菜单
Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Transforms research and knowledge base material into information-dense infographic copy—clear, scannable, authoritative, and reference-worthy. Use when converting clinical algorithms, decision trees, comparison data (drug vs drug, treatment options), trial results, myth vs truth content, patient preparation guides, checklists, or sequential process workflows into structured copy ready for single-image infographic formats. Supports multiple infographic types including hero stats, dense multi-section layouts, side-by-side comparisons, myth-busters, step-by-step processes, and patient checklists. Outputs structured copy with compressed noun-heavy phrasing, specific numbers, trial citations, and JSON-formatted handoff data for downstream infographic generation.
Manages all other skills by detecting capability gaps, proposing new skills, and keeping the system registry in sync. Use when the user encounters something the system cannot do, wants to log a capability gap, review the skill backlog, propose or build a new skill, analyze recurring unmet needs, or run sync pipelines to update context files (CLAUDE.md, GEMINI.md, AGENTS.md) after skill changes.
Template-driven medical infographics using the AntV Infographic framework. Use when creating structured infographics from clinical data, trial results, or patient education content using pre-built templates for hero stats, comparison layouts, process flows, myth-busters, and checklists. Outputs publication-ready HTML/PNG infographics with JACC/NEJM-grade design tokens.
Declarative, grammar-based charting using AntV G2 for complex medical visualizations. Use when creating forest plots, Kaplan-Meier curves, funnel plots, waterfall charts, or any statistical/clinical chart that requires grammar-of-graphics composition with medical-journal-grade styling and accessibility-validated color palettes.
Publication-grade design tokens and utilities for Nature/JACC/NEJM quality graphics. Use when generating charts, infographics, animations, or any visual content that requires medical-journal-grade color palettes, typography, accessibility-validated contrast ratios, and consistent branding. Provides Python token APIs, colorblind-safe palettes, G2 chart templates, AntV infographic templates, Vizzu animation presets, and Manim integration for animated medical explainers.
Content OS orchestrator - the master skill that produces ALL content types from one seed idea (forward mode) or splits long-form content into short-form pieces (backward mode). Invokes research, writing, quality review, and visual generation skills in a coordinated pipeline. Long-form content goes through full quality gates; short-form gets quick accuracy pass.
| name | quick-topic-researcher |
| description | Rapid topic mastery for video/content prep. Takes a topic → generates 5 research questions → parallel PubMed + web search → outputs McKinsey-style brief in 5 minutes. Use BEFORE recording videos or writing content. |
5 minutes to topic mastery. This skill generates a focused research brief you can use immediately before recording a video or writing content.
Different from deep-researcher: That skill is comprehensive (5+ sources, file-based, 30+ minutes). This skill is FAST (5 questions, parallel search, 5 minutes).
| Use Case | This Skill |
|---|---|
| Prepping for a YouTube video | Yes |
| Writing a quick tweet thread | Yes |
| Refreshing knowledge on a topic | Yes |
| Before a podcast discussion | Yes |
| Comprehensive literature review | No → Use deep-researcher |
| Writing a formal editorial | No → Use deep-researcher first |
TOPIC: "GLP-1 agonists in heart failure"
DOMAIN: "Cardiology"
│
▼
┌─────────────────────────────────────────────────────┐
│ STEP 1: Generate 5 Research Questions │
│ │
│ 1. Do GLP-1 agonists reduce heart failure │
│ hospitalization in diabetic patients? │
│ 2. Is there evidence of direct cardiac benefit? │
│ 3. What are the key trials showing CV outcomes? │
│ 4. Are there safety concerns in existing HF? │
│ 5. What do current guidelines recommend? │
└─────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ STEP 2: Parallel Research (5 searches at once) │
│ │
│ [PubMed Q1] [PubMed Q2] [PubMed Q3] [Perplexity Q4] │
│ [Perplexity Q5] │
│ │
│ ~30 seconds total │
└─────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ STEP 3: McKinsey-Style Brief │
│ │
│ EXECUTIVE SUMMARY │
│ • Key finding with strongest PMID │
│ │
│ ANALYSIS │
│ • Theme 1: Trial evidence (PMIDs) │
│ • Theme 2: Mechanisms (PMIDs) │
│ • Theme 3: Guidelines │
│ │
│ CLINICAL IMPLICATIONS │
│ • What this means for your content │
│ │
│ KEY PMIDS TO CITE │
│ • List of 5-7 citation-ready references │
└─────────────────────────────────────────────────────┘
Ask Claude:
Use quick-topic-researcher for [TOPIC] in [DOMAIN]
Example:
Use quick-topic-researcher for "SGLT2 inhibitors in CKD" in "Cardiology/Nephrology"
python skills/cardiology/quick-topic-researcher/scripts/quick_research.py \
--topic "GLP-1 agonists in heart failure" \
--domain "Cardiology"
| Source | Tool | Purpose |
|---|---|---|
| PubMed MCP | pubmed_search_articles, pubmed_fetch_contents | All medical evidence |
| Guidelines | Direct URL fetch to ACC/ESC/ADA | Recommendations |
| Source | Tool | Purpose |
|---|---|---|
| Perplexity | perplexity_ask via MCP | Quick context, trend discovery |
| Web Search | WebSearch | Background, non-medical context |
Rule: You can USE Perplexity to understand context, but you CITE only PubMed.
The skill outputs a structured brief:
# Quick Research Brief: [TOPIC]
**Domain:** [DOMAIN]
**Generated:** [DATE]
**Time to Read:** 3 minutes
---
## Executive Summary
[2-3 sentences: What you need to know before recording/writing]
Key takeaway: [ONE sentence with strongest PMID]
---
## Research Questions & Findings
### Q1: [Question]
**Answer:** [Concise answer]
**Evidence:** [Study name, PMID, key stat (HR, CI, p-value)]
### Q2: [Question]
**Answer:** [Concise answer]
**Evidence:** [Study name, PMID, key stat]
[... Q3-Q5 ...]
---
## Clinical Context
### What Guidelines Say
[ACC/ESC/ADA recommendations with class/level]
### Practice Implications
[What this means for real patients]
---
## Content Hooks
For your video/content, consider these angles:
1. [Hook 1 based on findings]
2. [Hook 2 based on findings]
3. [Hook 3 based on findings]
---
## Citation-Ready References
1. Author A, et al. TRIAL-NAME. Journal. Year. PMID: XXXXXXXX
2. Author B, et al. Study Name. Journal. Year. PMID: XXXXXXXX
3. [5-7 total references]
---
## Gaps & Caveats
- [What the evidence doesn't cover]
- [Patient populations with limited data]
1. Run quick-topic-researcher on your video topic
2. Review the brief (3 min read)
3. Open youtube-script-master with the brief in context
4. Script now has verified facts and PMIDs
1. Run quick-topic-researcher
2. Pick 1-2 key findings from the brief
3. Use x-post-creator-skill with specific PMIDs
1. Run quick-topic-researcher for quick take
2. If topic is complex, follow up with deep-researcher
3. Write with cardiology-newsletter-writer
User: Use quick-topic-researcher for "colchicine in cardiovascular prevention" in "Cardiology"
Claude:
I'll research this topic rapidly using PubMed and generate a brief.
Step 1: Generating 5 research questions...
Step 2: Parallel research...
[Executes 5 PubMed searches in parallel] [Fetches key article abstracts] [Searches Perplexity for context]
Step 3: Generating McKinsey-style brief...
Domain: Cardiology Generated: 2026-01-01 Time to Read: 3 minutes
Low-dose colchicine (0.5mg daily) reduces cardiovascular events in patients with established coronary artery disease through anti-inflammatory mechanisms. COLCOT and LoDoCo2 showed consistent benefit; CLEAR SYNERGY was neutral but in a different population.
Key takeaway: In stable CAD patients, colchicine 0.5mg daily reduces CV events by ~25% (HR 0.69-0.77) (PMID: 31733140, 32865377)
Answer: Yes, in stable CAD Evidence: LoDoCo2: HR 0.69 (0.57-0.83), p<0.001 for CV death/MI/stroke (PMID: 32865377)
Answer: COLCOT (post-MI), LoDoCo2 (chronic CAD), CLEAR SYNERGY (post-PCI) Evidence:
Answer: NLRP3 inflammasome inhibition, IL-1β reduction Evidence: Reduces hsCRP; targets residual inflammatory risk (PMID: 29724838)
Answer: GI side effects, myopathy with statins, CKD dose adjustment Evidence: NNH for GI: ~50; avoid with strong CYP3A4 inhibitors (PMID: 31733140)
Answer: Class IIb in ESC 2024 for chronic CAD Evidence: ESC 2024 CCS Guidelines, consider in patients with elevated hsCRP
ESC 2024: Class IIb, Level A - "Colchicine 0.5mg may be considered to reduce CV risk in chronic coronary syndrome"
Ready for your video! You now have verified facts, PMIDs, and content hooks.
| Skill | Time | Depth | Use Case |
|---|---|---|---|
| quick-topic-researcher | 5 min | Surface + key trials | Video prep, quick refresh |
deep-researcher | 30-60 min | Comprehensive | Editorials, literature review |
pubmed-database | 2 min | Single search | Specific question |
perplexity-search | 1 min | Trend only | Discovery, non-citable |
The skill uses Claude's ability to make multiple tool calls simultaneously:
# These run in parallel (single message, multiple tool calls)
pubmed_search_articles(queryTerm="colchicine cardiovascular RCT", maxResults=10)
pubmed_search_articles(queryTerm="colchicine mechanism inflammation", maxResults=5)
perplexity_ask(messages=[{"role": "user", "content": "colchicine cardiology guidelines 2024"}])
~/research_briefs/{topic}_{date}.mdThis skill gets you from "I need to know about X" to "I can confidently speak about X" in 5 minutes.