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 swan-gtm/gtm-skills --skill linkedin-success-factorsgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/linkedin-success-factors)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-success-factors"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-success-factors/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/swan-gtm/gtm-skills/linkedin-success-factors"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-success-factors.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 | $0.00047 | $0.00774 |
| Opus 5.5 | $0.00019 | $0.00310 |
| Sonnet 5.5 | $0.00009 | $0.00155 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
linkedin-success-factors 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-success-factors — 86% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Key Success Factors
The 7 Rules
1. Do Not Breach Limits
Most important rule
- Stay under 400 connections/month
- Stay under 120 interactions/day
- Build in buffer for safety
2. Target Active Users Only
Don't waste your 400 connection requests
- Use "Posted within 30 days" filter
- Check for recent activity
- Avoid dormant profiles
3. Keep Messages Super Short
3-4 sentences maximum
- One paragraph only
- No walls of text
- Get to the point fast
4. Incorporate Eye-Catching Lead Magnet
Stand out in the inbox
- Loom videos (high engagement)
- LinkedIn posts (social proof)
- Webinar links (value offer)
- Newsletter signups (nurture path)
5. Make Steps Appear Human
Avoid bot-like behavior
- Randomize timing between actions
- Don't follow exact same sequence every time
- Mix in genuine engagement
- Take breaks
6. Schedule Within Audience Timezone
Maximize inbox visibility
- 9 AM - 6 PM their time
- Weekdays only
- Tuesday-Thursday optimal
- Avoid Monday mornings, Friday afternoons
7. Message When They're Online
Inbox popup maximizes open rate
- Check "Active now" indicator
- Time zones matter
- Business hours priority
Campaign Checklist
Before launching any LinkedIn campaign:
- Targets filtered to active users (30-day post activity)
- Messages under 4 sentences
- Connection request volume under 20/day
- Sequence mimics human behavior
- Scheduling matches target timezone
- Lead magnet or value hook included
- Follow-up sequence planned
- Account warm-up completed (if needed)
Performance Benchmarks
| Metric | Good | Great | Excellent |
|---|---|---|---|
| Connection acceptance | 25% | 35% | 45%+ |
| Message response | 10% | 15% | 20%+ |
| Meeting booked | 2% | 5% | 8%+ |
Common Mistakes
- Sending pitch in connection request - Save it for message
- Generic first message - Personalize to their profile
- Too many follow-ups - 2-3 max, then stop
- Ignoring response timing - Reply quickly
- Not warming up account - Critical for new/dormant accounts
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.
- 1mo ago First seen · 128 lines · 47 tokens per session scan A ded8faede4e2
linkedin-success-factors is a skill published in the GitHub repository swan-gtm/gtm-skills (220 stars, last pushed 7d ago), licensed MIT. It adds 47 tokens to every session and 774 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
cold-outreach
Use when writing a cold email or LinkedIn DM to a stranger and its cadence: first-touch copy under a word ceiling, 4-7 step bump sequences, per-inbox volume and warm-up limits, the compliant opt-out footer. NOT SPF/DKIM/DMARC setup (that is email-deliverability), NOT sourcing the list (that is lead-gen), NOT life…
linkedin-outreach
Use when running LinkedIn social selling as a repeatable motion — Sales Navigator saved searches and trigger alerts, weekly request budgets, fixing a low acceptance rate, turning profile views and post engagers into warm threads, logging every touch. NOT the note or DM copy (that is cold-outreach), NOT the deal after…
Cold Email Writer
Write personalized cold outreach emails to hiring managers and founders - specific, human, not a pitch deck.
afrexai-lead-hunter
Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.
agent-browser
Drive a real headless Chromium on the workspace host to read pages, fill forms, click buttons, run JS, and take screenshots. Use this whenever you need more than a plain HTTP fetch — pages that require JS to render, pages with an interactive element you have to click through, or when you want an accessibility-tree…
find-leads
Find qualified B2B leads with OpenOutreach — run openoutreach find N [emails], read the CSV it prints on stdout, and hand the rows to whatever sends. Use when the user wants leads, prospects, an ICP-matched contact list, or asks what a campaign already has. Also covers first-run setup (openoutreach init), openoutreach…