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/team-opsWrote 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/team-ops)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/team-ops"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/team-ops/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/team-ops"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/team-ops.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 | $0.00894 |
| Opus 5.5 | $0.00000 | $0.00358 |
| Sonnet 5.5 | $0.00000 | $0.00179 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
team-ops 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 2mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Team Ops
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 team performance analysis and meeting intelligence: ruthless performance audits using the "Elon Algorithm" + automatic extraction of action items, decisions, and follow-ups from meeting transcripts.
When to Use
Use this skill when:
- Evaluating team performance against OKRs/KPIs with a structured framework
- Stack ranking team members to identify A/B/C players
- Finding redundant roles, bottlenecks, and automation opportunities in your org
- Extracting action items and decisions from meeting transcripts
- Processing batch meeting notes into structured follow-up lists
- Pushing meeting action items to CRM (HubSpot) as tasks
Tools
Team Performance
| Script | Purpose | Key Command |
|---|---|---|
team_performance_audit.py |
Elon Algorithm: 5-step team audit + stack rank + scorecards | python3 team_performance_audit.py --input team_data.json --output report.md |
Meeting Intelligence
| Script | Purpose | Key Command |
|---|---|---|
meeting_action_extractor.py |
Extract decisions, actions, follow-ups from transcripts | python3 meeting_action_extractor.py --transcript meeting.txt --format markdown |
Configuration
All scripts use environment variables for LLM API access. Copy .env.example to .env and fill in your values.
Required Environment Variables
ANTHROPIC_API_KEY— Anthropic API key (Claude for analysis)OPENAI_API_KEY— OpenAI API key (alternative LLM provider)
Optional Environment Variables
What ships with it
4 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.
- 2mo ago First seen · 108 lines · 0 tokens per session scan A ec6776382e70
team-ops is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,784 stars, last pushed 5d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 894 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.
team-ops compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| team-opsericosiu/ai-marketing-skills | Skill | 3.8k | 5d ago | MIT | A | 0 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 2d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |