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-content-engineWrote 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-content-engine)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/video-content-engine"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/video-content-engine/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-content-engine"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/video-content-engine.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 | $0.00086 | $0.01746 |
| Opus 5 | $0.00043 | $0.00873 |
| Sonnet 5 | $0.00017 | $0.00349 |
| Haiku 4.5 | $0.00009 | $0.00175 |
Grade A, and why
video-content-engine 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Content Engine
Turn one grounded source into the strongest justified portfolio. Do not manufacture a fixed number of derivatives. Find distinct promises, select formats that can pay them off, and give every asset a portfolio job.
Preamble
Run the repository's privacy-preserving version check and telemetry initializer when available:
python3 telemetry/version_check.py 2>/dev/null || true
python3 telemetry/telemetry_init.py 2>/dev/null || true
Remote telemetry is opt-in. Never log content, URLs, paths, credentials, names, or business data.
State the operating contract
Before work, state the exact source, owner, channel, destination, requested modules, delivery format, this turn's artifact, stop condition, and known blocker class.
Preserve the source read-only. Never claim a render, upload, preview, publication, or QC pass without fresh evidence. Treat performance targets as experiments, not forecasts.
Accept any grounded source
Accept a public or authorized URL, uploaded file, local file, transcript, audio recording, or source folder. A bare request such as run this skill on this video defaults to diagnose and recommend, not automatic production.
For a link:
- Resolve the exact page, owner, channel, title, duration, and accessible media or transcript.
- Prefer the original file, then an authorized downloadable master, then the published stream.
- Never substitute a mirror, alternate upload, account, episode, or transcript.
- If authentication, permissions, DRM, missing media, or an unavailable transcript prevents grounded analysis, request an accessible source. Do not make editorial recommendations from metadata alone.
Unless production is requested, return a brief with the source score, recommended editorial and operating modes, opportunity counts, first release wave, repairs and dependencies, production complexity, portfolio jobs, and approval required to begin.
Inventory the source
- Record path or URL, size, duration, streams, and SHA-256 when available.
- Produce a speaker-aware timestamped transcript. Correct names, products, and numbers against the source.
- Build:
- a claim ledger separating results, estimates, anecdotes, forecasts, and targets;
- a rights ledger for third-party media, logos, consent, embargoes, and sponsors;
- a content-atom inventory with timestamps, viewer, promise, proof, tension, framework, payoff, visuals, caveats, and dependencies.
- Read references/opportunity-routing.md, score viable atoms, and record selected and rejected opportunities.
What ships with it
10 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.
- agents/openai.yaml 305 B
- README.md 1.5 KB
- references/delivery-contract.md 1.3 KB
- references/format-modes.md 1.5 KB
- references/opportunity-routing.md 1.1 KB
- references/quality-gates.md 2.1 KB
- requirements.txt 63 B
- scripts/audit_edit_boundaries.py 2.9 KB runs code
- scripts/validate_delivery.py 3.7 KB runs code
- tests/test_scripts.py 2.3 KB runs code
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 · 140 lines · 86 tokens per session scan A ce9cf8a982a0
video-content-engine is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,517 stars, last pushed 3d ago), licensed MIT. It adds 86 tokens to every session and 1,746 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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