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.
/plugin marketplace add mishahanin/heading-os-marketplace/plugin install heading-contentWrote 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/mishahanin/heading-os-marketplace/linkedin-post)<a href="https://agentmods.dev/skills/mishahanin/heading-os-marketplace/linkedin-post"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/linkedin-post/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/mishahanin/heading-os-marketplace/linkedin-post"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os-marketplace/linkedin-post.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00052 | $0.00892 |
| Opus 5.5 | $0.00021 | $0.00357 |
| Sonnet 5.5 | $0.00010 | $0.00178 |
| Haiku 4.5 | $0.00005 | $0.00089 |
Grade A, and why
linkedin-post scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to linkedin-post — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post
Draft a LinkedIn post in Misha Hanin's authentic voice.
Variables
topic: [What is this post about?]
angle: [What's the specific angle, insight, or counterintuitive point?]
context: [Any specific details, data points, events, or recent observations to include]
length: short (~150 words) | medium (~300 words) | long (~500 words) — default: medium
Instructions
Customization (optional, Phase 0). This skill is customization-aware (pilot). Resolve any per-exec overrides first: python "${CLAUDE_PLUGIN_ROOT}"/scripts/resolve_customization.py --skill .claude/skills/linkedin-post. Apply any activation_steps_prepend, persistent_facts (e.g. a default length or recurring narrative), and output-path overrides from the merged result. On any failure, proceed with the defaults below — never block. Layout + authoring guide: config/skill-custom/README.md.
Before drafting, read:
reference/misha-voice.md— Complete voice guide (especially the LinkedIn section)datastore/content/linkedin-archive/old-archive/goal-is-a-cage.md— Best example of Misha's LinkedIn writingcontext/business-info.md— 31C positioning and ODUN.ONE contextdatastore/INDEX.md— If the post contains specific facts or numbers, validate against source documents
Draft a LinkedIn post that:
- Opens with a single, powerful sentence that works as a standalone hook
- Builds a narrative arc: observation → tension → insight → what it means
- Uses maritime metaphors only when they arise naturally (never forced)
- Connects to the category creation narrative (DPI → Deep Packet Intelligence, sovereignty, the incumbent vacuum) when relevant
- Ends with a thought-provoking statement — not a call to action, not a question asking for comments
- Reads like a human wrote it, not a marketing team
- Has paragraph breaks every 1-3 sentences (LinkedIn is read on mobile)
After the draft, provide:
- 2 alternative opening lines to consider
- 3-5 hashtags: always include some from #DataSovereignty #DeepPacketIntelligence #Cybersecurity #31Concept #ODUN
- Recommended posting time (Tuesday-Thursday, 7-9am UAE time typically performs best)
- One line on what to cut if shortening
What ships with it
3 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.
- 2mo ago First seen · 87 lines · 52 tokens per session scan A b50bcc574d61
linkedin-post is a skill published in the GitHub repository mishahanin/heading-os-marketplace (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 52 tokens to every session and 892 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to linkedin-post, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
domain-modeling
Use when pinning down domain terminology, building a ubiquitous language or project glossary, disambiguating overloaded or vague terms, or maintaining a CONTEXT.md — or when another skill needs to sharpen the domain model. Do NOT use for recording architectural decisions or for writing implementation specs.
code-review
Iterative, human-in-the-loop code review that detects gaps, presents them for selection, and fixes them in cycles. Supports both diff-based PR review and holistic codebase analysis.
address-pr-review
Address reviewer and bot comments on a GitHub pull request — validate each comment, apply fixes where valid, reply with reasoning, and resolve threads.
interview
Deep requirements gathering through probing questions that surface hidden requirements, edge cases, and trade-offs before implementation.
ideate
Facilitate brainstorming as a thinking partner that extracts and expands latent ideas rather than generating them outright.
writing-artifacts
Use when writing or revising a durable written artifact — README, ADR, design doc, PR description, release notes, runbook, error message, user-facing docs. Gives a positive writing system (reader model, sentence positions, document jobs), not a ban-list. Do NOT use for conversational replies to the user (global…