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/revenue-intelligenceWrote 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/revenue-intelligence)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/revenue-intelligence"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/revenue-intelligence/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/revenue-intelligence"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/revenue-intelligence.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 | $0.00000 | $0.01846 |
| Opus 5 | $0.00000 | $0.00923 |
| Sonnet 5 | $0.00000 | $0.00369 |
| Haiku 4.5 | $0.00000 | $0.00185 |
Grade A, and why
revenue-intelligence 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Revenue Intelligence
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true
# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to
~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. Seetelemetry/README.md.
AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting.
When to Use
- User wants to extract insights from Gong sales call transcripts
- User needs to identify objections, buying signals, or competitive mentions in calls
- User wants to prove content ROI by mapping content to closed deals
- User needs revenue attribution across first-touch and multi-touch models
- User wants to generate a unified client report from GA4 + HubSpot + Ahrefs + Gong
- User asks about content gaps in the buyer journey
- User needs anomaly detection across marketing metrics
Tools
Gong-to-Insight Pipeline (gong_insight_pipeline.py)
Extracts structured intelligence from sales call transcripts. Works with Gong API or plain transcript files.
# Analyze a single transcript file
python gong_insight_pipeline.py --file transcript.txt
# Analyze multiple transcript files
python gong_insight_pipeline.py --dir ./transcripts/
# Pull recent calls from Gong API (last 7 days)
python gong_insight_pipeline.py --gong --days 7
# Pull specific call by ID
python gong_insight_pipeline.py --gong --call-id abc123
# Output as JSON file
python gong_insight_pipeline.py --file transcript.txt --output insights.json
# Generate content topics from recurring objections
python gong_insight_pipeline.py --dir ./transcripts/ --content-topics
# Generate follow-up suggestions for outbound sequences
python gong_insight_pipeline.py --file transcript.txt --follow-ups
What ships with it
5 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.
- 12d ago First seen · 214 lines · 0 tokens per session scan A 6d8fed3e2c71
revenue-intelligence is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,846 tokens. 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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