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/shortform-productionWrote 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/shortform-production)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/shortform-production"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/shortform-production/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/shortform-production"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/shortform-production.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 | $0.00044 | $0.00564 |
| Opus 5 | $0.00022 | $0.00282 |
| Sonnet 5 | $0.00009 | $0.00113 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
shortform-production 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 — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shortform Production
Keep the visual style consistent. Change the storytelling to suit the source. An experiment is a hypothesis, not a proven retention gain.
Preamble
When available, use the repository's version check and telemetry initializer:
python3 telemetry/version_check.py 2>/dev/null || true
python3 telemetry/telemetry_init.py 2>/dev/null || true
Remote telemetry requires opt-in. Never log content, paths, account details, or credentials.
Choose the work
- Edit: Read V5 style and creative formats. Use the named source and timestamped transcript. Preserve credentials, claim qualifiers, natural speech, and the CTA. Inventory real assets before designing inserts. Use an available renderer; this package supplies editorial rules and delivery tools.
- Experiment: Produce three opening treatments for one clip. Choose one before rendering the full edit unless complete variants were requested. Do not automatically publish near-duplicates.
- Review: Read the bundled rubric, judge prompt, reference requirements, and scorecard schema. Apply review requirements. Report missing evidence; never invent a score.
- Caption or delivery: Read API delivery. Inspect the actual final video and CTA. Do not re-render an approved upload to match production defaults. Use the API for Metricool.
- Results: Read review and learning. Record missing metrics as null. One post cannot establish a winning style.
Installation does not authorize publishing, new accounts, automations, purchases, or public releases. Existing explicit task authorization is sufficient; do not ask twice.
Deliver
Include artifacts relevant to the requested mode: source hash and transcript; claims and asset provenance; hook hypothesis and shot list; versioned master and SRT; cover, title, caption and CTA; review evidence; verified delivery receipt; and supported performance observations.
What ships with it
16 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.
- .gitignore 63 B
- agents/openai.yaml 283 B
- profile.example.json 134 B
- README.md 1.5 KB
- references/creative-formats.md 3.0 KB
- references/eval/judge-prompt.md 2.1 KB
- references/eval/references.json 269 B
- references/eval/rubric.json 8.1 KB
- references/eval/scorecard.schema.json 3.2 KB
- references/metricool.md 3.8 KB
- references/next-batch.md 2.7 KB
- references/review-and-learning.md 3.4 KB
- references/v5-style.md 2.4 KB
- requirements.txt 18 B
- scripts/metricool.py 16 KB runs code
- tests/test_metricool.py 9.9 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.
- 5d ago First seen · 38 lines · 44 tokens per session scan A 8972cfb952b8
shortform-production is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 5d ago), licensed MIT. It adds 44 tokens to every session and 564 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.
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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
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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…