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 oyi77/1ai-skills --skill paperpodgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/paperpod)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/paperpod"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/paperpod/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/oyi77/1ai-skills/paperpod"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/paperpod.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 | $0.00064 | $0.00822 |
| Opus 5.5 | $0.00026 | $0.00329 |
| Sonnet 5.5 | $0.00013 | $0.00164 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
paperpod 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paperpod
When to Use
Trigger phrases:
- "paperpod"
- "Help me with paperpod"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
Isolated, agent-native sandboxes for code execution, live preview URLs, browser automation, 50+ tools (ffmpeg, sqlite, pandoc, imagemagick), LLM inference, and persistent memory — all via CLI or HTTP, no SDK or API keys required. Billed per second of compute usage and topup via stripe or x402.
When NOT to Use
- When the content requires deep domain expertise you do not have
- For legal, medical, or financial advice content
- When real-time data is required (use live data feeds)
Overview
Paperpod enables content production with professional quality and consistency.
Workflow
# Example: Content generation pipeline
def generate_content(topic: str, format: str = "article"):
outline = create_outline(topic)
draft = write_draft(outline, format)
edited = edit_for_quality(draft)
optimized = optimize_for_seo(edited)
return publish(optimized)
- Define brief — Set objectives, audience, and style guidelines
- Research and gather — Collect source material and reference content
- Create draft — Generate initial content following the brief
- Refine and edit — Polish for quality, accuracy, and engagement
- Publish and distribute — Deploy to target platforms
- Track performance — Monitor engagement and iterate
Quality Checklist
- Content matches the defined brief and audience
- All facts verified against authoritative sources
- Formatting consistent with style guidelines
- SEO/distribution optimization applied
- Call-to-action clear and compelling
Tools
- Content management system for publishing
- Analytics platform for performance tracking
- Design tools for visual assets
- Collaboration tools for review cycles
Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "Good enough content works" | Quality content drives engagement. Mediocre content gets ignored. |
| "I will optimize later" | SEO and distribution need optimization from the start. |
| "Templates are good enough" | Templates are a starting point. Custom content outperforms generic. |
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 · 115 lines · 64 tokens per session scan A c5253be51aef
paperpod is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 822 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 2026-09-03.
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