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-content-guidegit 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-content-guide)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-content-guide"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-content-guide/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-content-guide"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-content-guide.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 | $0.00023 | $0.01796 |
| Opus 5 | $0.00012 | $0.00898 |
| Sonnet 5 | $0.00005 | $0.00359 |
| Haiku 4.5 | $0.00002 | $0.00180 |
Grade A, and why
linkedin-content-guide 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 Content Guide
Builds a practitioner-first LinkedIn ICP and distills it to ONE focused offer — using Nick Broekema's framework adapted for AI-assisted research. Produces a 14-question ICP, a Pains→Goals content guide, an offer statement, and a SCART brief with 9 post ideas.
How it differs from /icp-research: that skill does market-level ICP analysis for B2B SaaS products. This skill builds a personal ICP for LinkedIn positioning — starting from your best collaborations and practitioner experience, supplemented by website research.
Doctrine inherited (Step 7 — 0626 rollout, locked 2026-06-04)
Output complies with output-tenets.md, output-simplicity.md, ai-speak-anti-patterns.md. Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]].
Refinements applied: R1 (guide doc is internal-reference for other LinkedIn skills — inline cites stay), R3 (offer + post-idea framing operator-direct), R5 (long-form essay anchor becomes voice anchor across the 9 post ideas), R9 (verb-led section headings).
When to run
- "Build my LinkedIn ICP" or "LinkedIn ICP and offer for [company/person]"
- "Who should I target on LinkedIn?" / "Create my LinkedIn offer proposition"
- "Optimize my offer for LinkedIn" / "Help me define my ideal client for LinkedIn content"
- "ICP + offer for [URL]"
Do NOT run when: user wants market-level ICP (/icp-research), behavioural buyer simulation (/icp-behavioural), to write a single post (/linkedin-content), profile optimization (/linkedin-profile-optimization), or competitive research (/competitor-research).
See the premium reference for full upstream/downstream wiring.
Inputs
Required
| Input | Description | Source |
|---|---|---|
| Website URL | Company or personal website to research | User provides |
Optional (significantly improves quality)
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 00e471421fff
linkedin-content-guide is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 1,796 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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