linkedin-skills is a collection of Claude Code and Codex skills for creating and managing LinkedIn content from a terminal. It helps users draft posts, comments, and replies, review their feeds, and plan a publishing cadence while requiring approval before publication. The catalogue entries are the project's skills, instructions, and plugin for using these workflows with coding agents.
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 sergebulaev/linkedin-skills --skill linkedin-comment-draftergit clone --depth 1 https://github.com/sergebulaev/linkedin-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/sergebulaev/linkedin-skills/linkedin-comment-drafter)<a href="https://agentmods.dev/skills/sergebulaev/linkedin-skills/linkedin-comment-drafter"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-comment-drafter/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/sergebulaev/linkedin-skills/linkedin-comment-drafter"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-comment-drafter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00097 | $0.02251 |
| Opus 5.5 | $0.00039 | $0.00900 |
| Sonnet 5.5 | $0.00019 | $0.00450 |
| Haiku 4.5 | $0.00010 | $0.00225 |
Grade A, and why
linkedin-comment-drafter 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Comment Drafter
Produce conversation-provoking comments on any LinkedIn post from a URL. The skill targets the patterns that actually got author replies in 2026 testing and avoids the thesis-restatement patterns that die with zero engagement.
When to use
- User pastes a LinkedIn post URL and says "comment on this", "draft me a comment", "engage with this post"
- User wants to be among the first 3 commenters on a viral post
- User wants to reply to a closing question the author asked
- User wants to reshare/repost a post to their own feed, with or without a one-line take ("repost this with my thoughts", "reshare this")
Input
A LinkedIn post URL in any of the standard shapes (see the top-level SKILL.md URL table).
Output
1-3 draft comment variants, each with:
- 200-350 char body, 1-2 short paragraphs, em dashes capped (about one per 100 words), no hashtags
- Assigned reaction type:
LIKE,PRAISE,EMPATHY,INTEREST,APPRECIATION, orENTERTAINMENT - Pattern label (which of the 7 templates was used)
- Estimated engagement fit based on what the author typically responds to
Then waits for user approval. On "post", calls Publora to react + comment.
Steps
Voice profile first (all drafts). If ../../references/voice-profile.md has filled: yes, load it and match the user's voice fingerprint, hard rules, and CTA/link style throughout. If it is not filled, mention once that linkedin-humanizer --mode profile can learn their voice from a few posts, then proceed with the generic voice rules. If ../../references/story-bank.md has filled: yes, load it too and take concrete details (numbers, dates, named projects) from there instead of asking mid-draft. Never invent a figure that is not in it; if the bank has nothing that fits, ask the user or offer linkedin-interviewer.
- Parse the URL. Use
lib.url_parser.parse_linkedin_urlto getpost_urnand, if present, the post's activity ID. - Fetch the post body. If
APIFY_TOKENis set, calllib.ApifyClient.fetch_post(url)for the post body andfetch_post_comments(post_id=..., max_items=10)for the top existing comments (so your draft doesn't duplicate an existing take). Both actors are no-cookies and cost roughly $0.001 + $0.005 per call on the Apify free tier. IfAPIFY_TOKENis not set, ask the user to paste the post text and (optionally) top comments.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago Changed · +2 lines 9a97a215de88
- 1mo ago Changed 81af2d20a844
- 1mo ago Changed 5abd91ec6d64
- 1mo ago Changed 3d0e97f47bfd
- 1mo ago Changed · +18 lines f6e87c96a5e9
- 1mo ago First seen · 111 lines · 97 tokens per session scan A 2e32f19495c2
linkedin-comment-drafter is a skill published in the GitHub repository sergebulaev/linkedin-skills (5,223 stars, last pushed 5d ago), licensed MIT. It adds 97 tokens to every session and 2,251 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
linkedin-content-calendar-planner
Generate a 4-week LinkedIn content calendar tuned to the user's pillars, posting cadence, and audience. Returns a day-by-day plan with topic, format, hook angle, and CTA per post. Use when the user wants a system for the next month instead of inventing content every morning. Once the plan is confirmed it writes a…
linkedin-comment-opportunity-finder
Find the LinkedIn posts the user should comment on today to earn visibility, followers, and inbound. Pulls live candidates from the Taplio engagement feeds (the personalized selection, a curated creator feed for their niche, or a keyword search), scores each post on recency, audience overlap, engagement velocity, and…
linkedin-comment-reply-manager
Clear the comment inbox under the user's own LinkedIn posts. Pulls the comments that still need an answer (already answered, own, and hidden comments are filtered out), triages each one (question, pushback, lead signal, praise, spam), drafts a reply in the user's voice, and schedules the approved replies to post under…
linkedin-daily-engagement-routine
Run the user's daily 15-minute LinkedIn engagement session end to end : answer what is waiting under their own posts, pick today's 5 posts to comment on from the Taplio feeds, draft every reply and comment in their voice, get one batch approval, schedule everything, and close with the queue, yesterday's failures, and…
linkedin-engagement-watchlist-builder
Build the bench of creators the user should engage with every day. Samples the Taplio curated feeds that match their niche, the personalized selection, and a keyword search, clusters the live posts by author, and returns a ranked watchlist of 10 to 15 creators split into reach targets (bigger audiences, for…
linkedin-smart-comment-writer
Write a LinkedIn comment that gets noticed by the post author and the audience, save it as a Taplio comment draft, and, only on the user's explicit go, schedule it to post under their name. Avoids the generic great-post trap : 3 options on one angle (add, disagree, story, framework, question), ranked by likelihood of…