Borrowing it
Nothing to install: this file belongs to u9401066/rootcause-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/rootcause-mcp/master/.codex/skills/academic-figure-drawing-harness/SKILL.mdgit clone --depth 1 https://github.com/u9401066/rootcause-mcpWrote 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/u9401066/rootcause-mcp/academic-figure-drawing-harness)<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/academic-figure-drawing-harness"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/academic-figure-drawing-harness/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/u9401066/rootcause-mcp/academic-figure-drawing-harness"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/academic-figure-drawing-harness.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 | $0.00044 | $0.00698 |
| Opus 5 | $0.00022 | $0.00349 |
| Sonnet 5 | $0.00009 | $0.00140 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
academic-figure-drawing-harness 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 10d 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
100% identical to academic-figure-drawing-harness — 1 line 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.
What it actually says
Academic Figures MCP: Codex Drawing Harness
這個技能專為整合 Codex 原生繪圖能力與 MCP 提供的 Gemini 圖像生成、PubMed 學術圖表檢索而設計。
核心策略 (Core Strategies)
-
原生與生成結合 (Hybrid Rendering)
- 結構化與流程圖: 優先使用 Codex 原生的 Markdown mermaid 語法、SVG 或 Python matplotlib/plotly 繪製精確的架構圖、長條圖、散佈圖與數據模型。
- 複雜醫學/生物插圖: 使用 MCP 工具（如 mcp_academic-figu_generate_figure 或 Gemini tools）生成高度複雜的 3D 解剖圖、顯微組織圖、或不規則概念圖。
- 雙重驗證: 如果需要，使用 mcp_academic-figu_plan_figure 來規劃最適切的呈現路由（決定應該用程式繪圖還是 AI 生成）。
-
學術級距要求 (Citation-Ready & Provenance)
- 引用的出處必須準確，可利用 PubMed 工具 unified_search 或 get_article_figures 索取參考來源或範例圖片。
- 生成的文字標題 (Caption) 必須符合學術期刊規範 (包含圖號、簡短標題、詳細說明、與 PMID 出處)。
-
編輯與優化 (Iterative Refinement)
- 當產生初步圖表後，利用互動工具 mcp_academic-figu_evaluate_figure (8 維度品質評估) 或 mcp_academic-figu_edit_figure 根據使用者回饋進行微調 (例如改顏色、調佈局)。
- 原生 SVG/Mermaid 代碼請直接利用 Codex 的 text edit 能力與 replace_string_in_file 重構並更新。
操作流程 (Workflow)
- Step 1: 規劃圖表 (Plan) 取得需求或文獻內容後，分析合適的圖表類型 (figure_type: flowchart, mechanism, comparison, data_visualization 等)。
- Step 2: 選擇路由 (Route Selection)
- 若能用程式化表達 (如統計數據)，引導 Codex 撰寫 Python 腳本或 Mermaid。
- 若需精美點陣圖，呼叫 generate_figure。
- Step 3: 附加上下文與組裝 (Assemble)
產生最終圖片的 Markdown 嵌入格式
，並加上完整的學術來源與 PICO/MeSH 背景說明。
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.
- 10d ago First seen · 35 lines · 44 tokens per session scan A 31946190f65b
academic-figure-drawing-harness is a skill published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 8d ago), licensed Apache-2.0. It adds 44 tokens to every session and 698 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to academic-figure-drawing-harness, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
ai-figure-generation
Generate scientific figures, diagrams, and illustrations using AI image models (DALL-E, Midjourney, Stable Diffusion) from research content. Use when creating visuals for slides, papers, or posters. Converts technical concepts into publication-ready imagery through structured prompts.
paper-illustration-image2
Generate publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to paper-illustration, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.
annotating-variants
Annotates VCF variants and normalizes HGVS nomenclature with public, license-free annotators (Ensembl VEP REST, VEP/SnpEff/ANNOVAR offline) and links variants to gnomAD population frequencies and the clinical context OpenMed extracts. Use when the user wants to predict variant consequences, map HGVS to genomic…
batch-processing-clinical-text
Run large-scale batch NER, PII extraction, or de-identification over many clinical notes on-device with OpenMed, with sharding, checkpointing, resumability, and append-only JSONL output. Use when the user needs to process a corpus or folder of notes, de-identify a dataset, run NER over thousands of documents, build a…
coding-hcc-risk-adjustment
Maps chronic conditions extracted by OpenMed to CMS-HCC V28 risk-adjustment categories and estimates a RAF (Risk Adjustment Factor) score as decision support. Use when the user wants to surface risk-adjustable diagnoses from notes, map ICD-10-CM codes to HCC categories, estimate or reconcile a patient/panel RAF, find…
coding-icd10
Suggests candidate ICD-10-CM diagnosis codes (and ICD-10-PCS procedure codes) for diagnoses and procedures extracted by OpenMed, with rationale and a human-coder caveat. Use when the user wants to code a problem list, map a diagnosis span to a billable ICD-10-CM code, route a finding to the right chapter, cross-walk…