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-daily-engagement-routinegit 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-daily-engagement-routine)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-plugin/linkedin-daily-engagement-routine"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-daily-engagement-routine/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-daily-engagement-routine"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-plugin/linkedin-daily-engagement-routine.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 | $0.00122 | $0.02316 |
| Opus 5.5 | $0.00049 | $0.00926 |
| Sonnet 5.5 | $0.00024 | $0.00463 |
| Haiku 4.5 | $0.00012 | $0.00232 |
Grade A, and why
linkedin-daily-engagement-routine 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
95% identical to linkedin-daily-engagement-routine — 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Daily Engagement Routine
Fifteen minutes a day, every day, beats one heroic hour on Sunday. This skill runs the whole session in one pass : inbox first, then outbound, then one approval, then the queue.
When to trigger
The user says "run my engagement routine", "do my daily comments", "engagement session", "let's do today's LinkedIn", or a scheduled task fires this skill.
Inputs to ask for (only if missing, and remember them for next time)
- The default curated feed to work (from the Engagement Watchlist Builder, or pick one that matches the niche).
- The daily outbound cap (default 5 comments : 3 reach targets, 2 peers).
- Guardrails : people to skip, topics to avoid, tone limits.
- Whether to include a keyword pass today (default no).
The session, step by step
Step 0 : baseline (30 seconds). Followers, connections, profile views today. Anything that failed or was cancelled since the last session, with the reason.
Step 1 : inbox (5 minutes). Every comment still waiting under the user's posts from the last 72 hours. Triage (question, pushback, lead signal, praise, spam) and draft a reply for each except spam. Same rules as the Comment Reply Manager skill.
Step 2 : outbound (7 minutes). Candidates from the personalized selection and the default feed. Score on recency, audience overlap, engagement velocity, topic match. Keep the top 5 within the reach / peer mix, never the same author twice, nobody older than 48 hours unless the thread is still moving. Draft one comment per pick with the Smart Comment Writer rules (specific reference, value, invitation ; no link, no pitch, no "great post").
Step 3 : review (2 minutes). One table with every reply and comment : target, text, characters. The user approves all, edits some, drops some.
Step 4 : ship (1 minute). Save every approved item as a Taplio comment draft (replies target their comment), then commit them in one go. Collect the scheduled times.
Step 5 : close (30 seconds). Queue status, lead signals to follow up, and the one thing to do differently tomorrow.
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 · +2 lines e6324adb086a
- 15d ago First seen · 104 lines · 122 tokens per session scan A 749d66ee2041
linkedin-daily-engagement-routine is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-plugin (3 stars, last pushed 5d ago), licensed MIT. It adds 122 tokens to every session and 2,316 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to linkedin-daily-engagement-routine, differing in 0 lines, and is treated as a copy.
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