Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/notque/vexjoy-agentnpx agentmods add skills/notque/vexjoy-agent/content-engineWrote 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/notque/vexjoy-agent/content-engine)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/content-engine"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/content-engine/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/notque/vexjoy-agent/content-engine"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/content-engine.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 | $0.00013 | $0.01871 |
| Opus 5.5 | $0.00005 | $0.00748 |
| Sonnet 5.5 | $0.00003 | $0.00374 |
| Haiku 4.5 | $0.00001 | $0.00187 |
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 20d 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Engine Skill
Repurpose anchor content into platform-native variants. This skill produces drafts only — it does not make API calls or publish content. Posting is handled downstream by x-api (single platform) or crosspost (multi-platform).
Platform-native means each variant is written from scratch for its target platform: different register (conversational on X, professional-but-human on LinkedIn, punchy on TikTok), different structure (thread vs. long-form post vs. short script vs. newsletter section), and different hook style (open fast on X, strong first line on LinkedIn, interrupt on TikTok). Shortening the same text for each platform is not adaptation — it produces content that reads identically everywhere and fails on every platform.
Instructions
Phase 1: GATHER — Collect Inputs Before Writing Anything
Establish everything needed to write platform-native variants. Do not begin writing until this phase is complete.
Required inputs:
| Input | Description | If Missing |
|---|---|---|
| Source asset | The content being adapted (article text, demo description, launch doc, insight, transcript) | Ask — required |
| Target platforms | X, LinkedIn, TikTok, YouTube, newsletter — one or many | Ask if not inferable from context |
| Audience | Builders, investors, customers, operators, general | Infer if a strong signal exists; ask if ambiguous |
| Goal | Awareness, conversion, recruiting, authority, launch support, engagement | Infer from source if obvious; ask otherwise |
| Constraints | Character limits already observed, brand voice notes, phrases to avoid | Skip if none stated |
Acquiring a transcript source asset: When the source asset is a video URL (YouTube, etc.) rather than text, extract the transcript first:
python3 scripts/video-transcript.py transcript "<video-url>" # add --timestamps or --json -o file as needed
Use the extracted transcript as the source asset for the rest of this phase.
What ships with it
3 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.
- 20d ago Changed · +125 lines · -5 tokens per session 49626f4aca12
- 26d ago Changed · -125 lines · +5 tokens per session 64532d12bd84
- 27d ago Changed · -17 lines 5d46ac2f5ed3
- 1mo ago First seen · 175 lines · 13 tokens per session scan A 1fb0f94a6033
content-engine is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 1,871 once invoked, about $0.0001 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 2026-09-03.
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