OpenClaw

pharmaclaw-market-intel-agent

Fetches and analyzes FAERS (FDA Adverse Event Reporting System) data from openFDA API. Supports drug names and SMILES (resolves via PubChem). Generates: events list, yearly trends (counts), top reactions/outcomes as JSON + matplotlib bar chart PNGs.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install cheminem/pharmaclaw-market-intel-agent

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Pharma Market Intel Agent - FAERS Query Skill

Overview

Query real-world post-market safety data for drugs. Useful for market intel on safety profiles, emerging risks, competitor analysis.

Key outputs:

JSON summaries (trends, top reactions/outcomes)
PNG bar charts (yearly reports, top 10 reactions/outcomes)
Sample recent events

Rate limits: openFDA ~240 req/min. Counts are fast (no full data).

Chemistry-Query Structure

Parse user queries into this model for standardized chaining:

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class ChemistryQuery:
    drug: str  # Drug name or SMILES
    query_type: str = 'faers'  # 'faers', 'pubchem', etc.
    metrics: Optional[List[str]] = None  # ['yearly_trends', 'top_reactions', 'top_outcomes', 'events']
    limit_events: int = 20

Example:

{
  \"drug\": \"aspirin\",  // or \"CC(=O)OC1=CC=CC=C1C(=O)O\"
  \"query_type\": \"faers\",
  \"metrics\": [\"yearly_trends\", \"top_reactions\"]
}

Quick Start / Workflows

1. Basic Query (All Metrics)

exec skills/pharma-market-intel-agent/scripts/query_faers.py --drug aspirin --output ./aspirin_faers

Generates:

aspirin_faers/aspirin_summary.json
*.png plots
Recent events JSON

2. SMILES Input

exec ... --drug \"CC(=O)OC1=CC=CC=C1C(=O)O\"  # Aspirin SMILES

Auto-resolves to name via PubChem.

3. Custom Limit

exec ... --drug ozempic --limit-events 50 --output ozempic_analysis

Chaining Examples

With chemistry-query: Resolve/validate SMILES first, then FAERS.
pharma-tox-agent: Feed top reactions for tox prediction.
pharma-ip-expansion-agent: Check safety for IP expansion targets.
traction-agent: Market risk scoring from FAERS trends.

# Agent workflow:
1. Parse ChemistryQuery
2. Resolve SMILES if needed (pubchempy or query_faers handles)
3. Run query_faers.py
4. Read PNGs/JSONs into response
5. Chain if metrics require

Clinical...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/cheminem/pharmaclaw-market-intel-agent
Version: —
Commit: ef4d3c90f93ac218179f83123434769a10f8a614
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Pharma Market Intel Agent - FAERS Query Skill

## Overview
Query real-world post-market safety data for drugs. Useful for market intel on safety profiles, emerging risks, competitor analysis.

Key outputs:
- JSON summaries (trends, top reactions/outcomes)
- PNG bar charts (yearly reports, top 10 reactions/outcomes)
- Sample recent events

**Rate limits**: openFDA ~240 req/min. Counts are fast (no full data).

## Chemistry-Query Structure
Parse user queries into this model for standardized chaining:

```python
from dataclasses import dataclass
from typing import List, Optional

@dataclass
class ChemistryQuery:
    drug: str  # Drug name or SMILES
    query_type: str = 'faers'  # 'faers', 'pubchem', etc.
    metrics: Optional[List[str]] = None  # ['yearly_trends', 'top_reactions', 'top_outcomes', 'events']
    limit_events: int = 20
```

Example:
```json
{
  \"drug\": \"aspirin\",  // or \"CC(=O)OC1=CC=CC=C1C(=O)O\"
  \"query_type\": \"faers\",
  \"metrics\": [\"yearly_trends\", \"top_reactions\"]
}
```

## Quick Start / Workflows

### 1. Basic Query (All Metrics)
```
exec skills/pharma-market-intel-agent/scripts/query_faers.py --drug aspirin --output ./aspirin_faers
```
Generates:
- aspirin_faers/aspirin_summary.json
- *.png plots
- Recent events JSON

### 2. SMILES Input
```
exec ... --drug \"CC(=O)OC1=CC=CC=C1C(=O)O\"  # Aspirin SMILES
```
Auto-resolves to name via PubChem.

### 3. Custom Limit
```
exec ... --drug ozempic --limit-events 50 --output ozempic_analysis
```

## Chaining Examples
- **With chemistry-query**: Resolve/validate SMILES first, then FAERS.
- **pharma-tox-agent**: Feed top reactions for tox prediction.
- **pharma-ip-expansion-agent**: Check safety for IP expansion targets.
- **traction-agent**: Market risk scoring from FAERS trends.

```
# Agent workflow:
1. Parse ChemistryQuery
2. Resolve SMILES if needed (pubchempy or query_faers handles)
3. Run query_faers.py
4. Read PNGs/JSONs into response
5. Chain if metrics require
```

---

## Clinical...

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