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 cdeistopened/skill-stack --skill social-content-creationgit clone --depth 1 https://github.com/cdeistopened/skill-stackWrote 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/cdeistopened/skill-stack/social-content-creation)<a href="https://agentmods.dev/skills/cdeistopened/skill-stack/social-content-creation"><img src="https://agentmods.dev/badge/skills/cdeistopened/skill-stack/social-content-creation/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/cdeistopened/skill-stack/social-content-creation"><img src="https://agentmods.dev/badge/skills/cdeistopened/skill-stack/social-content-creation.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 | $0.00070 | $0.03394 |
| Opus 5.5 | $0.00028 | $0.01358 |
| Sonnet 5.5 | $0.00014 | $0.00679 |
| Haiku 4.5 | $0.00007 | $0.00339 |
Grade A, and why
social-content-creation 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 1mo 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 — 458 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Content Creator
Transform any source material into high-performing social media posts using the framework fitting method: extract standalone concepts, match to proven templates, generate volume, select best, execute, and proliferate winners.
Purpose
Content creation isn't about constantly coming up with new ideas. It's about turning 1 idea into 1000 variations using proven frameworks.
Core Philosophy: Good content follows proven formats. The skill is in framework fitting - matching concepts to the delivery mechanisms that amplify them best.
When to Use This Skill
- Repurposing newsletter content into social posts
- Transforming long-form articles into multi-platform campaigns
- Creating LinkedIn posts from any concept or story
- Generating multiple post options quickly
- Multiplying high-performing posts using SCAMPER or desire reframing
Not for: Short-form video captions (use video-caption-creation), podcast clip selection (separate workflow)
The Framework Fitting Method
Core Principle
Good content follows proven formats/frameworks/templates. The skill is in framework fitting - matching concepts to the delivery mechanisms that amplify them best.
Critical Rule: Avoid First-Match Bias
Do NOT default to the first template that seems to fit.
"Analyze, Then Select" - Treat template selection as a distinct step. Consider ALL applicable frameworks before selecting.
The best framework might not be the obvious one.
The 4-Phase Workflow
Phase 1: Extract Standalone Concepts
Goal: Identify the most shareable "standalone" concepts from source material
Step 1: Read Source Material Thoroughly
- Full newsletter, article, or podcast transcript
- Look for bundles of ideas within their context
- Self-contained insights that can live independently
Step 2: Extract Concepts
For each standalone concept, capture:
- Core Insight (1-2 sentences)
- Context Needed (what background info is required?)
- Emotion/Value (what does this unlock for the reader?)
- Human Desire(s) (which of the 8 desires does this address?)
- Potential Angles (initial framework ideas)
What ships with it
6 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.
- 1mo ago First seen · 458 lines · 70 tokens per session scan A 709c3d4a4489
social-content-creation is a skill published in the GitHub repository cdeistopened/skill-stack (30 stars, last pushed 3mo ago), licensed MIT. It adds 70 tokens to every session and 3,394 once invoked, about $0.0003 per session on Opus 5.5. 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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