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 yniantongtian-oss/nature-skills --skill nature-paper2pptgit clone --depth 1 https://github.com/yniantongtian-oss/nature-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/yniantongtian-oss/nature-skills/nature-paper2ppt)<a href="https://agentmods.dev/skills/yniantongtian-oss/nature-skills/nature-paper2ppt"><img src="https://agentmods.dev/badge/skills/yniantongtian-oss/nature-skills/nature-paper2ppt/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/yniantongtian-oss/nature-skills/nature-paper2ppt"><img src="https://agentmods.dev/badge/skills/yniantongtian-oss/nature-skills/nature-paper2ppt.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 | $0.00146 | $0.01113 |
| Opus 5 | $0.00073 | $0.00557 |
| Sonnet 5 | $0.00029 | $0.00223 |
| Haiku 4.5 | $0.00015 | $0.00111 |
Grade A, and why
nature-paper2ppt 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
86% identical to nature-paper2ppt — 12 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper-to-PPTX — Router
This skill is split into two layers:
- A static layer under
static/that holds versioned, reusable content fragments (core principles, toolchain policy, the 9-step workflow, output/quality rules, and per-paper-type presentation arcs). - A dynamic layer (this file plus
manifest.yaml) that detects the paper type and loads only the fragments needed for the current job. Deep design, figure, and self-review material lives in on-demand references.
Do not try to apply the deck-building logic from memory or from this router. Always load fragments from disk as described below.
Routing protocol
Follow these five steps every time the skill is invoked.
1. Load the manifest and the core layer
Read manifest.yaml. It declares the paper_type axis, the allowed values, and the file paths each value maps to.
Also read every file listed under always_load. These hold the purpose and core principle, the lean operating mode and toolchain policy, the 9-step workflow spine, and the output/quality rules that apply to every deck, plus the shared Terminology Ledger used to keep technical terms consistent across slides.
2. Classify the paper type
Decide the paper_type value using the manifest's detect: hint and the source:
discovery— discovery / mechanism papers (question-to-evidence arc). Default.methods— methods / AI / tool / algorithm papers (problem-to-solution arc).resource— resource / dataset / atlas / omics / benchmark papers (workflow-to-validation arc).clinical— clinical / population / intervention studies (design-to-inference arc).materials— materials / chemistry / physics / engineering papers (property-to-mechanism / design-to-performance arc).review— reviews / perspectives / commentaries / meta-analyses (evidence-map arc).
State the detected value in one short line to the user before designing slides, so they can correct you cheaply.
3. Load the matching fragment
What ships with it
15 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.
- manifest.yaml 2.5 KB
- README.md 5.8 KB
- references/design-and-layout.md 11 KB
- references/figure-assets.md 3.1 KB
- references/self-review.md 5.5 KB
- static/core/output-and-quality.md 4.2 KB
- static/core/principles.md 2.9 KB
- static/core/toolchain.md 2.5 KB
- static/core/workflow.md 5.3 KB
- static/fragments/paper_type/clinical.md 1.2 KB
- static/fragments/paper_type/discovery.md 1.3 KB
- static/fragments/paper_type/materials.md 1.2 KB
- static/fragments/paper_type/methods.md 1.2 KB
- static/fragments/paper_type/resource.md 1.1 KB
- static/fragments/paper_type/review.md 1.0 KB
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 · 72 lines · 146 tokens per session scan A ae01315f6021
nature-paper2ppt is a skill published in the GitHub repository yniantongtian-oss/nature-skills (24 stars, last pushed 3mo ago), licensed MIT. It adds 146 tokens to every session and 1,113 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to nature-paper2ppt, differing in 12 lines, and is treated as a copy.
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