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 Boom5426/Nature-Paper-Skills --skill academic-presentationsgit clone --depth 1 https://github.com/Boom5426/Nature-Paper-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/boom5426/nature-paper-skills/academic-presentations)<a href="https://agentmods.dev/skills/boom5426/nature-paper-skills/academic-presentations"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/academic-presentations/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/boom5426/nature-paper-skills/academic-presentations"><img src="https://agentmods.dev/badge/skills/boom5426/nature-paper-skills/academic-presentations.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 | $0.00076 | $0.01685 |
| Opus 5 | $0.00038 | $0.00843 |
| Sonnet 5 | $0.00015 | $0.00337 |
| Haiku 4.5 | $0.00008 | $0.00169 |
Grade A, and why
academic-presentations 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 13d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Presentations
Produce slide decks (and optionally narrated demo videos) from research papers. The human drives all outline and visual decisions — the agent executes.
Pipeline
[1] Script Draft ──→ [2] Slide Generation ──→ [3] TTS Audio (optional) ──→ [4] Video Assembly (optional)
Claude Code nanobanana /edit edge-tts / Kokoro / ElevenLabs ffmpeg
Skip stages 3–4 for slide-only output. User can enter at any stage.
Stage 1: Script / Outline
Input: paper + user-provided outline or slide plan
Output: video-scripts.md or slide-outline.md — per-slide content with talking points
The agent drafts scripts based on the user's outline. The user owns the structure — agent does not decide slide count, order, or what to emphasize.
Stage 2: Slide Generation
Full reference: references/slide-generation.md
Tool: nanobanana (Gemini CLI extension)
Priority order (edit-first):
- Has paper figure → nanobanana
/editto wrap into slide frame - Has existing slide →
/editto adapt - User-provided reference (e.g., from NotebookLM or PPTX the user made) →
/editto refine - Title slide from scratch → generate with academic style prompt
- Content slide from scratch → generate with deck-style preamble
Key principle: prefer /edit on existing HQ paper figures over generating from scratch.
Deck style: create deck-style.md once per deck, prepend to all generate-from-scratch prompts. For /edit, style is inherited from the base image.
Example deck-style.md:
- Canvas: 1920x1080, white background
- Accent: #2563EB blue, text: #1e293b dark slate
- Clean sans-serif, flat design, no gradients/shadows
- Bottom bar: blue accent with white affiliation text
Stage 3: TTS Audio (optional)
Full reference: references/tts-engines.md Batch scripts: scripts/batch_tts_edge.py, scripts/batch_tts_kokoro.py
What ships with it
5 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.
- 13d ago First seen · 149 lines · 76 tokens per session scan A fce9bcbd47c2
academic-presentations is a skill published in the GitHub repository Boom5426/Nature-Paper-Skills (490 stars, last pushed 7d ago), licensed MIT. It adds 76 tokens to every session and 1,685 once invoked, about $0.0004 per session on Opus 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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