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-engagementgit 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-engagement)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-engagement"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-engagement/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-engagement"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-engagement.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 | $0.00032 | $0.02672 |
| Opus 5 | $0.00016 | $0.01336 |
| Sonnet 5 | $0.00006 | $0.00534 |
| Haiku 4.5 | $0.00003 | $0.00267 |
Grade A, and why
linkedin-engagement-prospects 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn engagement → prospects
Turn LinkedIn post engagement into an enriched prospect list. Input a post URL (or several), pull engagers via Apify, dedupe across posts, run /deepline-enrich for emails, and output a CSV with engagement context ready for outbound.
Adopted from: Extruct GTM skills (via /steal 2026-04-21). Real trigger: posts that go well attract the right ICP, and the engagement itself is a signal that the person is at least category-aware.
Doctrine inherited (Step 7 — 0626 rollout)
Output complies with:
output-tenets.md— the seven tenetsoutput-simplicity.md— length caps, three-layer source placement, robot-tells banoutbound-research-hygiene.md— dated signals (engagement date must be ≤90 days), no stale references- Step 6 calibration: see [[feedback_execution_doctrine_refinements_step6]]
Refinements applied to this skill:
| Code | Refinement | How it lands in linkedin-engagement-prospects |
|---|---|---|
| R1 | Source placement (three layers) | Engagement CSV is internal-reference (input to outbound). Inline metadata (post URL, engagement type, date) stays — the next skill (/outreach-emails) reads it. |
| R3 | Product-update tone | When the downstream message references our content, frame as "I posted about X" not "we are thrilled to share." |
| R6 | CTA hierarchy | DM follow-ups to engagers default to discovery-call or trial primary — never blog as primary. Engagement already showed they saw our content. |
| R9 | Action-oriented section names | "Pull the engagers / Dedupe across posts / Enrich for email / Hand off to outbound" — verb-led. |
Core philosophy — voice-locked
A like on your LinkedIn post is not the same as a marketing-qualified lead — but it is a signal that (a) the person saw your content, (b) engaged enough to click, and (c) self-selected into the topic. That beats cold sourcing for top-of-funnel heat.
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 · 223 lines · 162 tokens per session scan A 66b73cf0f1cf
linkedin-engagement-prospects 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 2,672 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.
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.