AI Marketing Skills is a collection of open-source workflows that help AI coding agents handle marketing and sales work, including growth experiments, pipeline management, content operations, outbound outreach, SEO, and finance analysis. It is intended for marketing and sales teams that want reusable agent-driven processes. The catalogue entries package these workflows as skills for compatible coding agents.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ericosiu/ai-marketing-skillsnpx agentmods add skills/ericosiu/ai-marketing-skills/video-clip-pipelineWrote 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/ericosiu/ai-marketing-skills/video-clip-pipeline)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/video-clip-pipeline"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/video-clip-pipeline/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/ericosiu/ai-marketing-skills/video-clip-pipeline"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/video-clip-pipeline.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 Output Handling · line 126 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00000 | $0.01539 |
| Opus 5 | $0.00000 | $0.00770 |
| Sonnet 5 | $0.00000 | $0.00308 |
| Haiku 4.5 | $0.00000 | $0.00154 |
Grade A, and why
video-clip-pipeline 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 13d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Form Video Clip Pipeline
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true
# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to
~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. Seetelemetry/README.md.
AI-powered pipeline that converts long-form YouTube episodes into standalone highlight clips. Download → Transcribe → AI Segment → Cut → Upload. A 60-minute episode becomes 3–5 clips in ~15 minutes.
When to Use
Use this skill when:
- Converting long-form YouTube content (podcasts, interviews, talks) into highlight clips
- Processing a YouTube back catalog into a clips channel
- Finding the best standalone segments from video transcripts
- Cutting video clips with verified sentence boundaries
- Running a high-volume clip publishing operation ($0.50–1.00 per episode)
Prerequisites
System Tools
brew install yt-dlp ffmpeg # macOS
# Or: apt install ffmpeg && pip install yt-dlp # Linux
pip install openai-whisper
Environment Variables
ANTHROPIC_API_KEY— Claude API key (required for segmentation)- YouTube Data API credentials (optional, for automated upload)
Tools
End-to-End Pipeline
| Script | Purpose | Key Command |
|---|---|---|
longform_pipeline.py |
Full pipeline: download → transcribe → segment → verify → cut | python3 longform_pipeline.py --url URL --max-clips 3 |
scored_pipeline.py |
Pipeline with 10-expert LLM quality scoring (only cuts 90+ clips) | python3 scored_pipeline.py --url URL --min-score 90 |
Individual Steps
| Script | Purpose | Key Command |
|---|---|---|
clip_segmenter.py |
Find clip-worthy segments from Whisper transcripts | python3 clip_segmenter.py --transcript file.json --output segments.json |
clip_cutter.py |
Cut clips from segment metadata using FFmpeg | python3 clip_cutter.py --source video.mp4 --segments segments.json --output-dir clips/ |
What ships with it
7 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.
- 13d ago First seen · 188 lines · 0 tokens per session scan A c98762a223e4
video-clip-pipeline is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,539 tokens. 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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