What is linkedin-series?

A plan for a LinkedIn content series made up of at least three connected posts published in a chosen order. LinkedIn is a professional social network for sharing posts and articles.

Use it to plan a sequence around a strategic theme, event, launch week, or multi-week publishing schedule.

It helps develop one theme across multiple posts instead of treating every update as a separate idea. It also connects the series to a timeframe, audience, and communication goal.

linkedin-series — Skill for Claude Code by mishahanin/heading-os

Install

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.

Any agent

npx skills add mishahanin/heading-os --skill linkedin-series

Clone the repo

git clone --depth 1 https://github.com/mishahanin/heading-os

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/mishahanin/heading-os/linkedin-series/github.svg)](https://agentmods.dev/skills/mishahanin/heading-os/linkedin-series)

Your own site

<a href="https://agentmods.dev/skills/mishahanin/heading-os/linkedin-series"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/mishahanin/heading-os/linkedin-series"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os/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 982 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 original No closer match found 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.

Model	Per session	Once invoked
Fable 5.1	$0.00048	$0.00982
Opus 5.5	$0.00019	$0.00393
Sonnet 5.5	$0.00010	$0.00196
Haiku 4.5	$0.00005	$0.00098

Measured 1mo ago against content hash 065b325e3007, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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

Copies of this mod

1 near-identical copy found in the catalogue:

linkedin-series — 88% identical, 17 lines differ

.claude/skills/linkedin-series/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 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:

canon/reference/reference/misha-voice.md — Voice guide including LinkedIn section
work/deliverable/datastore/content/linkedin-archive/old-archive/goal-is-a-cage.md — Voice and narrative example
canon/position/context/strategy.md — Strategic priorities to align content with
ledger/measurement/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 work/deliverable/outputs/content/linkedin/[theme-slug]/.memlog.md is absent, python scripts/memlog.py init --workspace work/deliverable/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 scripts/memlog.py append --workspace work/deliverable/outputs/content/linkedin/[theme-slug] --text "post 2 anchors on the MWC line-rate demo" --type decision.
On wrap-up: python scripts/memlog.py set --workspace work/deliverable/outputs/content/linkedin/[theme-slug] --key status --value complete.

Read the full file on GitHub · 96 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.

triggers.json 1007 B

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.

1mo ago Changed 065b325e3007
1mo ago Changed · +1 lines 849e60293933
2mo ago First seen · 95 lines · 48 tokens per session scan A a98f77484b3e

Subscribe to this mod's changes

linkedin-series is a skill published in the GitHub repository mishahanin/heading-os (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 982 once invoked, about $0.0002 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.

Claude Code LLM Marketing and SEO Security Writing and docs ai-agent anthropic automation

linkedin-series

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

Copies of this mod

How it starts

LinkedIn Content Series

Variables

Instructions

Session Memory (memlog)

What ships with it

What this file has done since we first saw it

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