Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/linkedin-post/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/linkedin-post)<a href="https://agentmods.dev/skills/different-ai/agent-bank/linkedin-post"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/linkedin-post/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/different-ai/agent-bank/linkedin-post"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/linkedin-post.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 | $0.00022 | $0.01939 |
| Opus 5.5 | $0.00009 | $0.00776 |
| Sonnet 5.5 | $0.00004 | $0.00388 |
| Haiku 4.5 | $0.00002 | $0.00194 |
Grade A, and why
linkedin-post 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.
How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I Do
Draft LinkedIn posts that apply learnings from the Tweet Lab performance tracker, adapted for LinkedIn's professional audience. This skill bridges the gap between Twitter/X learnings and LinkedIn's different engagement patterns.
The Process
1. RESEARCH -> Check Tweet Lab for what hooks/patterns work
2. CONTEXT -> Review 0 Finance messaging guidelines (avoid SEC red flags)
3. DRAFT -> Write post using winning patterns
4. ADAPT -> Adjust tone for LinkedIn (more professional, same authenticity)
5. REVIEW -> Check against compliance guidelines
Key Learnings from Tweet Performance Data
What Works (High Engagement)
| Pattern | Example | Why It Works |
|---|---|---|
| Personal story hook | "I always wished existed" | Creates emotional connection |
| Demo/Show format | Video + screenshots | Visual proof > claims |
| Relatable pain point | "Download PDF, read it, find bank details..." | Audience nods along |
| Casual lowercase | "thank you claude + playwright mcp" | Feels authentic, not corporate |
| Before/After transformation | Old way vs new way | Clear value proposition |
| No interface flex | "No login, no dashboard" | Simplicity is aspirational |
What Doesn't Work (Low Engagement)
| Anti-Pattern | Example | Why It Fails |
|---|---|---|
| Generic product description | "Created a small agent that..." | No hook, no story |
| Feature lists without context | "Features: X, Y, Z" | No emotional resonance |
| Corporate tone | "We're excited to announce..." | Feels like marketing |
| No visual | Text-only posts | Scroll-past material |
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 · 298 lines · 22 tokens per session scan A 1c089c9ef9d2
linkedin-post is a skill published in the GitHub repository different-ai/agent-bank (255 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 1,939 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 2026-08-30.
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