linkedin-series

linkedin-series is a skill for Claude Code from mishahanin/heading-os-marketplace. It costs 48 tokens per session (956 once invoked), scanned A, a copy of linkedin-series, Apache-2.0.

A planning tool for a LinkedIn series of at least three posts connected by one theme and publishing order. LinkedIn is a professional social network.

In plain words
What is it for?
Use it to plan multi-post LinkedIn campaigns, including the theme, audience, timing, opening lines, and angle for each post.
Why use it?
It turns a broad topic into a sequence of posts with a clear progression and purpose.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the heading-content plugin — 3 skills shipped together

Good fit Use it to plan multi-post LinkedIn campaigns, including the theme, audience, timing, opening lines, and angle for each post.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add mishahanin/heading-os-marketplace
Claude Code
/plugin install heading-content

Made for: Claude Code.

Or install heading-content, the plugin that ships this one along with the rest of its 3 skills.

Wrote 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.

agentmods badge for linkedin-series

README.md
[![agentmods](https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/linkedin-series/github.svg)](https://agentmods.dev/skills/mishahanin/heading-os-marketplace/linkedin-series)
Your own site
<a href="https://agentmods.dev/skills/mishahanin/heading-os-marketplace/linkedin-series"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/linkedin-series/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.

agentmods 80×15 button for linkedin-series

Your own site · 80×15
<a href="https://agentmods.dev/skills/mishahanin/heading-os-marketplace/linkedin-series"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/linkedin-series.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 956 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.00956
Opus 5 $0.00024 $0.00478
Sonnet 5 $0.00010 $0.00191
Haiku 4.5 $0.00005 $0.00096

Measured 12d ago against content hash bea667e11417, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

linkedin-series 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 12d 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.

Origin

This is a copy

88% identical to linkedin-series — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/heading-content/skills/linkedin-series/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Content Series

Plan a multi-post LinkedIn content series in Misha's voice around a theme or strategic moment.

Variables

theme: [Core theme or strategic narrative — e.g., "sovereignty vs. compliance", "the DPI category we're creating", "what MWC taught us"]

posts: [Number of posts — default: 4]

timeframe: [When to publish — e.g., "leading up to MWC", "during launch week", "over 4 weeks"]

goal: [What this series should accomplish — e.g., "establish category leadership", "build investor intrigue", "Tribe culture signal"]


Instructions

Before planning, read:

  • reference/misha-voice.md — Voice guide including LinkedIn section
  • datastore/content/linkedin-archive/old-archive/goal-is-a-cage.md — Voice and narrative example
  • context/strategy.md — Strategic priorities to align content with
  • context/current-data.md — Current milestones and proof points to reference

Produce a content series plan with:

Series Overview:

  • Theme and why it matters now
  • Strategic goal this series serves
  • Audience (who we're talking to)

For each post:

  • Post number and publish date
  • Title / working concept
  • Opening line (draft)
  • Core angle and narrative arc (2-3 sentences)
  • Key proof point or story to anchor it
  • Hashtags
  • How it connects to the next post in the series

Series Arc:

  • Post 1: Hook / provocation (sets up the tension)
  • Posts 2-N: Build evidence, story, proof
  • Final post: Resolution / call to the future

After the plan, produce a ready-to-publish draft of Post 1.


Session Memory (memlog)

A multi-post series is planned across turns. Keep an append-only working memory so the plan survives a context compaction and a later session can resume it.

  • On start: if outputs/content/linkedin/[theme-slug]/.memlog.md is absent, python "${CLAUDE_PLUGIN_ROOT}"/scripts/memlog.py init --workspace outputs/content/linkedin/[theme-slug] --field topic="[theme]" --field mode=series. If it already exists, do NOT re-run init — read it to resume, then append/set.
  • As you go: record each settled angle, hook, or proof point — python "${CLAUDE_PLUGIN_ROOT}"/scripts/memlog.py append --workspace outputs/content/linkedin/[theme-slug] --text "post 2 anchors on the MWC line-rate demo" --type decision.
  • On wrap-up: python "${CLAUDE_PLUGIN_ROOT}"/scripts/memlog.py set --workspace outputs/content/linkedin/[theme-slug] --key status --value complete.

Read the full file on GitHub · 95 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 95 lines · 48 tokens per session scan A bea667e11417

Subscribe to this mod's changes

linkedin-series is a skill published in the GitHub repository mishahanin/heading-os-marketplace (2 stars, last pushed 5d ago), licensed Apache-2.0. It adds 48 tokens to every session and 956 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to linkedin-series, differing in 6 lines, and is treated as a copy.

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