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 Freespirits/social-auto-engine --skill post-scorergit clone --depth 1 https://github.com/Freespirits/social-auto-engineWrote 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/freespirits/social-auto-engine/post-scorer)<a href="https://agentmods.dev/skills/freespirits/social-auto-engine/post-scorer"><img src="https://agentmods.dev/badge/skills/freespirits/social-auto-engine/post-scorer.svg" alt="Measured on agentmods" 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 | $0.00111 | $0.01833 |
| Opus 5 | $0.00056 | $0.00916 |
| Sonnet 5 | $0.00022 | $0.00367 |
| Haiku 4.5 | $0.00011 | $0.00183 |
Grade A, and why
post-scorer 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 8d 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.
This is a copy
100% identical to post-scorer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post Scorer
CRITICAL: Auto-start on load
When this skill triggers, go straight to Step 1. Do not summarise. Do not explain the scoring method. Start immediately.
Step 1. Get the post
If the user already pasted a post in the same message, use it. Otherwise say:
Paste the LinkedIn post you want scored.
Wait for the post.
Step 2. Load scoring data
The scorer needs two things: the user's voice system and real performance data.
Voice system
Read about-me.md and voice.md from the project if they exist. If missing, note it and score without voice matching.
Performance data
Check for cached LinkedIn data in the project or outputs folder. Look for files matching *-all-posts.json or *-posts.txt.
If cached data exists, use it. If not, ask the user:
[
{
"question": "To score your post against real data, I need your LinkedIn history. How should I get it?",
"header": "Data source",
"multiSelect": false,
"options": [
{"label": "Scrape my posts", "description": "Pull my last 100 posts from LinkedIn via Apify. Takes 1 to 2 minutes, costs about $0.50."},
{"label": "Use Charlie Hills data", "description": "Score against Charlie Hills benchmarks (1,872 avg engagement, 500 posts analysed). Good fallback."},
{"label": "Skip data scoring", "description": "Score against generic best practices only. Less accurate but instant."}
]
}
]
If "Scrape my posts":
- Ask for their LinkedIn username
- Call Apify actor apimaestro/linkedin-profile-posts with input: { "username": "[their-username]", "total_posts": 100 }
- Download results (do NOT use the fields parameter, it strips engagement data)
- Save as [username]-all-posts.json in the project
- Proceed to analysis
If "Use Charlie Hills data": Look for cached Charlie data at **/linkedin-data/charlie-all-posts.json. If found, use it. If not, note you are using the benchmarks from this skill file (listed below).
If "Skip data scoring": Fall back to voice-system-only scoring and general best practices.
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.
- 8d ago First seen · 191 lines · 111 tokens per session scan A af5bb39f8a66
post-scorer is a skill published in the GitHub repository Freespirits/social-auto-engine (23 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 1,833 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to post-scorer, differing in 0 lines, and is treated as a copy.
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