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-comment-opportunity-findergit 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-comment-opportunity-finder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-comment-opportunity-finder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-comment-opportunity-finder/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-comment-opportunity-finder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-comment-opportunity-finder.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 | $0.00134 | $0.02711 |
| Opus 5.5 | $0.00054 | $0.01084 |
| Sonnet 5.5 | $0.00027 | $0.00542 |
| Haiku 4.5 | $0.00013 | $0.00271 |
Grade A, and why
linkedin-comment-opportunity-finder 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 13d 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
94% identical to linkedin-comment-opportunity-finder — 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Comment Opportunity Finder
The fastest way to grow on LinkedIn with a small audience : comment on the right posts, early, before the thread saturates. This skill finds those posts from live data instead of a doom-scroll.
When to trigger
The user says "where should I comment today", "find me posts to engage with", "I want to grow through comments", "who is posting in my niche right now", "build my visibility through engagement".
Inputs to ask for (only if missing)
- How many opportunities they want (default 5).
- Where to look. Default to a mix : the personalized selection plus one curated feed that matches their niche. The user can also name a feed, a keyword, or a creator to focus on.
- The mix between reach targets (creators with a bigger audience than theirs) and peers (similar or smaller, for relationship building). Default 3 reach, 2 peers.
- Anyone to skip (direct competitors, people they already engage with daily).
Everything else (niche, language, topics, target audience) comes from get_me.
The 4 filters for a good comment opportunity
Score every candidate 0 to 3 on each filter. A post is worth commenting on at 8/12 or more.
- Recency : hours since
posted_at. Under 4 hours = 3, under 12 = 2, under 48 = 1, older = 0 unless the thread is still moving. - Audience overlap : does the author's headline and topic speak to the user's target audience ? Same audience = 3, adjacent = 2, loosely related = 1, unrelated = 0.
- Engagement velocity : comments per hour since posting (
metrics.commentsdivided by hours old). High and rising = 3. A post with many likes but almost no comments is a 1 : the audience is passive there. - Topic match : is this a subject where the user has a credible angle to add (their topics and keywords from
get_me) ? Yes with a specific take = 3, yes in general = 2, tangential = 1, no = 0.
Process
- Orient with the user's niche, language, topics, and target audience.
- Pull candidates from two or three sources (see the MCP section) : the personalized selection, one or two curated feeds matching the niche, and optionally a keyword search when the user wants a specific subject.
- Drop the user's own posts, posts from people on the skip list, and posts older than 48 hours unless the discussion is still active.
- Score the rest on the 4 filters. Keep the top N, respecting the reach / peer mix.
- For each pick, assign the comment angle the user should take :
- Add : bring the 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.
- Optionally peek at the existing thread on the top 3 to 5 picks to see which angles are already taken and whether the author replies to commenters.
- Return the prioritized list, then offer to draft the comments (hand off to the Smart Comment Writer skill, one post at a time, or draft them all in a batch with the same rules).
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.
- 13d ago Changed · +1 lines 4d9835517c02
- 14d ago First seen · 111 lines · 134 tokens per session scan A 77d2a5340a2d
linkedin-comment-opportunity-finder is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (3 stars, last pushed 5d ago), licensed MIT. It adds 134 tokens to every session and 2,711 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to linkedin-comment-opportunity-finder, differing in 0 lines, and is treated as a copy.
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