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 brainbytes-dev/everything-claude-marketing --skill social-linkedingit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWrote 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/brainbytes-dev/everything-claude-marketing/social-linkedin)<a href="https://agentmods.dev/skills/brainbytes-dev/everything-claude-marketing/social-linkedin"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/social-linkedin/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/brainbytes-dev/everything-claude-marketing/social-linkedin"><img src="https://agentmods.dev/badge/skills/brainbytes-dev/everything-claude-marketing/social-linkedin.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 | $0.00029 | $0.02378 |
| Opus 5.5 | $0.00012 | $0.00951 |
| Sonnet 5.5 | $0.00006 | $0.00476 |
| Haiku 4.5 | $0.00003 | $0.00238 |
Grade A, and why
social-linkedin 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 2mo 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Strategy
When to Activate
Use this skill when:
- Writing LinkedIn posts, articles, or newsletters
- Building thought leadership for an individual or executive
- Managing a company LinkedIn page
- Creating employee advocacy programs
- Generating B2B leads through content
- Planning LinkedIn content calendars
First Questions
Before creating LinkedIn content, clarify:
- Is this for a personal profile or company page? (Personal profiles get 5-10x more reach)
- What industry and job titles make up the target audience?
- What is the goal? (Thought leadership, lead gen, recruitment, brand awareness)
- What unique expertise or perspective does the author bring?
- What is the current connection count and average engagement?
- Is there a content calendar or is this ad hoc?
Core Rules
- LinkedIn rewards conversation, not broadcasting. Posts that generate comments win.
- Dwell time is king. The algorithm measures how long people spend on your post. Longer, formatted posts outperform short ones.
- Early engagement is critical. The first 60-90 minutes determine your post's reach.
- Personal stories outperform corporate speak. Even on a "professional" platform, human stories win.
- No external links in post body. Put links in the first comment. Posts with links get 40-50% less reach.
- Comment on your own post. Add the link, add context, or ask a follow-up question in the comments.
- Consistency over frequency. 3 great posts per week beat 7 mediocre ones.
- Company pages are billboards. Personal profiles are conversations. Act accordingly.
LinkedIn Algorithm (How It Works)
Phase 1: Initial Distribution (0-90 minutes)
Your post is shown to a small sample of your network (~5-10%). The algorithm measures:
- Dwell time — How long people pause on your post
- Reactions — Especially within the first 30 minutes
- Comments — Weighted 10-15x more than reactions
- Shares — Moderate signal
- Click "see more" — Signals the hook worked
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.
- 2mo ago First seen · 250 lines · 29 tokens per session scan A 28e329fb5a62
social-linkedin is a skill published in the GitHub repository brainbytes-dev/everything-claude-marketing (10 stars, last pushed 7mo ago), licensed MIT. It adds 29 tokens to every session and 2,378 once invoked, about $0.0001 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 2026-09-03.
social-linkedin compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| social-linkedinbrainbytes-dev/everything-claude-marketing | Skill | 10 | 7mo ago | MIT | A | 29 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |