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-thread-monitorgit 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-thread-monitor)<a href="https://agentmods.dev/skills/sergebulaev/linkedin-skills/linkedin-thread-monitor"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-thread-monitor/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-thread-monitor"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-thread-monitor.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 | $0.00103 | $0.01324 |
| Opus 5 | $0.00051 | $0.00662 |
| Sonnet 5 | $0.00021 | $0.00265 |
| Haiku 4.5 | $0.00010 | $0.00132 |
Grade A, and why
linkedin-thread-monitor 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 3d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Thread Monitor
Track which of your comments earned author replies. The author-reply signal is the highest-value inbound LinkedIn produces; this skill ensures you respond inside the window where momentum compounds.
Depends on APIFY_TOKEN. Without it, falls back to user-paste of recent comment URLs.
When to use
- Daily: "What threads need follow-up today?"
- After posting a batch of comments: "Check back in 6 hours"
- When an author replied personally: "Draft the response"
Input
- Your LinkedIn handle (last path segment of profile URL, e.g.
your-handle) - Optional: window in hours (default 72)
Output
Output format (daily report, warm-thread preview, weekly roll-up): see references/output-spec.md. Headline: a table of recent comments with author-reply status + recommended action.
Steps
- Fetch user's recent comments. If
APIFY_TOKENis set, calllib.ApifyClient.fetch_user_recent_comments(username=<your-handle>, result_limit=30). Each item already includes the parent post body, post URL, post author, and reaction stats. IfAPIFY_TOKENis not set, ask the user to list (or paste) the URLs of comments they've posted in the last 72h. - For each comment posted in last 72h: check the parent post's comment tree (use
fetch_post_comments(post_id=..., scrape_replies=True)) for:- Replies to the user's comment
- Whether the author posted any of those replies
- Timestamps (time since user's comment, time since latest reply)
- Classify stage:
- Hot (<6h): author just replied. Respond within 90 min for max thread momentum
- Warm (6-24h): the warm-reply window. Author replies most happen here
- Cool (24-72h): still respondable but lower velocity
- Dormant (>72h): don't reply in thread. Consider DM
- Draft responses for warm threads using
linkedin-reply-handler. - Flag suspicious patterns:
- Author replied but also deleted someone else's comment (author is actively moderating, tread carefully)
- Commenter is in thread self-promoting (your reply shouldn't engage them)
- DM routing: if thread is dormant but the author engaged meaningfully, draft a DM that references the thread specifically.
What ships with it
2 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.
- 3d ago Changed 6939aee9d5d9
- 9d ago Changed · +18 lines c0f46d7fb663
- 13d ago First seen · 84 lines · 103 tokens per session scan A c5c6d6cfaeb1
linkedin-thread-monitor is a skill published in the GitHub repository sergebulaev/linkedin-skills (1,826 stars, last pushed 3d ago), licensed MIT. It adds 103 tokens to every session and 1,324 once invoked, about $0.0005 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 2026-08-30.
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-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…
linkedin-analytics-interpreter
Translate raw LinkedIn analytics (impressions, engagement rate, profile visits, follower growth, top posts) into a clear diagnosis : what is working, what is not, and 3 specific actions to take next month. Use when the user has numbers but does not know what they mean or what to do about them. Requires the Taplio MCP…