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 agentmods add skills/mturac/everything-openai-codex/content-enginenpx skills add mturac/everything-openai-codex --skill content-enginegit clone --depth 1 https://github.com/mturac/everything-openai-codexWhat 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 | $0.00055 | $0.00936 |
| Opus 5 | $0.00028 | $0.00468 |
| Sonnet 5 | $0.00011 | $0.00187 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
content-engine 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 2d 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
95% identical to content-engine — 2 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine
Build platform-native content without flattening the author's real voice into platform slop.
When to Activate
- writing X posts or threads
- drafting LinkedIn posts or launch updates
- scripting short-form video or YouTube explainers
- repurposing articles, podcasts, demos, docs, or internal notes into public content
- building a launch sequence or ongoing content system around a product, insight, or narrative
Non-Negotiables
- Start from source material, not generic post formulas.
- Adapt the format for the platform, not the persona.
- One post should carry one actual claim.
- Specificity beats adjectives.
- No engagement bait unless the user explicitly asks for it.
Source-First Workflow
Before drafting, identify the source set:
- published articles
- notes or internal memos
- product demos
- docs or changelogs
- transcripts
- screenshots
- prior posts from the same author
If the user wants a specific voice, build a voice profile from real examples before writing.
Use brand-voice as the canonical workflow when voice consistency matters across more than one output.
Voice Handling
brand-voice is the canonical voice layer.
Run it first when:
- there are multiple downstream outputs
- the user explicitly cares about writing style
- the content is launch, outreach, or reputation-sensitive
Reuse the resulting VOICE PROFILE here instead of rebuilding a second voice model.
If the user wants mehet-turac / ecc voice specifically, still treat brand-voice as the source of truth and feed it the best live or source-derived material available.
Hard Bans
Delete and rewrite any of these:
- "In today's rapidly evolving landscape"
- "game-changer", "revolutionary", "cutting-edge"
- "here's why this matters" unless it is followed immediately by something concrete
- ending with a LinkedIn-style question just to farm replies
- forced casualness on LinkedIn
- fake engagement padding that was not present in the source material
Platform Adaptation Rules
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.
- 2d ago First seen · 131 lines · 55 tokens per session scan A 614c03e87a06
content-engine is a skill published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 9d ago), licensed MIT. It adds 55 tokens to every session and 936 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to content-engine, differing in 2 lines, and is treated as a copy.
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xhs_note
小红书图文创作 / 笔记 / 种草文案 (xiaohongshu / red note) — 端到端：成文→配图(≥3 张竖版)→去AI化→在线预览打包交付。以图为主、文字辅助：标题四件套 + 碎句正文 + 话题标签，配 3:4 竖版卡片，最少 3 张图。honors user persona & style memory.
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