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-engager-analyticsgit 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-engager-analytics)<a href="https://agentmods.dev/skills/sergebulaev/linkedin-skills/linkedin-engager-analytics"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-engager-analytics/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-engager-analytics"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-engager-analytics.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 | $0.00109 | $0.01292 |
| Opus 5 | $0.00055 | $0.00646 |
| Sonnet 5 | $0.00022 | $0.00258 |
| Haiku 4.5 | $0.00011 | $0.00129 |
Grade A, and why
linkedin-engager-analytics 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Engager Analytics
Pull every liker and commenter on a LinkedIn post and bucket them by ICP fit. Outputs a roster + action list you can feed into your DM or outreach queue.
Depends on APIFY_TOKEN. Without it, falls back to user-paste of the engager list.
When to use
- After publishing a post: "Who actually engaged? Are they ICP?"
- Before a campaign: "Pull the last 5 viral posts in my niche, group their commenters by company size"
- Reviewing competitor engagement: which prospects show up across multiple authors
Input
- One or more LinkedIn post URLs
- Optional: ICP definition (target titles, company size, industry)
- Optional: max engagers per post (default 100)
Output
Output format (engager roster, tier breakdown, action lists): see references/output-spec.md. Headline: a table of engagers labelled by ICP tier and a per-tier action list.
Steps
- Fetch engagers. Call
lib.ApifyClient.fetch_post_engagers(post_url=<url>, max_items=100). Returns a list of dicts withtype("commenters" | "likers"),name,subtitle(job title + company),url_profile,content(comment text if commenter),datetime. Cost is roughly $0.005 per engager-record. - Parse subtitle into structured fields. The
subtitletypically reads "Director at Acme Corp" or "Founder & CEO at SaaS Inc". Extract: title, company, seniority bucket (IC / Manager / Director / VP / C-suite / Founder). - Score ICP fit. Use the user's supplied ICP rules:
- Title match (regex or keyword list)
- Company size proxy (look up via the user's CRM if integrated, else mark Unknown)
- Industry match (parse company name + subtitle keywords)
- Assign tier.
- Peer: founder / operator at similar-stage company in same niche
- Aspirational: senior leader (Director+) at larger company in adjacent niche
- Prospect: title in ICP target list AND company in ICP target list
- Other: no match
- Produce action lists.
- Follow back: peers with active posting (heuristic: appears as author in
fetch_user_recent_commentsof any team member) - Comment-drop targets: aspirational tier
- DM-able: prospect tier, with a one-line DM opener referencing the specific post they engaged with ("Saw you reacted to . Curious. Are you currently ?")
- Follow back: peers with active posting (heuristic: appears as author in
- Optional cross-post analysis. If the user supplied multiple post URLs, deduplicate engagers and flag people who engaged with 2+ posts (highest-intent signal).
What ships with it
1 file 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.
- today Changed 127e2d9be217
- 6d ago Changed · +18 lines f7539b8650dd
- 10d ago First seen · 80 lines · 109 tokens per session scan A 46f73931968c
linkedin-engager-analytics is a skill published in the GitHub repository sergebulaev/linkedin-skills (1,489 stars, last pushed today), licensed MIT. It adds 109 tokens to every session and 1,292 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.
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Build a sharp, post-ready persona of the user's target LinkedIn audience : role, pains, jobs to be done, vocabulary, aspirations, what content they consume, what objections they raise. Use when the user is starting on LinkedIn or when their content does not resonate (low comments, no DMs, traffic without conversion).…
linkedin-content-pillars-builder
Define 3 to 5 LinkedIn content pillars consistent with the user's positioning, plus 5 to 10 post topics for each pillar. Pillars are the recurring themes that make a creator recognizable. Use after the user has defined their niche, or when their content feels random and they want a system. Requires the Taplio MCP…