the video path. The script will:
- Upload the video to Gemini Files API (or pass YouTube URL directly)
- Wait for processing (usually 10-30 seconds)
- Send the analysis prompt to
gemini-3-flash-preview - Return raw structured output
Step 2: Map against growth frameworks
After the script returns, interpret the output through these growth dimensions:
Hook — "The Hook Is a Contract"
- Did the hook make the viewer feel implicated, called out, or urgently curious?
- Does it pass any of the 5 hook techniques? (Contradiction, Specific number + unexpected context, Direct accusation, Stolen thought, Absurd reframe)
- Is it about the viewer's situation — or the creator's achievement?
- Framing trick to look for: deliberate word choice that raises perceived stakes
What ships with it
3 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.
- 2mo ago First seen · 214 lines · 137 tokens per session scan A 29af78728ce5
video-performance-analyzer is a skill published in the GitHub repository reymerekar7/rm-skills (39 stars, last pushed 3mo ago), licensed MIT. It adds 137 tokens to every session and 1,891 once invoked, about $0.0005 per session on Opus 5.5. 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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