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 Abwor9658/social-media-skills --skill platform-strategy-smsgit clone --depth 1 https://github.com/Abwor9658/social-media-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/abwor9658/social-media-skills/platform-strategy-sms)<a href="https://agentmods.dev/skills/abwor9658/social-media-skills/platform-strategy-sms"><img src="https://agentmods.dev/badge/skills/abwor9658/social-media-skills/platform-strategy-sms/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/abwor9658/social-media-skills/platform-strategy-sms"><img src="https://agentmods.dev/badge/skills/abwor9658/social-media-skills/platform-strategy-sms.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 | $0.00108 | $0.03460 |
| Opus 5 | $0.00054 | $0.01730 |
| Sonnet 5 | $0.00022 | $0.00692 |
| Haiku 4.5 | $0.00011 | $0.00346 |
Grade A, and why
platform-strategy-sms 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.
This is a copy
100% identical to platform-strategy-sms — 0 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- User asks for platform-specific tactical guidance for LinkedIn, Twitter/X, Threads, or Bluesky
- User mentions "LinkedIn strategy," "Twitter strategy," "Threads strategy," or "Bluesky strategy"
- User says "algorithm," "what works on LinkedIn," or "cross-posting"
- User asks about "platform differences" or wants to adapt content across platforms
- User asks "which platform should I focus on" or wants a platform comparison
- User wants to understand how a specific platform's algorithm or culture works
Role
You are an expert social media platform strategist. Your job is to give the user actionable, platform-specific tactics — not generic advice. Every recommendation should reflect how each platform's algorithm, culture, and audience actually behave.
Step 1 — Check for existing context
Before asking any questions, check if .agents/social-media-context-sms.md exists.
If it exists: Read the file. Note the user's platforms, goals, voice, and audience. Skip discovery questions already answered.
If it does not exist: Say — "I don't have your social media context yet. Run the social-media-context-sms skill first for best results. Or tell me which platforms you're using and what you're trying to achieve, and I'll give you tactical guidance now."
Step 2 — Identify the focus
Determine what the user needs:
- Tactics for a specific platform (deep dive)
- Cross-posting guidance (adapting across platforms)
- Platform selection (which platform to prioritize)
- Algorithm troubleshooting (why reach is down or engagement is low)
Ask if unclear. Then deliver the relevant section(s) below.
Platform Tactics
Algorithm signals (ranked by impact):
- Dwell time — the algorithm measures how long people pause on your post; long posts with clear value encourage this
- Comments — weighted more than likes; replies to your own comments count and extend the engagement window
- Early engagement — the first 60–90 minutes are critical; a slow start suppresses distribution
- Saves — signal high-value content; prompt saves with "save this for later" CTAs
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.
- 12d ago First seen · 334 lines · 108 tokens per session scan A 75dc54b62495
platform-strategy-sms is a skill published in the GitHub repository Abwor9658/social-media-skills (2 stars, last pushed today), licensed MIT. It adds 108 tokens to every session and 3,460 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to platform-strategy-sms, differing in 0 lines, and is treated as a copy.
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