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 matteotitta/genesys-skills --skill co-marketinggit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/co-marketing)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/co-marketing"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/co-marketing/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/matteotitta/genesys-skills/co-marketing"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/co-marketing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 178 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00101 | $0.01874 |
| Opus 5 | $0.00051 | $0.00937 |
| Sonnet 5 | $0.00020 | $0.00375 |
| Haiku 4.5 | $0.00010 | $0.00187 |
Grade A, and why
co-marketing 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/co-marketing — partner finding + joint campaign design
Find non-competing companies that share your audience and design joint campaigns. The leverage: when both sides promote, distribution roughly doubles for both — at half the asset cost.
When to invoke
- Distribution is organic-only and partner channel is untapped.
- A product launch needs amplified reach (pair with
/directory-submissions+/product-launch). - Genesys-internal cross-promotion with adjacent tools (Clay, Apollo, Smartlead, Instantly).
- Client needs joint plays with integration partners or vertical-adjacent SaaS.
Step 1 — Audience overlap analysis
The core test: does the partner serve our same buyer persona but solve a different problem? Same audience, non-competing solution.
Validate via:
- Account-overlap tools (Crossbeam, Reveal) — exact-match overlap data.
- LinkedIn job-title overlap (export both companies' followers, intersect).
- Newsletter sub-audience overlap (if both publish).
- Conference / event co-attendance.
Sharp rule: partners who share <30% audience are noise; >70% audience overlap with no competition is the gold zone.
Step 2 — 6-factor partner scoring
Score each candidate on 5 each:
| Factor | Question | Weight |
|---|---|---|
| Audience fit | Same ICP, non-competing? | High |
| Size | Audience reach roughly equivalent (within 3× either direction)? | High — asymmetric partnerships are short-lived |
| Brand alignment | Voice, professionalism, ethics compatible? | Medium |
| Engagement quality | Their audience actively responds (LinkedIn comments, email open rates) — not just numbers | High |
| Reciprocity history | Have they done co-marketing before? Reliable? | Medium |
| Execution ease | Geography, timezone, calendar friction | Medium |
Total ≥ 22/30 = green-light. 18–21 = yellow (start with low-effort format). <18 = pass.
Step 3 — Campaign type selection
Order by effort (low → high) and depth (light → heavy):
| Format | Effort | Depth | Best for |
|---|---|---|---|
| Social swap (mutual share / quote post) | Very low | Light | First partnership; brand-warm-up |
| Newsletter cross-promo (sponsored slot) | Low | Light | Audience introduction |
| Joint blog post / co-authored content | Medium | Medium | SEO + thought leadership |
| Joint webinar / panel | Medium-high | Medium | Lead capture + relationship |
| Integration play (real product integration) | High | Deep | Long-term partnership |
| Joint research report / data study | High | Deep | PR + earned media |
| Joint conference / community activation | Very high | Very deep | Established partnership |
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 · 190 lines · 101 tokens per session scan A 0c31607d44df
co-marketing is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 101 tokens to every session and 1,874 once invoked, about $0.0005 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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