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 Stallin-Sanamandra/b2b-saas-marketing-skills --skill linkedin-post-generator-operator-povgit clone --depth 1 https://github.com/Stallin-Sanamandra/b2b-saas-marketing-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/stallin-sanamandra/b2b-saas-marketing-skills/linkedin-post-generator-operator-pov)<a href="https://agentmods.dev/skills/stallin-sanamandra/b2b-saas-marketing-skills/linkedin-post-generator-operator-pov"><img src="https://agentmods.dev/badge/skills/stallin-sanamandra/b2b-saas-marketing-skills/linkedin-post-generator-operator-pov/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/stallin-sanamandra/b2b-saas-marketing-skills/linkedin-post-generator-operator-pov"><img src="https://agentmods.dev/badge/skills/stallin-sanamandra/b2b-saas-marketing-skills/linkedin-post-generator-operator-pov.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 | $0.00115 | $0.05057 |
| Opus 5 | $0.00057 | $0.02528 |
| Sonnet 5 | $0.00023 | $0.01011 |
| Haiku 4.5 | $0.00012 | $0.00506 |
Grade A, and why
linkedin-post-generator-operator-pov 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.
How it starts
The opening of the file, as written. The whole thing — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Generator (Operator POV)
A repeatable framework for turning real operator insights into LinkedIn posts that read like they came from someone running the work — not a content marketer pretending to. Replaces the "this could apply to anyone" voice with specificity that signals depth, while keeping employer-identifiable details off the public record.
Why This Skill Exists
Most marketing leaders' LinkedIn output reads identical to every other marketing leader's LinkedIn output: vague aphorisms, framework worship, and posts that could have been written by anyone in any company. The signal of "I've actually done this" is missing.
This skill fixes three failure modes:
-
Generic frame, generic content. Posts about "the importance of alignment" or "data-driven decision making" are wallpaper. They don't distinguish the writer from any other marketing voice.
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Abstract claims, no proof. "ABM works." "Pipeline marketing is dead." Without operator-level specificity (decisions, trade-offs, directional numbers), claims read as opinions, not evidence.
-
Polish without payoff. A well-formatted post with no actual insight is worse than a rough post with one real lesson. Most generators optimize for readability and skip the substance.
This skill produces posts that lead with a real operator situation (decision, trade-off, miss, win), expose the reasoning, and end with a learnable takeaway or open question — not a forced CTA or generic conclusion.
When to Use This Skill
- Drafting a LinkedIn post about a marketing decision, framework, or lesson
- Turning an internal artifact (skill, doc, dashboard) into a public-facing post
- Announcing a new repo, framework, or open-source contribution
- Converting a longer piece of writing (newsletter, blog) into a LinkedIn-native version
- Building a 4-post queue to fill out a weekly publishing cadence
- Responding to a competitor or industry event with an operator point of view
- Documenting a miss or change in approach (these often outperform "win" posts)
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.
- 12d ago First seen · 518 lines · 115 tokens per session scan A b0362bf5c469
linkedin-post-generator-operator-pov is a skill published in the GitHub repository Stallin-Sanamandra/b2b-saas-marketing-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 115 tokens to every session and 5,057 once invoked, about $0.0006 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 2026-08-31.
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