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-algo-auditgit 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-algo-audit)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-algo-audit"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-algo-audit/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-algo-audit"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-algo-audit.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 | $0.00026 | $0.01260 |
| Opus 5 | $0.00013 | $0.00630 |
| Sonnet 5 | $0.00005 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
linkedin-algo-audit 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Algo Audit
Check LinkedIn posts and profile sections against 2026 algorithm data. Standalone quality gate — runs independently of voice, pillar, or client context. Returns a structured audit with pass/warn/fail scores and specific fixes.
Data sources: Shield Analytics (50K posts, Dec 2025), AuthoredUp (3M+ posts, Jan 2026), 360brew GPU-RAR framework, Propelgrowth blog, Scripe 2026 updates.
Claude Code Triggers
Invoke this skill when user says:
- "check this against the algo"
- "will this post perform?"
- "algo audit"
- "is my profile 360brew optimized?"
- "LinkedIn algorithm check"
- "optimize for the algorithm"
- "why is my content not getting reach?"
Do NOT invoke when:
- User wants voice review → use
voice-reviewer - User wants to write a post → use the appropriate post skill
- User wants overall content strategy → use
linkedin-content-guide
Inputs
| Input | Description | Source |
|---|---|---|
| Post text or profile section | The content to audit | User provides or from last assistant message |
| Audit type | Post audit, Profile audit, or Full audit | User specifies or infer from content |
Validation:
- Content is provided (post text or profile section)
- Audit type is determinable
Algorithm Foundation: GPU-RAR (2026)
Voice-locked framework — this is the spine of the audit logic. Stays in body.
LinkedIn replaced thousands of individual ranking models with a single AI model that reads content semantically — like a language model, not a keyword matcher.
GPU-RAR Framework (360brew):
- G — Generate embeddings from your profile text and post content
- P — Profile match between content topic and your stated expertise
- U — User interest matching (member embedding against topic clusters)
- R — Relevance scoring against the specific audience segment
- A — Amplification based on early engagement signals
- R — Redistribution to new segments if content holds up
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 · 142 lines · 96 tokens per session scan A 58a365ef5f07
linkedin-algo-audit is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,260 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.
Other skills, from other repositories
gingiris-b2b-growth
🇺🇸 B2B SaaS Growth — PLG vs SLG Playbook — Diagnose whether your problem is distribution, pricing, or PMF. PLG/SLG selection by ACV and sales cycle, the 5-stage path from $0 to $10M ARR, NRR discipline, affiliate & channel motion, enterprise tiering. Built from HeyGen, Deel, Vercel, Supabase, Snowflake patterns.…
gr-b2b-growth
A guide to growing a business-to-business software product from early user research to large-scale sales. B2B software is sold to companies rather than individual consumers.
go-to-market-playbook
A reusable Go-to-Market strategy template for both B2B and B2C launches. Covers positioning, messaging, ICP definition, channel selection, and competitive analysis frameworks. By @WeiYipei.
gingiris-go-global
🇺🇸 AI Product / SaaS Go-Global Complete SOP — From competitor research to launch to monetization. A full-cycle playbook covering Phase 0-5 (market validation, positioning, first 100 users, user interviews, beta-to-growth) plus open-source launch, Product Hunt, Reddit, SEO/GEO, conversion, and org principles.…
gr-competitor-research
Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI…
ai-launch-playbook
Launch your AI product to global attention — the playbook behind Manus, Devin, and AFFiNE's breakout launches. Covers AI-specific GTM strategy, hype cycle management, waitlist tactics, and multi-market rollout for maximum day-one impact.