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
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 ericosiu/ai-marketing-skills --skill agentic-video-understandinggit clone --depth 1 https://github.com/ericosiu/ai-marketing-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/ericosiu/ai-marketing-skills/agentic-video-understanding)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/agentic-video-understanding"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/agentic-video-understanding/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/agentic-video-understanding"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/agentic-video-understanding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00077 | $0.00963 |
| Opus 5 | $0.00039 | $0.00481 |
| Sonnet 5 | $0.00015 | $0.00193 |
| Haiku 4.5 | $0.00008 | $0.00096 |
Grade A, and why
Agentic video understanding 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 10d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic video understanding
Hireable understanding layer. The model takes a goal and decides what to watch, at what speed, and through which modality (frames, audio, transcript), fetching only the moments needed. Vendor claims: up to ~66% lower cost and ~88% fewer tokens vs static fixed-FPS ingest, with higher accuracy.
What this is / is not
Is: goal → watch only what you need → timestamps + quotes + confidence.
Is not: a video editor. Do not cut, overlay, caption-burn, render, schedule, post, email, or write CRM from this skill. Hand cuts to Overlap, FFmpeg, or net-new-video-editor. Approvals stay with the calling lane.
When to use
- Pre-call / sales-call mining: buyer objection, next step, competitive mention
- Shortform scoring: find a 3-second standalone hook and in/out points
- Longform / X research: named-person + contrast moments in podcast or YouTube tape
- Talent review: bar evidence in a Loom or trial recording
- Client audit: every mention of a keyword across a discovery recording
Skip when the job is already a clean transcript and you only need text search.
Inputs
| Field | Required | Notes |
|---|---|---|
source |
yes | URL or local media path the runtime can read |
goal |
yes | One sentence retrieval goal |
keywords |
no | Extra strings to bias retrieval |
max_moments |
no | Default 5 |
modality |
no | auto (default), frames, audio, or transcript |
Process
- Restate the goal as 1–3 retrieval queries. Done when each query is falsifiable (you would know if a moment matched).
- Call Gemini agentic video understanding (Gemini API or AI Studio) with
source, queries,max_moments, and modality preference. Prefer the agentic path over fixed-FPS full ingest when available. Done when the API returns candidate windows or an explicit empty set. - Normalize moments into the output schema below. Flag paraphrase vs verbatim. Drop fabricated timestamps. Done when every kept moment has
t_start,t_end,modality,quote,why,confidence. - Stop and hand off to the caller. Do not cut, overlay, schedule, publish, email, or CRM-write.
What ships with it
1 file 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.
- 10d ago First seen · 95 lines · 77 tokens per session scan A f6355a9919bd
Agentic video understanding is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 4d ago), licensed MIT. It adds 77 tokens to every session and 963 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 2026-09-02.
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