plugin/skills/tooluniverse-clinical-guidelines/SKILL.md
Search and retrieve clinical practice guidelines from 12+ authoritative sources — NICE, WHO, NCCN, AHA, ADA, SIGN, USPSTF, IDSA, NIH consensus, ESMO/ESC/EASL European societies, and US specialty associations. Use for evidence-graded treatment recommendations, dosing protocols, screening guidance, and authoritative-source-prioritized clinical guidance (NICE/WHO ranked above society guidelines).
npx skillsauth add mims-harvard/tooluniverse tooluniverse-clinical-guidelinesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Not all guidelines carry equal weight. Evaluate sources in this order:
Always check publication date. A 2015 guideline may be superseded by a 2024 update. When presenting results, include the year prominently and note if newer guidance may exist.
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
[condition] guideline [year].Always query a minimum of 3 databases to catch guidelines that one source may miss. Prioritize: NICE > GIN > TRIP > Society-specific > Literature databases.
After identifying relevant guidelines from search results, use full-text tools to get recommendation details before synthesizing.
When a clinical question asks "which test should be ordered?" or "what is the most appropriate next diagnostic step?", apply this reasoning framework BEFORE searching guidelines.
Diagnostic tests serve different purposes. Identify which one the question demands:
Ask: "What piece of information am I MISSING that would change management?"
| Scenario | Prioritize | Reasoning | |----------|-----------|-----------| | Ruling OUT a dangerous condition | High sensitivity | A negative result reliably excludes the disease | | Confirming before invasive treatment | High specificity | A positive result reliably confirms the disease | | Differentiating two similar conditions | Test unique to one | Choose marker present in condition A but absent in condition B | | Emergency with life-threatening DDx | Fastest available test | Speed trumps perfect accuracy in acute settings |
Guidelines give population-level recommendations. When presenting findings:
| Tool | Key Parameters | Notes |
|------|---------------|-------|
| NICE_Clinical_Guidelines_Search | query, limit (both required) | Best general source; returns list directly |
| GIN_Guidelines_Search | query, limit (both required) | Best multi-society aggregator |
| TRIP_Database_Guidelines_Search | query, limit, search_type='guidelines' (all required) | Must include search_type |
| WHO_Guidelines_Search | query, limit | Limited topic filtering; may return unrelated WHO docs |
| CMA_Guidelines_Search | query, limit | Canadian guidelines |
| SIGN_search_guidelines | query (NOT q), limit | Scottish/UK |
| CTFPHC_search_guidelines | query (NOT q), limit | Canadian prevention |
| OpenAlex_Guidelines_Search | query, limit, optional year_from/year_to | Academic publications |
| EuropePMC_Guidelines_Search | query, limit | Loosely relevant; use for discovery |
| PubMed_Guidelines_Search | query, limit, optional api_key | Literature fallback |
All general search tools return lists directly — access as result[0]['title'].
ADA (Diabetes)
ADA_list_standards_sections() — No params. Lists all sections of ADA Standards of Care.ADA_search_standards(query, limit) — Use broad medical terms, not specific drug names.ADA_get_standards_section(section_number) — Returns section abstract only.AHA/ACC (Cardiology)
AHA_ACC_search_guidelines(query, limit) — Search AHA/ACC guidelines.AHA_list_guidelines(limit) / ACC_list_guidelines(limit) — List recent.AHA_ACC_get_guideline(pmid) — Full text via PMC.NCCN (Oncology)
NCCN_list_patient_guidelines(limit) — Field is cancer_type, NOT title.NCCN_search_guidelines(query, limit) — Returns JNCCN abstracts, not proprietary text.NCCN_get_patient_guideline(url) — Pass full URL string, NOT an integer ID.MAGICapp (Living Guidelines)
MAGICapp_list_guidelines(limit) — Returns dict: use r.get('data', []). Field is name, NOT title.MAGICapp_get_guideline(guideline_id) / MAGICapp_get_recommendations(guideline_id) / MAGICapp_get_sections(guideline_id)NCI — Catalogs research tools/datasets, NOT clinical guidelines. Use q (not query), size (not limit). Access: r.get('data',{}).get('results',[]).
All CPIC tools return dict-wrapped: use r.get('data', []).
Workflow:
CPIC_get_gene_info(genesymbol='CYP2D6') — Gene overviewCPIC_get_gene_drug_pairs(genesymbol='CYP2D6') — All drugs with CPIC levels (A=strongest)CPIC_list_guidelines(limit=50) — Find guidelineId for target gene+drug pairCPIC_get_recommendations(guideline_id=N) — Dosing recommendations (deduplicate by phenotype)CPIC_get_alleles(genesymbol='CYP2D6') — Use clinicalfunctionalstatus (NOT functionalstatus)Gotchas:
CPIC_get_recommendations takes guideline_id (integer), NOT genesymbolCPIC_search_gene_drug_pairs requires PostgREST syntax: genesymbol='eq.CYP2D6'| Source | Tool | Input |
|--------|------|-------|
| NICE | NICE_Guideline_Full_Text(url) | URL from search results; try .../chapter/Recommendations |
| WHO | WHO_Guideline_Full_Text(url) | May return PDF link, not full text |
| AHA/ACC | AHA_ACC_get_guideline(pmid) | PMID from search results |
| NCCN | NCCN_get_patient_guideline(url) | Full URL from list results |
| System | Strong | Moderate | Weak/Expert Opinion | |--------|--------|----------|-------------------| | ADA | Grade A | Grade B/C | Grade E (consensus) | | AHA/ACC | Class I | Class IIa/IIb | Class III | | SIGN | Strong | Conditional | Good practice point | | CPIC | Level A | Level B | Level C/D | | NICE | "Offer" (strong) | "Consider" (weaker) | Research recommendation |
'pharmacologic approaches' not 'metformin first-line')# Clinical Guidelines: [Topic]
## Summary
[2-3 sentences: what do the guidelines agree on? Where do they diverge?]
## Key Recommendations
### [Source 1 — Organization, Year]
- Recommendation text [Evidence grade]
- URL
### [Source 2 — Organization, Year]
- Recommendation text [Evidence grade]
## Patient-Specific Considerations
[Comorbidities, interactions, or population factors that modify these recommendations]
## Pharmacogenomics (if applicable)
[CPIC phenotype-to-dosing table, deduplicated]
## References
[All source URLs]
Spine triage -- use TLICS scoring, not gestalt:
Hemorrhagic shock -- fluid selection:
Brown-Sequard -- lesion level determination:
Post-valve surgery monitoring:
Incidental findings:
Post-surgical complications:
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Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
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