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 TaplioOfficial/taplio-linkedin-claude-skills --skill linkedin-hook-generatorgit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-claude-skillsWrote 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/taplioofficial/taplio-linkedin-claude-skills/linkedin-hook-generator)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-hook-generator"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-hook-generator/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/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-hook-generator"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-hook-generator.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 | $0.00100 | $0.01193 |
| Opus 5 | $0.00050 | $0.00596 |
| Sonnet 5 | $0.00020 | $0.00239 |
| Haiku 4.5 | $0.00010 | $0.00119 |
Grade A, and why
linkedin-hook-generator 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-hook-generator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- The topic or angle of the post.
- The post body (if they have one). If not, work from the topic alone.
- The audience. Default to "professional LinkedIn audience".
The 7 hook patterns to rotate through
- Curiosity gap : "I just spent $X to learn one thing about Y."
- Contrarian : "Stop doing X. Here is why."
- Number tension : "9 out of 10 founders make this mistake."
- Personal stake : "I almost lost my company last month."
- Question : "Why do most LinkedIn posts get zero comments ?"
- Before / after : "2 years ago I had 200 followers. Today I have 50K. Here is what changed."
- Callout : "If you are a [persona] doing [action], read this."
Process
- Generate at least 1 hook per pattern (so 7 minimum).
- Add 3 more in the patterns that fit the topic best.
- For each hook, write 2 lines max (line 1 + line 2 if needed).
- 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.
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.
- 12d ago First seen · 84 lines · 100 tokens per session scan A 239cd5407121
linkedin-hook-generator is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed today), licensed MIT. It adds 100 tokens to every session and 1,193 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
linkedin-humanizer
Remove the AI tells human readers and LinkedIn's AI-slop filter react to in a post or comment: 2026 vocabulary by paragraph density, reveal bridges, staccato fragments, stacked triads, performed sincerity. Tiered rewriter (forensic / strict / aesthetic / all) plus --mode audit pass-fail review and --mode profile voice…
linkedin-marketing
Plan, draft, audit, and publish LinkedIn posts and comments. Use when the user wants to write a viral LinkedIn post, draft a comment or reply on any LinkedIn post URL, audit a draft against 2026 algorithm heuristics, remove AI tells, extract hook formulas from viral posts, or plan a week of content. Powered by the…
linkedin-reply-handler
Draft a reply to a specific existing LinkedIn comment from its URL. Use when the user wants to reply to a comment on any post, or follow up after an author replied to them. Parses the commentUrn, resolves the correct parentComment target (LinkedIn flattens threads to 2 levels), and posts via Publora on approval. Not…
linkedin-post-writer
Draft a new LinkedIn post from scratch using one of 20 2026 hook formulas (anaphora, R.I.P., time-anchor, curiosity-gap, contrarian, controlled A/B, false-binary, and more) plus a founders-edition angle library, picked by engagement goal (comments, reposts, likes, saves). Runs the humanizer pass and schedules via…
linkedin-comment-drafter
Draft a LinkedIn comment on someone else's post from its URL, or reshare (repost) it to your feed with optional commentary. Use when the user pastes a post URL and asks to comment, engage, be first commenter, or repost with their thoughts. Produces 1-3 variants in the user's voice, picks a reaction, and publishes via…
linkedin-content-planner
Generate a 7-day LinkedIn content plan from a theme, audience, and pillars. Produces per-day post pillar, format, hook type, CTA, posting time, daily comment targets, and a weekly inbound-readiness check. Use when the user wants to plan a week or month of content, not draft a single post.