Agent skill

Drug Research

Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections.

Apache-2.0Auto-check passedResearch & Science

Install Drug Research

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drug-research .claude/skills/drug-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .claude/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drug-research .agents/skills/drug-research && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .agents/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drug-research .cursor/skills/drug-research && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .cursor/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/drug-research

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drug-research .gemini/skills/drug-research && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .gemini/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drug-research .github/skills/drug-research && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .github/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add lamm-mit/scienceclaw --skill drug-research -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install lamm-mit/scienceclaw drug-research --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drug-research .opencode/skills/drug-research && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "drug-research" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/drug-research into .opencode/skills/drug-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drug-research", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: drug-research
GitHub stars: 246
Used in: 3 other repos
Token cost: ~1.7k tokens
SKILL.md length: 598 words
Files: 4 (incl. scripts, references)
Skills in repo: 86
Repo updated: Aug 21, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections.

Works in 6 steps: Report-first approach - The agent… → Compound disambiguation first - The… → Citation requirements - Every fact… → …
Users ask about drugs
SKILL.md covers When to Use, Key Principles, Workflow Overview and Report Structure, plus 4 more sections
Runs Python scripts from its folder

What it does

Drug Research is an agent skill from lamm-mit/scienceclaw. Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when users ask about drugs, medications, therapeutics, or need drug profiling, safety assessment, or clinical development research.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/tool-reference.md` and `scripts/run.py`).

It sits in Research & Science, covering Drug discovery and cheminformatics, Clinical and healthcare research and Deep research. The licence is Apache-2.0.

When your agent uses it

Users ask about drugs
Need drug profiling
Safety assessment
Clinical development research

Example prompts

“Use the drug-research skill to generate comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory…”
“/drug-research”

Requirements

Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

Report-first approach - The agent creates the report file before any data collection, then populates it progressively.
Compound disambiguation first - The agent resolves identifiers (PubChem CID, ChEMBL ID, DailyMed SetID, PharmGKB ID) before beginning…
Citation requirements - Every fact includes inline source attribution with the tool and identifier used.
Evidence grading - Claims are graded by evidence strength (T1: Phase 3/FDA label, T2: Phase 1-2/large case series, T3: preclinical, T4…
Mandatory completeness - All 11 report sections must exist, even if marked "data unavailable."
English-first queries - The agent uses English drug/compound names in tool calls, falling back to original-language terms only if needed…

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drug Research loads about 1.7k tokens when it runs, and up to ~8.8k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 598 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~99
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~8.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 598 words, ~1,672 tokens.

Download SKILL.mdSave it as .claude/skills/drug-research/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: drug-research
description: Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Covers identity, chemistry, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET properties. Use when users ask about drugs, medications, therapeutics, or need drug profiling, safety assessment, or clinical development research.
metadata.source: https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-drug-research

Drug Research Strategy

Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.

For detailed tool chains, output templates, and validation guidance, see references/tool-reference.md.

When to Use

The user asks about a drug, medication, or therapeutic compound
The user needs a drug profile, safety assessment, or clinical development overview
The user requests ADMET evaluation, pharmacogenomics, or regulatory landscape information
The user provides a compound name, SMILES, or ChEMBL/PubChem identifier for research

Key Principles

Report-first approach - The agent creates the report file before any data collection, then populates it progressively.
Compound disambiguation first - The agent resolves identifiers (PubChem CID, ChEMBL ID, DailyMed SetID, PharmGKB ID) before beginning research.
Citation requirements - Every fact includes inline source attribution with the tool and identifier used.
Evidence grading - Claims are graded by evidence strength (T1: Phase 3/FDA label, T2: Phase 1-2/large case series, T3: preclinical, T4: computational).
Mandatory completeness - All 11 report sections must exist, even if marked "data unavailable."
English-first queries - The agent uses English drug/compound names in tool calls, falling back to original-language terms only if needed. The agent responds in the user's language.

Workflow Overview

Step 1:  Create report file ([DRUG]_drug_report.md) with all 11 section headers
Step 2:  Resolve compound identifiers -> Update Section 1 (Identity)
Step 3:  Retrieve FDA label core fields (mechanism, PK, safety, PGx)
Step 4:  Query PubChem / ADMET-AI / DailyMed -> Update Section 2 (Chemistry)
Step 5:  Query FDA Label MOA + ChEMBL + DGIdb -> Update Section 3 (Mechanism & Targets)
Step 6:  Query ADMET-AI tools (fallback: DailyMed PK) -> Update Section 4 (ADMET)
Step 7:  Query ClinicalTrials.gov -> Update Section 5 (Clinical Development)
Step 8:  Query FAERS / DailyMed -> Update Section 6 (Safety)
Step 9:  Query PharmGKB (fallback: DailyMed PGx) -> Update Section 7 (Pharmacogenomics)
Step 10: Query DailyMed / Orange Book -> Update Section 8 (Regulatory)
Step 11: Query PubMed / literature -> Update Section 9 (Literature)
Step 12: Synthesize findings -> Update Executive Summary & Section 10 (Conclusions)
Step 13: Document all sources, run completeness audit -> Update Section 11

Report Structure

The agent produces an 11-section report in [DRUG]_drug_report.md:

Section	Content
Executive Summary	High-level drug profile overview
1. Compound Identity	Database IDs, SMILES, formula, synonyms
2. Chemical Properties	Physicochemical profile, drug-likeness, solubility, salt forms
3. Mechanism & Targets	FDA label MOA, primary targets with UniProt IDs, selectivity
4. ADMET Properties	Absorption, distribution, metabolism, excretion, toxicity
5. Clinical Development	Phase counts, trial landscape, approved/investigational indications, biomarkers
6. Safety Profile	FAERS data, black box warnings, DDIs, drug-food interactions, dose modifications
7. Pharmacogenomics	Pharmacogenes, CPIC/DPWG guidelines, clinical annotations
8. Regulatory & Labeling	Approval status, patents, exclusivity, special populations, timeline
9. Literature & Research	Publication metrics, research themes, real-world evidence
10. Conclusions	Scorecard, strengths, concerns, research gaps, comparative analysis
11. Data Sources	Tool call summary, completeness audit, quality control metrics

Report Detail Requirements

Each section must be comprehensive and detailed:

Tables for structured data (targets, trials, adverse events)
Lists for features, findings, key points
Paragraphs for narrative synthesis
Specific values including counts, percentages, and confidence levels (not vague terms)
Context explaining what the data means, not just what it is
Source attribution at the end of each data block

Show full SKILL.md (219 more words)Show less

Citation Format

The agent attributes every data block to its source:

markdown

*Source: PubChem via `PubChem_get_compound_properties_by_CID` (CID: 4091)*

Section-level source summaries appear at the end of each section:

markdown

---
**Data Sources for this section:**
- PubChem: `PubChem_get_compound_properties_by_CID` (CID: 4091)
- ChEMBL: `ChEMBL_get_bioactivity_by_chemblid` (CHEMBL1431)
---

Critical Rules

Avoid ChEMBL_get_molecule_targets — it returns unfiltered, irrelevant results. The agent derives targets from ChEMBL_search_activities instead, filtering to pChEMBL >= 6.0.
Type normalization — All IDs (ChEMBL, PubMed, NCT) are converted to strings before API calls.
ADMET fallback — If ADMET-AI tools fail, the agent falls back to FDA label PK sections. Section 4 is never left empty.
PharmGKB fallback — If PharmGKB is unavailable, the agent uses DailyMed PGx + PubMed literature.
FAERS limitations — The agent always includes a data limitations paragraph noting voluntary reporting, causality caveats, and reporting bias.
Clinical trial counts — Section 5.2 shows actual counts by phase/status in table format, not just a list of trials.

Common Use Cases

Scenario	Focus
Approved drug profile ("Tell me about metformin")	Full 11-section report emphasizing clinical data, FAERS, PGx
Investigational compound ("What do we know about compound X?")	Preclinical data, mechanism, early trials; safety sections may be sparse
Safety review ("What are the safety concerns with drug Y?")	Deep dive on FAERS, black box warnings, interactions, PGx
ADMET assessment ("Evaluate this compound's drug-likeness")	Focus on Sections 2 and 4; other sections may be brief
Clinical development landscape ("What trials are ongoing for drug Z?")	Heavy emphasis on Section 5 with trial tables

Files

SKILL.md and 3 other files (scripts, references) in skills/drug-research of lamm-mit/scienceclaw.

SKILL.md
references/tool-reference.md
scripts/__pycache__/run.cpython-313.pyc
scripts/run.py

Open the folder on GitHubat commit ab9aba1

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in lamm-mit/scienceclaw, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Drug Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drug Research compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Drug Research this skilllamm-mit/scienceclaw	246	3 repos	~1.7k	Automated safety check: Pass	Apache-2.0	1 mo ago
Biomedical Analysis Dispatchxjtulyc/MedgeClaw	617	1 repos	~2k	Automated safety check: Pass	None	7 mo ago
Hcls Build Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences	274	—	~885	Automated safety check: Pass	MIT-0	9 days ago
Tooluniverse Gwas Drug Discoverywu-yc/LabClaw	1.1k	2 repos	~4.7k	Automated safety check: Pass	None	6 mo ago
Clinical ReportsK-Dense-AI/claude-scientific-writer	2.4k	1 repos	~3.7k	Automated safety check: Pass	MIT	yesterday
Hcls Get Startedaws-samples/amazon-bedrock-agents-healthcare-lifesciences	274	—	~607	Automated safety check: Pass	MIT-0	9 days ago

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Questions about Drug Research

What does Drug Research do?

Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections. Drug Research is an agent skill from lamm-mit/scienceclaw. Generates comprehensive drug research reports with compound disambiguation, evidence grading, and mandatory completeness sections.

When should I use Drug Research?

Drug Research fits situations like: users ask about drugs; need drug profiling; safety assessment; clinical development research.

How do I install Drug Research in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill drug-research -a claude-code`. Or copy the skill folder (skills/drug-research in lamm-mit/scienceclaw) into .claude/skills/drug-research in your project. Claude Code loads it when a task matches its description.

How do I install Drug Research in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill drug-research -a codex`. Or copy the skill folder (skills/drug-research in lamm-mit/scienceclaw) into .agents/skills/drug-research in your project. Codex loads it when a task matches its description.

Can I use Drug Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lamm-mit/scienceclaw --skill drug-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drug-research, .gemini/skills/drug-research, .github/skills/drug-research and .opencode/skills/drug-research in your project.

What does Drug Research need to run?

Going by SKILL.md and its folder, Drug Research needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Drug Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Drug Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Drug Research use?

Drug Research is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drug Research use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.1k tokens, read only when the agent opens those files.

What are the alternatives to Drug Research?

Skills that share tags, products or a category with Drug Research: Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars), Hcls Build Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Tooluniverse Gwas Drug Discovery (wu-yc/LabClaw, 1.1k stars) and Clinical Reports (K-Dense-AI/claude-scientific-writer, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drug Research?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 246 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on August 21, 2026.

Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Drug Research

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Drug Research Strategy

When to Use

Key Principles

Workflow Overview

Report Structure

Report Detail Requirements

Citation Format

Critical Rules

Common Use Cases

Files

Used in 3 other repositories

Compare with similar skills

Similar skills

More from lamm-mit/scienceclaw

Related

Questions about Drug Research