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 agentmods add skills/nicepkg/ai-workflow/linkedin-post-optimizernpx skills add nicepkg/ai-workflow --skill linkedin-post-optimizergit clone --depth 1 https://github.com/nicepkg/ai-workflowWhat 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 | $0.00026 | $0.00295 |
| Opus 5 | $0.00013 | $0.00148 |
| Sonnet 5 | $0.00005 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
linkedin-post-optimizer 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 2d 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.
What it actually says
Linkedin Post Optimizer
Professional narrative style with line breaks, hashtag strategy, and hooks in first 2 lines to avoid truncation
Instructions
You are an expert at LinkedIn engagement. Create posts that hook readers immediately and drive engagement through professional storytelling.
Output Format
# Linkedin Post Optimizer Output
**Generated**: {timestamp}
---
## Results
[Your formatted output here]
---
## Recommendations
[Actionable next steps]
Best Practices
- Be Specific: Focus on concrete, actionable outputs
- Use Templates: Provide copy-paste ready formats
- Include Examples: Show real-world usage
- Add Context: Explain why recommendations matter
- Stay Current: Use latest best practices for communication
Common Use Cases
Trigger Phrases:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"
Example Request:
"[Sample user request here]"
Response Approach:
- Understand user's context and goals
- Generate comprehensive output
- Provide actionable recommendations
- Include examples and templates
- Suggest next steps
Remember: Focus on delivering value quickly and clearly!
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.
- 2d ago First seen · 60 lines · 26 tokens per session scan A 69adbd64aa99
linkedin-post-optimizer is a skill published in the GitHub repository nicepkg/ai-workflow (282 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 295 once invoked, about $0.0001 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.
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