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 | $0.00023 | $0.00387 |
| Sonnet 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 12d 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.
- 12d 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 (150 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 774 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 2026-08-30.
Other skills, from other repositories
leadership-change-outreach
End-to-end leadership change signal composite. Takes any set of companies, detects recent leadership changes (new VP+, C-suite hires and promotions), evaluates relevance to your product, and drafts personalized outreach. Uses Apollo People Search (free) for fast detection + Apollo Enrichment (1 credit/person) for…
news-signal-outreach
End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the…
champion-move-outreach
End-to-end champion/buyer/user job change signal composite. Takes a set of known people (past buyers, champions, power users), detects when they move to a new company, researches the new company for ICP fit, and drafts personalized outreach leveraging the existing relationship. Tool-agnostic — works with any people…
linkedin-outreach
End-to-end LinkedIn outreach campaign builder. Takes leads from Supabase, upstream skills, or CSV. Aligns on campaign goal and tone, writes personalized LinkedIn message sequences (connection request + follow-ups + optional InMail), presents for review, and exports for the user's outreach tool (Dripify, Botdog…
funding-signal-outreach
End-to-end funding signal composite. Takes any set of companies, detects recent funding events, qualifies against your company context, finds relevant people (buyers, champions, users), and drafts personalized outreach. Tool-agnostic — works with any company source, contact finder, and outreach platform.
hiring-signal-outreach
End-to-end hiring signal composite. Takes any set of companies, detects job postings that your product augments or replaces, finds relevant people (the hiring manager, buyers, champions, users), and drafts personalized outreach using the job role as the hook. Tool-agnostic — works with any company source, job board…