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 mishahanin/heading-os --skill linkedin-seriesgit clone --depth 1 https://github.com/mishahanin/heading-osWrote 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/linkedin-series)<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.
<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>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 | $0.00932 |
| Opus 5 | $0.00024 | $0.00466 |
| Sonnet 5 | $0.00010 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linkedin-series — 88% identical, 6 lines differ
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 sectiondatastore/content/linkedin-archive/old-archive/goal-is-a-cage.md— Voice and narrative examplecontext/strategy.md— Strategic priorities to align content withcontext/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.mdis absent,python scripts/memlog.py init --workspace outputs/content/linkedin/[theme-slug] --field topic="[theme]" --field mode=series. If it already exists, do NOT re-runinit— read it to resume, thenappend/set. - As you go: record each settled angle, hook, or proof point —
python 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 scripts/memlog.py set --workspace outputs/content/linkedin/[theme-slug] --key status --value complete.
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.
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.
- 10d ago First seen · 95 lines · 48 tokens per session scan A a98f77484b3e
linkedin-series is a skill published in the GitHub repository mishahanin/heading-os (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 932 once invoked, about $0.0002 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.
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