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 linkedin-weekly-contentgit 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/linkedin-weekly-content)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-weekly-content"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-weekly-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/matteotitta/genesys-skills/linkedin-weekly-content"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-weekly-content.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 | $0.00032 | $0.01251 |
| Opus 5 | $0.00016 | $0.00626 |
| Sonnet 5 | $0.00006 | $0.00250 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
linkedin-weekly-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 9d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Weekly Content
Generate a full week of LinkedIn content for Matteo Tittarelli / Genesys Growth. Produces 4 posts + 2 visual briefs, appends to a persistent Google Doc, and sends a Slack notification.
Weekly Schedule
Voice-locked operational constants — these stay in body.
- Monday: Claude Skills newsletter (separate pipeline — no LinkedIn post)
- Tuesday: Story post (35% pillar — personal experiences, founder journey)
- Wednesday: Expert post (40% pillar — give away the playbook)
- Thursday: Sales post (25% pillar — case study storytelling, offer integration)
- Friday: Rotated post (cycles: Story → Expert → Sales, ISO week mod 3)
- Sunday: GTM Pulse newsletter (separate pipeline)
Visual briefs (one of each per week):
- 1x Infographic brief (paired with any post)
- 1x Carousel brief (paired with any post)
Process
9-phase orchestration:
Phase 1: Load Context → Phase 2: Generate Hooks → Phase 3: Generate 4 Posts
↓
Phase 6: Photo Rec ← Phase 5: Visual Briefs ← Phase 4: Algo Audit
↓
Phase 7: GDrive Append → Phase 8: Slack Notify → Phase 9: Update Rotation Tracker
Phase-by-phase detail (skill invocations, post lengths, archetype rotation logic, MCP detection, GDrive append script) in the premium reference.
Quality Gates (Applied Automatically)
Voice-locked rules — these stay in body.
- Anti-AI detection: No "Here's the thing:", no false contrast reframes, no wrapped-bow endings, no generic praise
- 100 Posts Test: Each post must feel authentic for 100 consecutive posts
- Offer integration: Even non-sales posts subtly showcase what Genesys does (per Matteo's voice rules)
- Wordiness check: Trim 15-20% from first draft (Matteo's tendency)
- No "genuinely asking": Stop using pseudo-engagement closings
- Source integrity: No fabricated stories, metrics, or quotes — all from content banks
Full per-post and per-batch checks in the premium reference.
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.
- 9d ago First seen · 129 lines · 132 tokens per session scan A ad82606febe9
linkedin-weekly-content is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 1,251 once invoked, about $0.0002 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 2026-09-03.
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