tooluniverse-drug-research
Generates evidence-based research reports on drugs, mechanisms, and clinical trials.
Install
mkdir -p .claude/skills/tooluniverse-drug-research && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1959" && unzip -o skill.zip -d .claude/skills/tooluniverse-drug-research && rm skill.zipInstalls to .claude/skills/tooluniverse-drug-research
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.Key capabilities
- →Resolve chemical compound identifiers
- →Grade scientific evidence with T1-T4 scores
- →Trace drug mechanism of action
- →Compile full clinical/safety profiles
How it works
Queries clinical and chemical databases to aggregate information, applying evidence grading and section-specific completion logic.
Inputs & outputs
When to use tooluniverse-drug-research
- →Generating a drug mechanism report
- →Finding clinical trial status for a compound
- →Assembling safety data for regulatory research
About this skill
Drug Research Strategy
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Compound disambiguation FIRST - Resolve identifiers before research
- Citation requirements - Every fact must have inline source attribution
- Evidence grading - Grade claims by evidence strength (T1-T4)
- Mandatory completeness - All sections must exist, even if "data unavailable"
- English-first queries - Always use English drug/compound names in tool calls, even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
LOOK UP, DON'T GUESS
When asked about a drug, query ChEMBL/PubChem/DailyMed FIRST. Don't guess at mechanism, targets, or side effects — look them up. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.
Drug Mechanism Reasoning
When investigating a drug's mechanism of action, trace the full causal chain:
- Target engagement - Which protein(s) does the drug bind, and with what affinity/selectivity?
- Molecular effect - Does binding inhibit, activate, or modulate the target's function?
- Pathway consequence - Which signaling or metabolic pathway is altered downstream?
- Cellular phenotype - What changes occur at the cell level (proliferation, apoptosis, secretion)?
- Physiological outcome - How does the cellular effect translate to the therapeutic benefit in the patient?
Workflow Overview
1. Report-First Approach (MANDATORY)
DO NOT show the search process or tool outputs to the user. Instead:
- Create the report file FIRST -
[DRUG]_drug_report.mdwith all 11 section headers and[Researching...]placeholders. See REPORT_TEMPLATE.md for the full template. - Progressively update the report - Replace placeholders with findings as you query each tool.
- Use ALL relevant tools - Query multiple databases for each data type; cross-reference across sources.
2. Citation Requirements (MANDATORY)
Every piece of information MUST include its source. Use inline citations:
*Source: PubChem via `PubChem_get_compound_properties_by_CID` (CID: 4091)*
3. Progressive Writing Workflow
Step 1: Create report file with all section headers
Step 2: Resolve compound identifiers -> Update Section 1
Step 3: Query PubChem/ADMET-AI/DailyMed SPL -> Update Section 2 (Chemistry)
Step 4: Query FDA Label MOA + ChEMBL + DGIdb -> Update Section 3 (Mechanism)
Step 5: Query ADMET-AI tools -> Update Section 4 (ADMET)
Step 6: Query ClinicalTrials.gov -> Update Section 5 (Clinical)
Step 7: Query FAERS/DailyMed -> Update Section 6 (Safety)
Step 8: Query PharmGKB -> Update Section 7 (Pharmacogenomics)
Step 9: Query DailyMed/Orange Book -> Update Section 8 (Regulatory)
Step 10: Query PubMed/literature -> Update Section 9 (Literature)
Step 11: Synthesize findings -> Update Executive Summary & Section 10
Step 12: Document all sources -> Update Section 11 (Data Sources)
Compound Disambiguation (Phase 1)
CRITICAL: Establish compound identity before any research.
Identifier Resolution Chain
1. PubChem_get_CID_by_compound_name(compound_name)
-> Extract: CID, canonical SMILES, formula
2. ChEMBL_search_molecules(query=drug_name)
-> Extract: ChEMBL ID, pref_name
3. DailyMed_search_spls(drug_name)
-> Extract: Set ID, NDC codes (if approved)
4. PharmGKB_search_drugs(query=drug_name)
-> Extract: PharmGKB ID (PA...)
Handle Naming Ambiguity
| Issue | Example | Resolution |
|---|---|---|
| Salt forms | metformin vs metformin HCl | Note all CIDs; use parent compound |
| Isomers | omeprazole vs esomeprazole | Verify SMILES; separate entries if distinct |
| Prodrugs | enalapril vs enalaprilat | Document both; note conversion |
| Brand confusion | Different products same name | Clarify with user |
Research Paths Summary
Each path has detailed tool chains and output examples in REPORT_GUIDELINES.md.
PATH 1: Chemical Properties & CMC
Tools: PubChem properties -> ADMET-AI physicochemical -> ADMET-AI solubility -> DailyMed chemistry/description Output: Physicochemical table, Lipinski assessment, QED score, salt forms, formulation comparison
PATH 2: Mechanism & Targets
Tools: DailyMed MOA -> ChEMBL activities (NOT ChEMBL_get_molecule_targets) -> ChEMBL target details -> DGIdb -> PubChem bioactivity
Critical: Derive targets from activities filtered to pChEMBL >= 6.0. Avoid ChEMBL_get_molecule_targets.
Output: FDA MOA text, target table with UniProt/potency, selectivity profile
PATH 3: ADMET Properties
Tools: ADMET-AI (bioavailability, BBB, CYP, clearance, toxicity) Fallback: DailyMed clinical_pharmacology + pharmacokinetics + drug_interactions Critical: If ADMET-AI fails, automatically use fallback. Never leave Section 4 empty.
PATH 4: Clinical Trials
Tools: search_clinical_trials -> compute phase counts -> extract outcomes/AEs -> fda_pharmacogenomic_biomarkers Critical: Section 5.2 must show actual counts by phase/status in table format.
PATH 5: Post-Marketing Safety
Tools: FAERS (reactions, seriousness, outcomes, deaths, age) + DailyMed (DDI, dosing, warnings) Critical: Include FAERS date window, seriousness breakdown, and limitations paragraph.
PATH 6: Pharmacogenomics
Tools: PharmGKB (search -> details -> annotations -> guidelines) Fallback: DailyMed pharmacogenomics section + PubMed literature
PATH 7: Regulatory & Patents
Tools: FDA Orange Book (search, approval history, exclusivity, patents, generics) + DailyMed (special populations via LOINC codes) Note: US-only data; document EMA/PMDA limitation.
PATH 8: Real-World Evidence
Tools: ClinicalTrials.gov (OBSERVATIONAL studies) + PubMed (real-world, registry, surveillance)
PATH 9: Comparative Analysis
Tools: Abbreviated tool chains for each comparator + head-to-head trial search + PubMed meta-analyses
FDA Label Core Fields
For approved drugs, retrieve these DailyMed sections early (after getting set_id):
| Batch | Sections | Maps to Report |
|---|---|---|
| Phase 1 | mechanism_of_action, pharmacodynamics, chemistry | Sections 2-3 |
| Phase 2 | clinical_pharmacology, pharmacokinetics, drug_interactions | Sections 4, 6.5 |
| Phase 3 | warnings_and_cautions, adverse_reactions, dosage_and_administration | Sections 6, 8.2 |
| Phase 4 | pharmacogenomics, clinical_studies, description, inactive_ingredients | Sections 5, 7 |
Fallback Chains
| Primary Tool | Fallback | Use When |
|---|---|---|
PubChem_get_CID_by_compound_name | ChEMBL_search_drugs | Name not in PubChem |
ChEMBL_get_molecule_targets | Use ChEMBL_search_activities instead | Always avoid this tool |
ChEMBL_get_activity | PubChemBioAssay_get_assay_summary | No ChEMBL ID |
DailyMed_search_spls | PubChemTox_get_acute_effects | DailyMed timeout |
PharmGKB_search_drugs | DailyMed PGx sections + PubMed | PharmGKB unavailable |
PharmGKB_get_dosing_guidelines | DailyMed pharmacogenomics section | PharmGKB API error |
FAERS_count_reactions_by_drug_event | Document "FAERS unavailable" + use label AEs | API error |
ADMETAI_* (all tools) | DailyMed clinical_pharmacology + pharmacokinetics | Invalid SMILES or API error |
Quick Reference: Tools by Use Case
| Use Case | Primary Tool | Fallback | Evidence |
|---|---|---|---|
| Name -> CID | PubChem_get_CID_by_compound_name | ChEMBL_search_drugs | T1 |
| Properties | PubChem_get_compound_properties_by_CID | ADMET-AI physicochemical | T1/T2 |
| FDA MOA | DailyMed_parse_clinical_pharmacology (mechanism_of_action) | - | T1 |
| Targets | ChEMBL_search_activities -> ChEMBL_get_target | DGIdb_get_drug_info | T1 |
| ADMET | ADMETAI_predict_* (5 tools) | DailyMed PK sections | T2/T1 |
| Trials | search_clinical_trials | - | T1 |
| Trial outcomes | extract_clinical_trial_outcomes | - | T1 |
| FAERS | FAERS_count_reactions_by_drug_event | Label adverse_reactions | T1 |
| Dose mods | DailyMed_parse_clinical_pharmacology (dosage, warnings) | - | T1 |
| PGx | PharmGKB_search_drugs | DailyMed PGx + PubMed | T2/T1 |
| Label | DailyMed_search_spls | PubChemTox_get_acute_effects | T1 |
| Literature | PubMed_search_articles | EuropePMC_search_articles | Varies |
| Regulatory | FDA_OrangeBook_* tools | DailyMed label data | T1 |
See TOOLS_REFERENCE.md for the complete tool listing with parameters and input format requirements.
Type Normalization
Many tools require string inputs. Always convert IDs before API calls:
- ChEMBL IDs, PubMed IDs, NCT IDs: convert int -> str
- SMILES for ADMET-AI: pass as list
["SMILES_STRING"] - FAERS drug names: use UPPERCASE (e.g.,
"METFORMIN") - ChEMBL IDs: full format
"CHEMBL1431"not"1431" - PharmGKB IDs: PA prefix
"PA450657"not"450657"
Common Use Cases
| Use Case | Primary Sections | Light Sections |
|---|---|---|
| Approved Drug Profile | All 11 sections | None |
| Investigational Compound | 1, 2, 3, 4, 9 | 5, 6, 7, 8 |
| Safety Review | 1, 5, 6, 7, 9 | 2, 3, 4, 8 |
| ADMET Assessment | 1, 2, 4 | 3, 5, 6, 7, 8, 9 |
| Clinical Development Landscape | 1, 5, 9 | 2, 3, 4, 6, 7, 8 |
Always maintain all section headers but adjust depth based on query focus and data availability.
When NOT to Use This Skill
- Target research -> Use target-intelligence-gatherer skill
- Disease research -> Use disease-resea
Content truncated.
When not to use it
- →Non-pharmaceutical research
- →Situations requiring real-time diagnostic advice
Prerequisites
Limitations
- →Strictly for research, not clinical decision support
- →Data availability dependent on external registry sources
How it compares
Requires citation of every fact and uses a structured evidence-grading system.
Compared to similar skills
tooluniverse-drug-research side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| tooluniverse-drug-research (this skill) | 3 | 2mo | No flags | Advanced |
| scientific-critical-thinking | 18 | 7mo | Review | Advanced |
| scholar-evaluation | 4 | 2mo | Review | Intermediate |
| literature-review | 559 | 2mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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