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 video-analysisgit 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/video-analysis)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/video-analysis"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/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/ericosiu/ai-marketing-skills/video-analysis"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/video-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00045 | $0.00977 |
| Opus 5 | $0.00023 | $0.00489 |
| Sonnet 5 | $0.00009 | $0.00195 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
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 5d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Analysis
Answer the user's question without making them watch or listen. Read transcripts for spoken content; inspect footage when the answer depends on visuals, sound, or timing.
When running inside this repository, use its available version check and telemetry helpers as described in README.md. A standalone installation works without them.
1. Select the evidence
| Request | Starting evidence | When to inspect footage |
|---|---|---|
| Summary, research, argument review, repurposing | Timestamped transcript | Missing context, ambiguous references, or essential on-screen information |
| Quote extraction | Transcript | Uncertain wording or attribution; verify audio before calling it verbatim |
| Demo or tutorial review | Transcript plus relevant video sections | Check what the interface actually shows against the narration |
| Editing, delivery, visual pacing | Video and audio | Inspect the requested range; text cannot establish performance or cut quality |
| Clip selection | Transcript to shortlist moments | Verify start/end speech, pauses, transitions, and essential visuals |
| Explicit full-video analysis | Entire requested video | Honor the requested coverage; a transcript is not a replacement |
Use the exact URL or file supplied. For “latest,” verify the named channel, upload date, and requested format from its current listings or metadata. Distinguish Videos, Shorts, and Live. Do not use search ranking as proof of recency.
2. Acquire the transcript silently
Prefer a supplied transcript, existing captions, or a configured transcript connector. YouTubeToTranscript is an optional extraction service, not a required dependency. Check its current access terms and API documentation before automating it; do not assume its free website implies free API access.
Retain timestamps, language, source URL, and whether captions are automatic or human-edited. Preserve gaps and uncertain words. If translations were used, label them. Keep transcription corrections separate from verbatim quotations.
What ships with it
4 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.
- 5d ago First seen · 58 lines · 45 tokens per session scan A 1913f31511df
video-analysis is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 5d ago), licensed MIT. It adds 45 tokens to every session and 977 once invoked, about $0.0002 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 2026-09-07.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…