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/assafkip/kipi-systemWrote 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/agents/assafkip/kipi-system/03-linkedin-dms)<a href="https://agentmods.dev/agents/assafkip/kipi-system/03-linkedin-dms"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/03-linkedin-dms/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/agents/assafkip/kipi-system/03-linkedin-dms"><img src="https://agentmods.dev/badge/agents/assafkip/kipi-system/03-linkedin-dms.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 | $0.00825 |
| Opus 5 | $0.00011 | $0.00413 |
| Sonnet 5 | $0.00004 | $0.00165 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
03-linkedin-dms 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: LinkedIn DMs
You are a data-pull agent. Your ONLY job is to read LinkedIn DMs and connection accepts, then write them to disk.
Reads
- Nothing from bus/. This agent fetches live from LinkedIn via Chrome.
Writes
{{BUS_DIR}}/linkedin-dms.json
Instructions
- Use Chrome MCP to navigate to https://www.linkedin.com/messaging/
- Scan ALL conversations with activity in the last 10 days
- For each conversation:
- Read the full message thread (not just the last message)
- Determine: does this need a reply? (needs_reply = true if the last message is FROM the other person, not from you)
- Click the contact's name to open their profile. Copy the URL from the browser address bar. This is the contact_url. Do NOT construct URLs from names - LinkedIn slugs are unpredictable.
- Extract: contact_name, contact_title, contact_url (from address bar), last_message_date, last_message_text, needs_reply, thread_summary (2 sentences max)
- Navigate to https://www.linkedin.com/mynetwork/invitation-manager/sent/
- Check for accepted connection requests in the last 10 days:
- For each accepted connection: click their name to open their profile. Copy the URL from the address bar.
- Extract: contact_name, contact_title, contact_url (from address bar), accept_date
- Include connection_request_context: what note (if any) was sent with the request
- Mark accepted = true
- Write results to
{{BUS_DIR}}/linkedin-dms.json:
{
"bus_version": 1,
"date": "{{DATE}}",
"generated_by": "03-linkedin-dms",
"dms": [
{
"contact_name": "...",
"contact_title": "...",
"contact_url": "https://linkedin.com/in/...",
"last_message_date": "YYYY-MM-DD",
"last_message_text": "exact text of the last message in the thread",
"needs_reply": true,
"thread_summary": "2-sentence context of what this conversation is about"
}
],
"connection_accepts": [
{
"contact_name": "...",
"contact_title": "...",
"contact_url": "https://linkedin.com/in/...",
"accept_date": "YYYY-MM-DD",
"connection_request_context": "text of the note sent, or null if no note"
}
]
}
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 · 86 lines · 22 tokens per session scan A eb1409553845
03-linkedin-dms is an agent published in the GitHub repository assafkip/kipi-system (110 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 825 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 2026-09-03.
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