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 flaqai/backlink_skills --skill linkedin-writergit clone --depth 1 https://github.com/flaqai/backlink_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/flaqai/backlink_skills/linkedin-writer)<a href="https://agentmods.dev/skills/flaqai/backlink_skills/linkedin-writer"><img src="https://agentmods.dev/badge/skills/flaqai/backlink_skills/linkedin-writer/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/flaqai/backlink_skills/linkedin-writer"><img src="https://agentmods.dev/badge/skills/flaqai/backlink_skills/linkedin-writer.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 | $0.00135 | $0.04785 |
| Opus 5 | $0.00068 | $0.02393 |
| Sonnet 5 | $0.00027 | $0.00957 |
| Haiku 4.5 | $0.00014 | $0.00479 |
Grade A, and why
linkedin-writer 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-writer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Writer
Goal
Turn a topic, product, argument, report, source bundle, or existing draft into a credible LinkedIn-native long-form article that helps a defined professional audience make a decision, understand a change, or improve how they work.
This skill reuses the parent writer workflow for fact checking, humanization, image packaging, and optional Cloudflare R2 delivery, but it does not treat LinkedIn as a generic SEO blog host or as Medium with a different publishing button.
LinkedIn-native writing prioritizes:
- a specific professional reader and work context;
- a defensible point of view or useful decision framework;
- current LinkedIn search and conversation signals;
- expertise demonstrated through evidence, examples, and boundaries;
- short, skimmable sections for busy readers;
- a discussion-worthy close rather than a generic sales conclusion;
- a complete native publishing pack, including LinkedIn SEO settings.
Default language follows the user's request. If the user gives no language, use the language of their source material or target audience.
Format Routing
Use the requested format, not a blended default:
| Destination | Workflow |
|---|---|
| LinkedIn Article or LinkedIn newsletter edition | Use this skill in full |
| LinkedIn short feed post only | Use the short-post rules and publishing pack in this skill; do not force a long article |
| Google-first website article | Use ../SKILL.md |
| Medium article or third-party editorial essay | Use ../medium-writer/SKILL.md |
| Chinese WeChat Official Account article | Use ../wechat-writer/SKILL.md |
If the user says only “LinkedIn article” or “LinkedIn long-form,” default to a native LinkedIn Article. If they already run a newsletter and provide its name or theme, package the piece as a newsletter edition. Do not claim a newsletter was created or published without direct evidence.
Required References
Read each relevant file completely before acting:
- Topic discovery, LinkedIn search, trend expansion, and current seed topics:
references/linkedin-topic-research.md - Google-to-LinkedIn discovery, business-depth enrichment, and final LinkedIn humanization:
references/linkedin-business-depth-and-humanization.md - New article, rewrite, or reusable output format:
references/linkedin-article-template.md - Article review, scoring, and revision gate:
references/linkedin-review-rubric.md - Fact-heavy claims, comparisons, current products, or statistics:
../references/fact-check-and-style.md - Final natural-language edit after factual and structural fixes:
../references/humanization.md - Images, local paths, file packaging, and optional R2 delivery:
../references/output-packaging.md - R2 upload tasks only:
../references/r2-image-upload.mdand../references/r2-security.md
What ships with it
5 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.
- 10d ago First seen · 444 lines · 135 tokens per session scan A e65dcb6bca46
linkedin-writer is a skill published in the GitHub repository flaqai/backlink_skills (692 stars, last pushed 16d ago), licensed MIT. It adds 135 tokens to every session and 4,785 once invoked, about $0.0007 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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