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 Nero1688/claude-academic-skills --skill academic-postergit clone --depth 1 https://github.com/Nero1688/claude-academic-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/nero1688/claude-academic-skills/academic-poster)<a href="https://agentmods.dev/skills/nero1688/claude-academic-skills/academic-poster"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/academic-poster/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/nero1688/claude-academic-skills/academic-poster"><img src="https://agentmods.dev/badge/skills/nero1688/claude-academic-skills/academic-poster.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 | $0.00298 | $0.01491 |
| Opus 5.5 | $0.00119 | $0.00596 |
| Sonnet 5.5 | $0.00060 | $0.00298 |
| Haiku 4.5 | $0.00030 | $0.00149 |
Grade A, and why
academic-poster 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 18d 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.
What it actually says
學術海報產生器(Academic Poster)
Step 1|情境與規格(一次問完)
- 研討會規定:尺寸(A0 直式最常見/A1/自訂)、直式或橫式、有無模板強制。
- 素材:論文全文或摘要?圖表現成的有哪些?(需要新圖 → 轉 management-figure)
- 版式哲學二選一(給使用者看差異再選):
- 傳統三欄式:完整敘事(摘要→方法→結果→結論),資訊量大,適合保守場合 與需要細節的方法論社群。
- Better Poster 式:中央大字放主發現一句話+QR code 連完整論文,兩側精簡 支撐,適合大型國際會議的注意力競爭。(理念source:Mike Morrison Better Poster 運動)
- 語言與機構識別:中/英、校徽、合作機構 logo、聯絡方式與 QR。
- 風格參考見
academic-pptx的references/academic-style-catalog.md(poster-session-talk 節)。
Step 2|內容重組(海報不是論文縮印)
- 一句話主發現先寫出來(≤20字中文/15 words英文),它決定整張海報——寫不出來 就回頭找 academic-pptx 的內容決策流程。
- 三層閱讀動線分配內容:
層 停留時間 元素 字級(A0) 吸睛層 3–10 秒 標題+主發現大字+核心圖 標題 ≥85pt,主發現 ≥60pt 瀏覽層 1 分鐘 各區塊標題+圖表+bullet 區標 ≥48pt,內文 ≥28pt 深讀層 5 分鐘 方法細節、模型設定、參考文獻 ≥24pt,絕不小於 20pt - 文字總量紅線:A0 全版 ≤800 字(中文)/≤600 words;超過就砍,砍不動就進 QR。
- 表格慎用:迴歸全表是海報毒藥,改用係數森林圖或只列關鍵係數卡(β、SE、星號)。
Step 3|產出(.pptx 單頁大版面)
用 pptx 技術產單頁海報(page size 直接設為海報實際尺寸,如 A0 = 84.1×118.9 cm):
- 版面規格、色彩、印刷注意事項讀
references/poster-layout.md。 - 實際產出用
scripts/poster_scaffold.py:填好templates/poster-spec.json(欄位說明見範例檔本身)後執行python scripts/poster_scaffold.py --spec poster-spec.json -o poster.pptx; 該腳本會依layout(three-column/better-poster)自動排版、套用三層閱讀動線字級門檻, 並確保中文 run 正確帶有東亞字型(標楷體)。可用--verify模式讀回檢查尺寸與字級是否過關。 - 海報上的圖不直接縮放期刊圖:依
references/poster-layout.md「海報上的圖」以欄寬實寸重出 (字級達瀏覽層、精簡層級、圖下交代省略了什麼、全張一個強調色)。 - 產出後縮圖自檢:縮到 10% 大小看——若主發現讀不到,吸睛層失敗,重排。
- 交付雙格式:.pptx(可編輯)+ 轉 PDF(送印),並提醒印前確認出血與 CMYK 問題 (校內輸出中心通常收 PDF 即可)。
紅線
- 主發現大字必須是論文真實結論,不誇大(「顯著正向調節」不能寫成「證明了」)。
- 字級紅線(深讀層 ≥20pt)不可為塞內容而破——塞不下是內容問題不是字級問題。
- 校徽與機構識別遵守學校 CI 規範;合作者掛名順序由使用者定,不代決。
What ships with it
4 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.
- 18d ago Changed · +2 lines b56737f3214f
- 1mo ago Changed · +5 lines fe9a2c5590d4
- 1mo ago First seen · 47 lines · 298 tokens per session scan A bb2d421a8025
academic-poster is a skill published in the GitHub repository Nero1688/claude-academic-skills (13 stars, last pushed 5d ago), licensed MIT. It adds 298 tokens to every session and 1,491 once invoked, about $0.0012 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 2026-08-31.
Other skills, from other repositories
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…
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…
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-deeptools
Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles…
alterlab-esm
Run ESM protein language models — ESMC for embeddings and representations, ESMFold2 for structure prediction, and ESM3 for generative multimodal protein design across sequence, structure, and function — locally or through the hosted Biohub Platform API (formerly Forge). Use when working with protein sequences…