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-plugin --skill linkedin-smart-comment-writergit clone --depth 1 https://github.com/TaplioOfficial/taplio-linkedin-pluginWrote 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-plugin/linkedin-smart-comment-writer)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-smart-comment-writer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-smart-comment-writer/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-plugin/linkedin-smart-comment-writer"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-smart-comment-writer.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 | $0.00143 | $0.02168 |
| Opus 5.5 | $0.00057 | $0.00867 |
| Sonnet 5.5 | $0.00029 | $0.00434 |
| Haiku 4.5 | $0.00014 | $0.00217 |
Grade A, and why
linkedin-smart-comment-writer 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 14d 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.
This is a copy
91% identical to linkedin-smart-comment-writer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Smart Comment Writer
A comment the author pins can drive more profile visits than a full post. This skill writes that comment, saves it to Taplio, and ships it only when the user says go.
When to trigger
The user pastes a post or a LinkedIn url and says "write me a comment", "what should I say on this", "comment on this without being basic", "make this comment land", or picks a post from the Comment Opportunity Finder ("draft 1", "draft all").
Inputs to ask for (only if missing)
- The target post : a
post_urnfrom the Comment Opportunity Finder, a LinkedIn post url, or the pasted text. - The angle, or "pick for me" :
- Add : bring a missing angle, data, or context.
- Disagree : push back on one specific point with respect.
- Story : a 2-line micro-experience that mirrors or counters the post.
- Framework : a usable mental model or checklist.
- Question : the question that pushes the conversation forward.
- Anything to avoid (a topic, a product mention, a person).
Voice, language, and positioning come from get_me.
Process
- Resolve the post to a
post_urnand read its full text (see the MCP section). - Read the thread already under it : which angles are taken, whether the author replies, what the top comments do. The comment must add something the thread does not have yet.
- Identify the 2 or 3 strongest claims the author makes.
- Pick the angle that fits the user's positioning and the open gap in the thread.
- Write 3 variants with this structure :
- Open with a specific reference to something in the post (proves you read it).
- Deliver the value (the add, the disagreement, the story, the framework, the question).
- Close with something that invites a reply from the author or the audience.
- Rank them, strongest first. Show the character count of each.
- When the user picks one (or edits it), save it as a Taplio comment draft. Then ask, in one clear question, whether to schedule it. Only commit on an explicit yes.
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.
- 14d ago First seen · 107 lines · 143 tokens per session scan A 4ef7a3ec64d9
linkedin-smart-comment-writer is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (3 stars, last pushed 5d ago), licensed MIT. It adds 143 tokens to every session and 2,168 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to linkedin-smart-comment-writer, differing in 0 lines, and is treated as a copy.
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