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-content-plannergit 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-content-planner)<a href="https://agentmods.dev/skills/sergebulaev/linkedin-skills/linkedin-content-planner"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-content-planner/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-content-planner"><img src="https://agentmods.dev/badge/skills/sergebulaev/linkedin-skills/linkedin-content-planner.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 | $0.00072 | $0.01901 |
| Opus 5 | $0.00036 | $0.00950 |
| Sonnet 5 | $0.00014 | $0.00380 |
| Haiku 4.5 | $0.00007 | $0.00190 |
Grade A, and why
linkedin-content-planner 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Content Planner
Produce a 7-day LinkedIn plan built around the 3-pillar discipline (Authority 40-50%, Personal Narrative 30-40%, Community 20-30%). Optionally adds a Product/Offer pillar at 10-15%.
When to use
- User asks "plan my week" or "what should I post this week"
- User wants to escape ad-hoc shipping and establish rhythm
- Before a launch week (user needs product-pillar alignment)
Input
- Theme (optional): e.g., "AI agents shipping in production", "first 6 months of Co.Actor"
- Audience description: e.g., "B2B founders, AI ops leaders, marketing VPs"
- Pillar mix (optional): defaults to 40% Authority / 30% Narrative / 20% Community / 10% Product
- Posting days (optional): defaults to Tue/Wed/Thu/Fri (4 posts)
- Voice samples (optional): paths to past posts for voice calibration
Output
A markdown plan with:
7-day calendar
| Day | Time | Pillar | Format | Hook formula | 1-line angle | CTA type | Goal |
|---|---|---|---|---|---|---|---|
| Mon | — | (commenting day) | — | — | — | — | — |
| Tue | 8:00 AM local | Authority | Text | F7 Odd-Precision Money | "What 3 months of agent ops costs" | Question close | Saves |
| Wed | 9:30 AM local | Narrative | Text | F4 Time-Anchor Confession | "Why I stopped publishing for 4 weeks" | Mirror question | Comments |
| Thu | 8:00 AM local | Community | Text | F14 Named Gratitude | "The 3 people who shaped our launch" | Tag + thanks | Reposts |
| Fri | 9:00 AM local | Narrative | Text | F11 Emotional Cold-Open | "The night our first deploy failed" | Soft close | Likes |
| Sat/Sun | — | (off) | — | — | — | — | — |
The Goal column spans saves / comments / reposts / likes across the four posts, satisfying the Goal mix check below.
Daily comment targets
For each posting day:
- 3-5 creators to engage (names or archetypes: "peer founders at 5-20k", "VCs with AI thesis", "BigCo CTOs")
- Comment pattern to apply (first-commenter, data-first, answer-their-question)
- Target count: 10-20 substantive comments per day
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.
- today Changed f44a7d0b80cd
- 10d ago First seen · 131 lines · 72 tokens per session scan A 872465d998fd
linkedin-content-planner is a skill published in the GitHub repository sergebulaev/linkedin-skills (1,489 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,901 once invoked, about $0.0004 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-niche-definer
Help the user define (or sharpen) their LinkedIn niche : audience, problem they solve, unique angle, and one-line positioning. The skill walks the user through a 7-question diagnostic, then synthesizes a positioning statement they can use across headline, About, and posts. Use when the user says "I do not know what to…
linkedin-swipe-file-builder
Help the user assemble a personal swipe file of high-performing LinkedIn posts, organized by hook pattern, format, and angle. The skill defines the structure, asks for inputs, turns saved posts into a usable reference library, then drafts the user's own post for every reference in the file (reusing structure, not…
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…
linkedin-audience-persona-builder
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…