log-linkedin-network
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Growing a LinkedIn network, and reading and answering its inbox. A log play (docs/useCases/linkedin/network.md): Invite the people LinkedIn suggests on My Network, slowly, and read and answer the account's conversations, from its own session with no browser.
In this repository82 other files it gives its agents.
- AGENTS.mdAGENTS.md
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- log-instagram-dm-replySkill
- log-instagram-new-platformSkill
- log-linkedin-postsSkill
- log-reddit-contributor-maxxingSkill
- log-slack-dm-replySkill
- log-twitter-affiliate-marketingSkill
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- log-twitter-engager-dmsSkill
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- log-twitter-get-the-actionSkill
- log-twitter-manager-maxxingSkill
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- The first 60 shown.
The file
Files
Growing a LinkedIn network, and reading and answering its inbox
This is the log play linkedin/network. Before anything else, call the log MCP tool getGuide({ useCase: "linkedin/network" }): it returns this page and makes it the session's play, so every log tool's answer says which step you are on and a send with no play loaded is refused (rules 157, 158). Then follow the steps in order.
Draft every message from that person's own thread and matt's real replies (voice with no text, search), never from memory, and never send two that read alike (sameAsSent).
Invite the people LinkedIn suggests on My Network, slowly, and read and answer the account's conversations, from its own session with no browser.
How the owner asks for it: "connect with people on linkedin", "who does linkedin suggest", "read my linkedin messages", "what did <someone> say on linkedin", "reply to <someone> on linkedin"
Run it from a fresh context
Say: "go on docs/useCases/linkedin/network.md", with who to invite or whose messages to read. The agent reads this page and runs the steps.
The steps
| # | Step | Tool | Done when |
| 0 | The account is known and signed in | localAccountList {}, then linkedinSessionCheck { account }; if it isn't, the owner runs log linkedin cookies save --account <publicId> in a terminal | loggedIn as that profile |
| 1 | See who LinkedIn suggests | linkedinSuggestions { account } | the people on My Network, each with a public id |
| 2 | Pick who to invite with the owner, and dry run it | linkedinConnect { account, profile } | the dry run names the person and the pace left today |
| 3 | Invite one person | linkedinConnectLive { account, profile, approvedBy: "<owner>", confirmLive: true } | LinkedIn sets the invitation to Pending |
| 4 | List the inbox | linkedinInbox { account } | the 20 newest conversations, who wrote last and when |
| 5 | Read one conversation | linkedinMessages { account, with } or { account, conversation } | every message in it, attachments and voice messages marked |
| 6 | Answer them, when asked: show the owner the draft first | linkedinMessage { account, with, text }, then linkedinMessageLive { account, with, text, approvedBy: "<owner>", confirmLive: true } | LinkedIn names a new message with each line's text |
| 7 | Tell the owner what came back | the reply to the owner | who was invited, or what they said, with times |
What the runs taught
- Only people LinkedIn suggests. Connect sends the request My Network's own Connect button sends, read out of the page for that person. Someone not suggested is
profileNotSuggested. - Slow on purpose. One invitation a run, 15 minutes apart, at most 5 in 24 hours (
connectTooSoon,connectDailyCap). The pace is checked before the governor counts a write, so a refused one costs nothing. - LinkedIn's "invitation not sent" is a stop. It is
connectRefusedand counts as a throttle: nothing more goes out until the owner says so (rule 94). - One message per line. A draft on several lines goes out as that many messages, each with LinkedIn's typing signal first;
asOnesends it as one. The first line LinkedIn doesn't confirm stops the rest. - Only existing conversations. A first message to someone the account has never written to isn't built.
- Reading marks nothing seen. The inbox and a conversation are read with the calls the web client makes to list and open them, and none of the calls that mark a conversation seen.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

