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/sales-pipelineWrote 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/sales-pipeline)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/sales-pipeline"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/sales-pipeline/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/sales-pipeline"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/sales-pipeline.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 | $0.00893 |
| Opus 5 | $0.00000 | $0.00447 |
| Sonnet 5 | $0.00000 | $0.00179 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
sales-pipeline scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- No other external dependencies — scripts use stdlib HTTP server and urllib How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Sales Pipeline
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.
Complete AI-powered sales pipeline automation: website visitor identification → intent scoring → suppression → campaign routing → dead deal resurrection → trigger prospecting → self-learning ICP optimization.
When to Use
Use this skill when:
- Setting up automated outbound from website visitor identification (RB2B)
- Running suppression checks before cold outreach
- Routing leads to the right cold email campaigns
- Reviving closed-lost deals from HubSpot
- Finding companies showing buying signals (new hires, funding, job postings)
- Analyzing prospect approve/reject patterns to improve ICP targeting
Tools
RB2B Pipeline (visitor → outbound)
| Script | Purpose | Key Command |
|---|---|---|
rb2b_webhook_ingest.py |
Webhook server + intent scoring | python3 rb2b_webhook_ingest.py --serve --port 4100 |
rb2b_suppression_pipeline.py |
5-layer suppression checks | python3 rb2b_suppression_pipeline.py --email [email protected] |
rb2b_instantly_router.py |
Full pipeline: score → suppress → route → enroll | python3 rb2b_instantly_router.py --serve --port 4100 |
Deal Intelligence
| Script | Purpose | Key Command |
|---|---|---|
deal_resurrector.py |
3-layer dead deal revival (time decay + POC expansion + champion tracking) | python3 deal_resurrector.py --top 10 --dry-run |
trigger_prospector.py |
Web signal monitoring (new hires, funding, agency searches) | python3 trigger_prospector.py --days 7 --top 15 |
icp_learning_analyzer.py |
Learn from approve/reject decisions, recommend ICP changes | python3 icp_learning_analyzer.py |
What ships with it
11 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.
- .env.example 2.1 KB
- data/campaigns.json.example 143 B
- data/icp-config.example.json 234 B
- deal_resurrector.py 26 KB runs code
- icp_learning_analyzer.py 11 KB runs code
- rb2b_instantly_router.py 15 KB runs code
- rb2b_suppression_pipeline.py 13 KB runs code
- rb2b_webhook_ingest.py 15 KB runs code
- README.md 15 KB
- requirements.txt 232 B
- trigger_prospector.py 17 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.
- 13d ago First seen · 81 lines · 0 tokens per session scan A 064d539f9653
sales-pipeline 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 893 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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