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.
git clone --depth 1 https://github.com/eddiebelaval/squireWrote 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/commands/eddiebelaval/squire/post-linkedin)<a href="https://agentmods.dev/commands/eddiebelaval/squire/post-linkedin"><img src="https://agentmods.dev/badge/commands/eddiebelaval/squire/post-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/commands/eddiebelaval/squire/post-linkedin"><img src="https://agentmods.dev/badge/commands/eddiebelaval/squire/post-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 | $0.00000 | $0.00840 |
| Opus 5 | $0.00000 | $0.00420 |
| Sonnet 5 | $0.00000 | $0.00168 |
| Haiku 4.5 | $0.00000 | $0.00084 |
Grade A, and why
post-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 5d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/post-linkedin - Post to LinkedIn for ID8Labs
Post content to Eddie's LinkedIn profile using Playwright browser automation.
Usage
/post-linkedin [content]
/post-linkedin --from-essay <url> # Generate from essay URL
/post-linkedin --draft # Review before posting
Process
-
Prepare Content
- If content provided directly, use it
- If --from-essay, fetch essay and adapt for LinkedIn
- LinkedIn posts should be 1300-3000 characters (long-form performs well)
-
LinkedIn Format Optimization
Structure that works:
- Strong hook (first 3 lines visible before "see more")
- Line breaks between paragraphs
- Use emoji sparingly (1-3 per post)
- Call-to-action or question at the end
- No hashtags in text (comment with hashtags after)
-
Pre-Flight Check
- Verify Comet browser is running with debugging
- Confirm logged into LinkedIn as Eddie Belaval
- Display content for confirmation
-
Post via Playwright
Navigate to: linkedin.com/feed Click: "Start a post" button Type content (preserve line breaks) Click: "Post" button Capture screenshot as confirmation -
Post-Publish
- Add hashtags in first comment
- Capture post URL
- Log for analytics tracking
LinkedIn vs X Differences
| Aspect | X/Twitter | |
|---|---|---|
| Length | 1300-3000 chars | 280 chars (or threads) |
| Tone | Professional but personal | Casual, punchy |
| Hashtags | In comments, 3-5 | In post, 0-2 |
| Links | OK in post | Penalized, put in reply |
| Format | Long-form storytelling | Hooks and one-liners |
Voice Adaptation for LinkedIn
Eddie's voice on LinkedIn is:
- Still authentic and personal
- More polished, less parenthetical
- Professional credibility woven in
- Lessons and frameworks emphasized
- Community-building language
Transform:
- "(Loud ape sounds)" → [omit or professional equivalent]
- "jacked to the tits" → "deeply excited about"
- Short punchy lines → Developed paragraphs
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.
- 5d ago First seen · 127 lines · 0 tokens per session scan A d503fb50e5eb
post-linkedin is a command published in the GitHub repository eddiebelaval/squire (21 stars, last pushed 23d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 840 tokens. 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.
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