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 autoresearchgit 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/autoresearch)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/autoresearch"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/autoresearch/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/autoresearch"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/autoresearch.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 | $0.00096 | $0.02187 |
| Opus 5.5 | $0.00038 | $0.00875 |
| Sonnet 5.5 | $0.00019 | $0.00437 |
| Haiku 4.5 | $0.00010 | $0.00219 |
Grade A, and why
autoresearch 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch Skill
Karpathy-style optimization loops for any conversion-focused content. No traffic needed. Simulated expert panel. Minutes, not weeks.
When to use this: Pre-launch content optimization. Generate 50+ variants, score with 5 simulated experts, evolve winners, output the best version + full experiment log.
When NOT to use this: Post-launch real-traffic A/B testing — that requires real analytics, not simulated scoring.
The sequence: Run autoresearch FIRST to hit 85+ simulated score. Then deploy. Then validate with real traffic.
What You'll Produce
Every run outputs 3 files:
| File | Purpose |
|---|---|
{name}-optimized.{ext} |
The winning optimized content |
data/{name}-experiments.json |
Full experiment log — all variants + all scores |
data/{name}-optimization-report.md |
Human-readable summary with winner rationale |
Expert Panel (5 Personas)
Score every variant against all 5. Batch all variants into a single API call per round.
| # | Persona | Scoring Lens |
|---|---|---|
| 1 | CMO at a mid-market B2B company (50M+ revenue) | "Would this make me stop and engage?" |
| 2 | Skeptical founder | "Do I believe this? Would I trust this company?" |
| 3 | Conversion rate optimizer | "Is this clear, specific, and action-driving?" |
| 4 | Senior copywriter | "Is this compelling, differentiated, and well-crafted?" |
| 5 | Your CEO/founder | "Direct, ROI-obsessed, no BS. Would I put this on my site?" |
Customization: Replace persona #5 with your own CEO/founder voice. Define their priorities and communication style in a
references/founder-voice.mdfile.
Each judge scores 0–100. Final score = average across all 5 judges.
Round Structure (Per Content Element)
Round 1:
→ Generate 10 variants of the element
→ Batch-score all 10 with the 5-expert panel (1 API call)
→ Rank by average score
→ Keep top 3
Round 2 (Evolution):
→ Analyze what the top 3 did right
→ Generate 10 new variants that push those winning patterns further
→ Batch-score all 10 (1 API call)
→ Keep top 3
Round 3 (If score < threshold):
→ Identify weakest scoring dimension
→ Generate 10 variants optimized for that dimension
→ Batch-score → keep top 1
Multi-element cross-breeding:
→ Take top 1 winner from each element
→ Generate 5 combinations that mix winning elements
→ Score holistically as complete units
→ Output the single best combination
What ships with it
3 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 · 260 lines · 96 tokens per session scan A 4c7732e3ad0f
autoresearch is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,784 stars, last pushed 5d ago), licensed MIT. It adds 96 tokens to every session and 2,187 once invoked, about $0.0004 per session on Opus 5.5. 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.
autoresearch compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| autoresearchericosiu/ai-marketing-skills | Skill | 3.8k | 5d ago | MIT | A | 96 | 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 |