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 Lord1Egypt/scientific-agent-toolkit --skill pptx-postersgit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote 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/lord1egypt/scientific-agent-toolkit/pptx-posters)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/pptx-posters"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pptx-posters/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/lord1egypt/scientific-agent-toolkit/pptx-posters"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pptx-posters.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 | $0.00066 | $0.03586 |
| Opus 5 | $0.00033 | $0.01793 |
| Sonnet 5 | $0.00013 | $0.00717 |
| Haiku 4.5 | $0.00007 | $0.00359 |
Grade A, and why
pptx-posters 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 5d 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 pptx-posters — 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 — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PPTX Research Posters (HTML-Based)
Overview
⚠️ USE THIS SKILL ONLY WHEN USER EXPLICITLY REQUESTS PPTX/POWERPOINT POSTER FORMAT.
For standard research posters, use the latex-posters skill instead, which provides better typographic control and is the default for academic conferences.
This skill creates research posters using HTML/CSS, which can then be exported to PDF or converted to PowerPoint format. The web-based approach offers:
- Modern, responsive layouts
- Easy integration of AI-generated visuals
- Quick iteration and preview in browser
- Export to PDF via browser print function
- Conversion to PPTX if specifically needed
When to Use This Skill
ONLY use this skill when:
- User explicitly requests "PPTX poster", "PowerPoint poster", or "PPT poster"
- User specifically asks for HTML-based poster
- User needs to edit poster in PowerPoint after creation
- LaTeX is not available or user requests non-LaTeX solution
DO NOT use this skill when:
- User asks for a "poster" without specifying format → Use latex-posters
- User asks for "research poster" or "conference poster" → Use latex-posters
- User mentions LaTeX, tikzposter, beamerposter, or baposter → Use latex-posters
AI-Powered Visual Element Generation
STANDARD WORKFLOW: Generate ALL major visual elements using AI before creating the HTML poster.
This is the recommended approach for creating visually compelling posters:
- Plan all visual elements needed (hero image, intro, methods, results, conclusions)
- Generate each element using scientific-schematics or Nano Banana Pro
- Assemble generated images in the HTML template
- Add text content around the visuals
Target: 60-70% of poster area should be AI-generated visuals, 30-40% text.
CRITICAL: Poster-Size Font Requirements
⚠️ ALL text within AI-generated visualizations MUST be poster-readable.
When generating graphics for posters, you MUST include font size specifications in EVERY prompt. Poster graphics are viewed from 4-6 feet away, so text must be LARGE.
What ships with it
7 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.
- assets/poster_html_template.html 5.5 KB
- assets/poster_quality_checklist.md 11 KB
- references/poster_content_guide.md 20 KB
- references/poster_design_principles.md 22 KB
- references/poster_layout_design.md 22 KB
- scripts/generate_schematic_ai.py 32 KB runs code
- scripts/generate_schematic.py 5.0 KB runs code
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.
- 5d ago First seen · 415 lines · 66 tokens per session scan A 07972eb4c735
pptx-posters is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (2 stars, last pushed 3mo ago), licensed MIT. It adds 66 tokens to every session and 3,586 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 pptx-posters, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
graphical-abstract-generator
Converts a biomedical study storyline into a graphical abstract and, when direct image capability is available, generates the graphical abstract directly; otherwise it falls back to prompts, Mermaid flowcharts, or designer-facing briefs.
slide-deck-for-lab-meeting
Structures research progress into focused and actionable slides for lab meetings or project reviews without inventing missing content.
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-deep-research
Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…