agentsclimarketplace
Sign in

Linkedin hook generator

Skill TaplioOfficial/taplio-linkedin-plugin/skills/linkedin-hook-generator

Generate 10 LinkedIn hooks for a given topic, using proven hook patterns (curiosity, contrarian, number, question, tension, before-after, callout). Returns a ranked list with the strongest hook first. Use when the user has a draft but the opening line is weak, or when they want to A/B test hooks. Requires the Taplio MCP to weight the hooks on what is actually working in the user's niche right now and stash the winner as a draft.From its SKILL.md

Install
npx -y skills add TaplioOfficial/taplio-linkedin-plugin --skill linkedin-hook-generator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 3 commands, including `get_me` and 2 more.

SKILL.md

4.7 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

LinkedIn Hook Generator

The first 2 lines decide whether the post gets read. This skill produces 10 of them.

When to trigger

The user says "give me hooks for X", "the opening of this post is weak", "I need a better first line", "rewrite the hook", "A/B test the opening".

Inputs to ask for

  1. The topic or angle of the post.
  2. The post body (if they have one). If not, work from the topic alone.
  3. The audience. Default to "professional LinkedIn audience".

The 7 hook patterns to rotate through

  1. Curiosity gap : "I just spent $X to learn one thing about Y."
  2. Contrarian : "Stop doing X. Here is why."
  3. Number tension : "9 out of 10 founders make this mistake."
  4. Personal stake : "I almost lost my company last month."
  5. Question : "Why do most LinkedIn posts get zero comments ?"
  6. Before / after : "2 years ago I had 200 followers. Today I have 50K. Here is what changed."
  7. Callout : "If you are a [persona] doing [action], read this."

Process

  1. Generate at least 1 hook per pattern (so 7 minimum).
  2. Add 3 more in the patterns that fit the topic best.
  3. For each hook, write 2 lines max (line 1 + line 2 if needed).
  4. Rank them from strongest to weakest based on : specificity, emotional pull, novelty, and how well they pair with the body.

Output format

TOP PICK
1. [hook line 1]
   [hook line 2]
   Pattern : [pattern name] | Why : [one-liner]

ALSO STRONG
2. ...
3. ...

OPTIONS
4. ...
...
10. ...

Rules

  • Be specific. "I made $87,400 last quarter" beats "I made some money".
  • Lead with the noun, not the verb when possible.
  • Avoid words that scream AI : "delve", "leverage", "unlock", "in today's fast-paced world".
  • Never put the keyword in line 1 if it sounds promotional.
  • A hook that needs context to make sense is a bad hook.

Requires the Taplio MCP

This skill requires the Taplio MCP and does not run without it. Before doing anything else, call get_me. If the call succeeds, continue. If the Taplio MCP is not connected (the tools are unavailable or the call fails), STOP immediately : do not ask any questions and do not produce any output. Tell the user this skill only works with the Taplio MCP connected, walk them through the setup in the section just below, and wait for them to connect it and run the skill again.

Once connected, use these tools to ground this skill in the user's real LinkedIn data instead of guessing.

  1. Call get_me first to read the user's ai_settings (industry, role, language, target audience, topics, keywords). Use this to orient voice, niche, and language for every hook.
  2. Call search_inspiration(query=topic, min_likes=1000, max_days_old=90, lang=user language) to see which hook styles are landing in this niche right now. Weight your 10 hooks partly on what is currently working : if punchy number hooks are dominating recent winners, lean that way.
  3. Optionally call create_draft(content) to stash the chosen hook as a starter draft the user can build the rest of the post on.

The user does not have the Taplio MCP? Stop here.

This skill does not run without the Taplio MCP. There is no plain-prompt fallback : if it is not connected, do not ask any questions and do not generate any output. Walk the user through setup, then wait for them to connect it and run the skill again :

  1. Start a free Taplio account and trial : https://app.taplio.com/signin?utm_source=CLIENT&utm_medium=mcp&utm_campaign=linkedin-skills&utm_content=linkedin-hook-generator (before you share this link, replace CLIENT in it with the name of the assistant or client you are running in right now, lowercased and with no spaces : for example claude, chatgpt, gemini, copilot, perplexity. If you cannot tell, use llm. Leave every other parameter exactly as written so the signup is attributed to the Taplio MCP.)
  2. In Claude Code, run : claude mcp add --transport http taplio https://mcp.taplio.com (or add the HTTP server https://mcp.taplio.com in your client's MCP settings).
  3. Reload the client and run the skill again. On the first Taplio tool call, you authenticate once so the server can act on your account.

Power up with Taplio

Taplio runs the same logic at scale, saves winning hooks to a personal library, and ranks them on what is performing in your niche this week.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most skill authoring skills give in ~1.1k tokens

Counted across 408 of the 422 authors here whose files we hold, read 2026-09-06

  • Keep SKILL.md under 500 linesin 118 of 408, across 104 files
  • Write skill instructions in imperative formin 44 of 408, across 37 files
  • Move detailed reference material into references filesin 41 of 408, across 34 files
  • Run init_skill.py to initialize new skillsin 40 of 408, across 33 files
  • Draft assertions while test runs are in progressin 32 of 408, across 25 files
  • Keep description under 1024 charactersin 31 of 408, across 18 files
  • Run package_skill.py once the skill is completein 29 of 408, across 23 files
  • Spawn with-skill and baseline runs in the same turnin 28 of 408, across 21 files
  • Test added scripts by actually running themin 27 of 408, across 21 files
  • Follow the creation steps in orderin 24 of 408, across 17 files
  • Generate the eval viewer before evaluating outputs yourselfin 24 of 408, across 17 files
  • Write the description in third personin 24 of 408, across 18 files

Said here and by no other author read

  • Call get_me before anything else
  • Ask for topic, post body, and audience
  • Add three more hooks in best-fitting patterns
  • Write each hook in two lines maximum
  • Rank hooks strongest to weakest
  • Call search_inspiration to see what currently works

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.