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 kostja94/marketing-skills --skill employee-generated-contentgit clone --depth 1 https://github.com/kostja94/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/kostja94/marketing-skills/employee-generated-content)<a href="https://agentmods.dev/skills/kostja94/marketing-skills/employee-generated-content"><img src="https://agentmods.dev/badge/skills/kostja94/marketing-skills/employee-generated-content/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/kostja94/marketing-skills/employee-generated-content"><img src="https://agentmods.dev/badge/skills/kostja94/marketing-skills/employee-generated-content.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00084 | $0.01040 |
| Opus 5 | $0.00042 | $0.00520 |
| Sonnet 5 | $0.00017 | $0.00208 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
employee-generated-content 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- employee-generated-content — 100% identical, 0 lines differ
- employee-generated-content — 100% identical, 0 lines differ
- employee-generated-content — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Channels: EGC (Employee-Generated Content)
Guides EGC and employee advocacy strategy for AI/SaaS products. EGC is content created by employees (social posts, videos, blogs, testimonials) that reflects authentic workplace and product insights. Employee-shared content generates ~8x more engagement than brand posts; LinkedIn employee posts reach ~561% more than brand content.
When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and brand voice.
Identify:
- Goal: Brand trust, thought leadership, recruitment, or conversion
- Platform: LinkedIn (B2B primary), X, Instagram, TikTok
- Employee base: Size, roles, existing social presence
EGC vs. UGC vs. Creator Program
| Dimension | EGC | UGC | Creator Program |
|---|---|---|---|
| Source | Employees | Customers | External creators |
| Trust | Company experts (66% vs 47% for ads) | Peer reviews | Influencer reach |
| Cost | Low; leverage workforce | Incentives, curation | Credits, payment |
| Best for | B2B, SaaS, professional services | Social proof, reviews | Content scale, tutorials |
Why EGC Works
- Algorithm favor: Social platforms prioritize personal accounts over brand pages
- Authenticity: 92% trust recommendations from individuals over branded content; 81% need to trust before buying
- B2B fit: LinkedIn is primary; employees share industry expertise and product insights
- Results: 27% engagement increase, 19% sales increase in first year; 24% higher conversion vs traditional content
Content Formats
| Format | Use | Platform |
|---|---|---|
| Day-in-the-life | Culture, behind-the-scenes | LinkedIn, TikTok, Instagram |
| Industry insights | Thought leadership, expertise | |
| Short-form video | Quick tips, demos | TikTok, LinkedIn, Instagram |
| Testimonials | Product experience | Website, case studies |
| Serialized content | Consistent presence | Personal + brand accounts |
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 · 84 lines · 84 tokens per session scan A daea105f4ca4
employee-generated-content is a skill published in the GitHub repository kostja94/marketing-skills (967 stars, last pushed 3mo ago), licensed MIT. It adds 84 tokens to every session and 1,040 once invoked, about $0.0004 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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