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-engagement-watchlist-buildergit 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-engagement-watchlist-builder)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-engagement-watchlist-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-engagement-watchlist-builder/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-engagement-watchlist-builder"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-engagement-watchlist-builder.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 | $0.00141 | $0.02071 |
| Opus 5.5 | $0.00056 | $0.00828 |
| Sonnet 5.5 | $0.00028 | $0.00414 |
| Haiku 4.5 | $0.00014 | $0.00207 |
Grade A, and why
linkedin-engagement-watchlist-builder 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-engagement-watchlist-builder — 95% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Engagement Watchlist Builder
Commenting works when it is a routine on the same 10 to 15 people, not a random walk through the feed. This skill builds that bench from live data.
When to trigger
The user says "who should I engage with", "build my engagement list", "which creators should I comment on every day", "which Taplio feed fits me", "my commenting routine is stale".
Inputs to ask for (only if missing)
- The size of the bench (default 12 : 6 reach targets, 6 peers).
- Creators to exclude (competitors, people they already talk to daily).
- Language of the creators (default the user's language, plus English if they post in both).
The user's follower count, niche, and audience come from get_me.
Scoring a creator
From the posts sampled for each author :
- Audience fit (0 to 3) : the author's headline and topics speak to the user's target audience.
- Thread health (0 to 3) : comments per post, and the comments-to-likes ratio. Above 1 comment per 5 likes = conversational audience = 3. Lots of likes, few comments = 1.
- Cadence (0 to 2) : 2 or more posts in the sample window = 2, one = 1. No posts to comment on means no bench slot.
- Reachability (0 to 2) : based on
followers_countwhen present. 3x to 20x the user's followers = reach target, 2. Similar size = peer, 2. Above 200K = usually saturated threads, 1. Unknown = 1 and mark "verify".
8 and above = bench. 5 to 7 = reserve. Under 5 = skip.
Process
- Orient : niche, language, target audience, follower count.
- List the curated feeds and shortlist the 3 to 5 that match the niche (industry feed, topic feed, regional feed).
- Sample each shortlisted feed and the personalized selection. Add one keyword search for peers the feeds miss.
- Cluster every sampled post by author. Compute the 4 scores.
- Split into reach targets and peers. Fill the bench, keep a reserve.
- For each creator on the bench : why they are there, their usual angle, the comment angle the user should take with them (add, disagree, story, framework, question), and the feed to find them in.
- Recommend the 2 or 3 feeds to open daily, and the routine.
- Tell the user where to keep the list (see limitations). Re-run monthly.
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 Changed · +1 lines 4f8296dcaf57
- 1mo ago First seen · 107 lines · 141 tokens per session scan A 81f32f87458e
linkedin-engagement-watchlist-builder is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (7 stars, last pushed 13d ago), licensed MIT. It adds 141 tokens to every session and 2,071 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-09-12.
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