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-sales-postsgit 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-sales-posts)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-sales-posts"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-sales-posts/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-sales-posts"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-sales-posts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.02564 |
| Opus 5 | $0.00012 | $0.01282 |
| Sonnet 5 | $0.00005 | $0.00513 |
| Haiku 4.5 | $0.00002 | $0.00256 |
Grade A, and why
linkedin-sales-posts 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Sales Posts (Converting)
Generate converting LinkedIn posts that sell your offer through case study storytelling — without reading like an ad. Uses Nick Broekema's 12-section converting post template to weave proof, objection handling, qualification, and scarcity into a compelling narrative.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with output-tenets.md, output-simplicity.md, ai-speak-anti-patterns.md, marketing-psychology.md. Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]].
Refinements applied: R1 (no source tags in post body), R3 (proof framing capability-led, not "thrilled"), R6 (offer woven as soft CTA — DM / sign-up primary, never engagement-farming closer), R9 (12-section template verb-led).
Produces:
- Case study extraction — before/transformation/after elements from a real client result
- 12-section converting post — ready to copy/paste, offer woven in as "easter egg"
- Visual element recommendation — what screenshot/image to pair with the post
Differs from sibling skills:
linkedin-content— general founder content (educational, thought leadership, personal)linkedin-content-guide— builds ICP + offer foundation (upstream dependency)linkedin-hooks— hook formulas library (referenced for section 1)- linkedin-sales-posts — specifically generates CONVERTING posts that sell through case study storytelling
Source: Nick Broekema (Content Design) — "Converting Post Breakdown" framework + 6-post pattern analysis.
When to run
Invoke when user says:
- "Write a converting post about [client/result]" → Archetype 1 (case study)
- "Sales post for my offer" → Ask which archetype
- "Turn this case study into a LinkedIn post" → Archetype 1
- "Sell my [offer] on LinkedIn" → Ask which archetype
- "Write an offer post for [case study]" → Archetype 1
- "LinkedIn post to drive DMs" → Ask which archetype
- "Problem callout post" or "diagnose my ICP's problem" → Archetype 2 (problem diagnosis)
- "Origin story for my offer" or "how I created my offer" → Archetype 3 (origin story)
- "Post from a DM I received" or "quote hook post" → Archetype 4 (quote hook)
- "Handle objections in a post" or "niche proof post" → Archetype 5 (objection-led)
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 · 193 lines · 112 tokens per session scan A 4d9b6b88338d
linkedin-sales-posts is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 2,564 once invoked, about $0.0001 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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