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 closed-loop-analytics-upgradegit 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/closed-loop-analytics-upgrade)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/closed-loop-analytics-upgrade"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/closed-loop-analytics-upgrade/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/closed-loop-analytics-upgrade"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/closed-loop-analytics-upgrade.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 | $0.00060 | $0.00987 |
| Opus 5 | $0.00030 | $0.00494 |
| Sonnet 5 | $0.00012 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00099 |
Grade A, and why
closed-loop-analytics-upgrade 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 12d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Closed-Loop Analytics Upgrade
Principle
A workflow is not a closed loop until it checks whether the change worked and updates the playbook from that evidence.
For marketing skills, that means pulling analytics after the change window. Manual opinions are useful. Platform truth wins.
Core pattern
- Input: usage logs, recommendations, shipped changes, platform analytics, owner feedback, and cost/runtime data.
- AI action: compare baseline vs candidate, find repeatable signals, and propose a skill/playbook patch.
- Output: candidate patch to a prompt, skill, connector, brief template, scoring rubric, or next-action rule.
- Judgment: success rate, speed, cost, quality, human correction rate, and actual performance delta.
- Self-improvement: promote only if it beats baseline. Otherwise keep testing, rollback, or mark unproven.
Analytics by surface
X/Twitter
Track:
- impressions
- engagement rate
- replies
- reposts
- bookmarks
- profile clicks
- follower delta
- post length
- hook style
- proof number
- CTA type
- topic bucket
Use for:
- title/hook formulas
- longform structure
- CTA patterns
- post timing
- topic scoring
YouTube
Track:
- impressions
- CTR
- average view duration
- retention curve
- watch time
- subscribers gained
- comments
- traffic source
- title/thumbnail/hook metadata
- video length and topic bucket
Use for:
- title formulas
- thumbnail rules
- first-15-second hook
- retention beats
- chapter structure
- Shorts cutdowns
- repurposing guidance
SEO/AEO/GEO
Track:
- GSC clicks, impressions, CTR, average position, query/page mix
- GA4 sessions, engaged sessions, conversions, assisted leads
- Ahrefs rankings, backlinks, traffic estimates, keyword movement
- ClickFlow opportunities
- AI-search / answer-engine visibility where available
- CMS/page change log
Use for:
- content refresh patterns
- AEO/GEO opportunity scoring
- query/page prioritization
- internal linking and schema recommendations
- rollback decisions
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.
- 12d ago First seen · 170 lines · 60 tokens per session scan A a3c096bdf46f
closed-loop-analytics-upgrade is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 4d ago), licensed MIT. It adds 60 tokens to every session and 987 once invoked, about $0.0003 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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