03-linkedin-dms

03-linkedin-dms is an agent for Claude Code from assafkip/kipi-system. It costs 22 tokens per session (825 once invoked), scanned A, original, MIT.

A LinkedIn data agent that reads recent direct-message conversations and accepted connection requests.

In plain words
What is it for?
It records recent message threads, reply status, contact profile links, summaries, and newly accepted connections from the last ten days.
Why use it?
It identifies conversations that need a reply and preserves contact details and thread context, reducing manual inbox review.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter.

Good fit It records recent message threads, reply status, contact profile links, summaries, and newly accepted connections from the last ten days.

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Install with agentmods
npx agentmods add agents/assafkip/kipi-system/03-linkedin-dms
Install

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.

Clone the repo
git clone --depth 1 https://github.com/assafkip/kipi-system

Made for: Claude Code.

Wrote 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.

agentmods badge for 03-linkedin-dms

README.md
[![agentmods](https://agentmods.dev/badge/agents/assafkip/kipi-system/03-linkedin-dms/github.svg)](https://agentmods.dev/agents/assafkip/kipi-system/03-linkedin-dms)
Your own site
<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.

agentmods 80×15 button for 03-linkedin-dms

Your own site · 80×15
<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>
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 825 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.00825
Opus 5.5 $0.00009 $0.00330
Sonnet 5.5 $0.00004 $0.00165
Haiku 4.5 $0.00002 $0.00082

Measured 1mo ago against content hash eb1409553845, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

q-system/.q-system/agent-pipeline/agents/03-linkedin-dms.md · 86 lines

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

  1. Use Chrome MCP to navigate to https://www.linkedin.com/messaging/
  2. Scan ALL conversations with activity in the last 10 days
  3. 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)
  4. Navigate to https://www.linkedin.com/mynetwork/invitation-manager/sent/
  5. 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
  6. 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"
    }
  ]
}

Read the full file on GitHub · 86 lines

Changes

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.

  1. 1mo ago First seen · 86 lines · 22 tokens per session scan A eb1409553845

Subscribe to this mod's changes

03-linkedin-dms is an agent published in the GitHub repository assafkip/kipi-system (128 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 825 once invoked, about $0.0001 per session on Opus 5.5. 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.