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 | $0.00078 | $0.02195 |
| Opus 5.5 | $0.00031 | $0.00878 |
| Sonnet 5.5 | $0.00016 | $0.00439 |
| Haiku 4.5 | $0.00008 | $0.00219 |
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 23d 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 — 138 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): only if
../../references/voice-profile.mdis not filled yet
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.
- 23d ago Changed · +7 lines 1d4e4c3679e3
- 1mo ago Changed · +6 tokens per session 06a49bb5c69e
- 1mo ago Changed f44a7d0b80cd
- 1mo 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 (5,223 stars, last pushed 5d ago), licensed MIT. It adds 78 tokens to every session and 2,195 once invoked, about $0.0003 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…