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-comment-reply-managergit 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-comment-reply-manager)<a href="https://agentmods.dev/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-comment-reply-manager"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-comment-reply-manager/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-comment-reply-manager"><img src="https://agentmods.dev/badge/skills/taplioofficial/taplio-linkedin-claude-skills/linkedin-comment-reply-manager.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 | $0.00131 | $0.02146 |
| Opus 5 | $0.00066 | $0.01073 |
| Sonnet 5 | $0.00026 | $0.00429 |
| Haiku 4.5 | $0.00013 | $0.00215 |
Grade A, and why
linkedin-comment-reply-manager 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 today.
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.
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 Comment Reply Manager
The first hour after a post is where reach is won, and the author's replies are what keep the thread alive. This skill finds every comment still waiting under the user's posts and answers them in their voice.
When to trigger
The user says "reply to my comments", "what is waiting under my posts", "answer the comments on my last post", "clear my LinkedIn inbox", "keep my thread going", or runs the Daily Engagement Routine.
Inputs to ask for (only if missing)
- Which posts : default to the posts published in the last 7 days that have comments. The user can name one post, a window, or "everything".
- Tone guardrails, if any (formal, playful, never mention pricing). Voice and language come from
get_me. - Whether to approve replies one by one or as a batch. Default : one batch review, one go.
The 5 buckets
Triage every comment into one bucket, in this priority order :
- Question : they ask something. Answer it specifically. Highest value, highest reach.
- Pushback : they disagree or challenge. Answer with respect and one concrete point. These threads travel furthest.
- Lead signal : the commenter matches the user's target audience, asks about their method, offer, or results, or shares their own situation. Answer, then flag them for the Warm Lead Finder skill.
- Praise or agreement : short, warm, specific. Add a follow-up question to extend the thread when the commenter looks worth talking to.
- Spam or bait : self-promotion, generic AI filler, "DM me", trolling. Do not reply. List them so the user knows they were seen.
Process
- Orient : identity, username, language, target audience.
- List the user's recent posts and keep the ones with comments, newest first. Posts under 48 hours old come first.
- Pull the comments still waiting under those posts, a handful of posts at a time.
- Triage each comment into a bucket. Note
pinnedcomments and comments with replies already under them (reply_count) : those threads are visible to more people. - Draft one reply per comment in buckets 1 to 4 : 1 to 3 sentences, specific to what they wrote, first name when natural, a question at the end when the thread deserves to continue.
- Present everything in one review table. The user approves all, edits some, or drops some.
- Save each approved reply as a Taplio comment draft targeting that comment, then, on one explicit go for the batch, commit them. Quote the scheduled time of each.
- Close with the queue status and the lead signals to pass on.
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.
- today First seen · 108 lines · 131 tokens per session scan A 6ea40970e2c3
linkedin-comment-reply-manager is a skill published in the GitHub repository TaplioOfficial/taplio-linkedin-claude-skills (5 stars, last pushed yesterday), licensed MIT. It adds 131 tokens to every session and 2,146 once invoked, about $0.0007 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 2026-09-12.
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.