About

Imported from majiayu000/claude-skill-registry (skills/documents/target-profile/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill target-profile. Copyright stays with the author.

Target Profile Skill

Name: target-profile
Author: majiayu000

Generate comprehensive target dossiers for drug discovery decision-making.

Quick Start

/target EGFR
/target-profile KRAS G12C
Create a target dossier for HER2 including clinical trials
Analyze druggability of BRAF V600E

What's Included

Section	Description	Data Source
Executive Summary	Key insights in one page	Aggregated
Target Overview	Gene/protein name, class, location	UniProt
Druggability	Tractability scores, target class	Open Targets
Disease Associations	Associated diseases, evidence scores	Open Targets
Pathways	Signaling pathways, interactions	KEGG, Reactome
Competition	Existing drugs, pipeline	ChEMBL, DrugBank
Safety	Known safety concerns	Pharos, SIDER

Data Sources

Source	API	Coverage
Open Targets	`api.opentargets.org`	20k+ targets, 1.2M associations
UniProt	`rest.uniprot.org`	200M+ proteins
ChEMBL	`www.ebi.ac.uk/chembl/api`	2.5M+ compounds
KEGG	`rest.kegg.jp`	500+ pathways
Reactome	`reactome.org`	2600+ pathways

Output Structure

# EGFR Target Profile

## Executive Summary
EGFR is a high-tractability receptor tyrosine kinase with strong validation
in NSCLC. 9 drugs approved, 34 in development. Key opportunity: resistance
mechanisms and combination therapies.

## Quick Stats
| Metric | Value |
|--------|-------|
| Tractability | 8.2/10 (Small molecule) |
| Disease Associations | 142 diseases |
| Approved Drugs | 9 |
| Pipeline Candidates | 34 |
| Safety Tier | 2 (Moderate risk) |

## 1. Target Overview
- **Gene**: EGFR (ERBB1)
- **Protein**: Epidermal growth factor receptor
- **Class**: Receptor tyrosine kinase
- **Location**: Cell membrane (Plasma membrane)
- **Length**: 1210 amino acids
- **MW**: 134 kDa

## 2. Druggability Assessment
### Tractability Scores
| Modality | Score | Evidence |
|----------|-------|----------|
| Small molecule | 8.2/10 | 9 approved drugs |
| Antibody | 7.8/10 | 4 approved antibodies |
| PROTAC | 6.5/10 | Emerging approach |

### Target Development Level
**Tclin (Highest)** - Target with drugs approved for clinical use

## 3. Disease Associations
| Disease | Association Score | Evidence Type |
|---------|------------------|---------------|
| Lung adenocarcinoma | 0.95 | Genetic association |
| Glioblastoma | 0.87 | Somatic mutation |
| Head and neck cancer | 0.82 | Genetic association |

## 4. Pathway Context
- **Primary Pathway**: ErbB signaling pathway (KEGG: hsa04012)
- **Upstream**: EGF, TGF-alpha, Amphiregulin
- **Downstream**: MAPK, PI3K-Akt, JAK-STAT
- **Cross-talk**: MET, HER2, HER3

## 5. Competitive Landscape
### Approved Drugs
| Drug | Company | Year | Type | Indication |
|------|---------|------|------|------------|
| Erlotinib | Astellas | 2004 | TKI | NSCLC |
| Gefitinib | AstraZeneca | 2003 | TKI | NSCLC |
| Osimertinib | AstraZeneca | 2015 | 3rd-gen TKI | NSCLC |

### Pipeline (Selected)
| Drug | Company | Phase | Differentiation |
|------|---------|-------|----------------|
| Lazertinib | Yuhan | III | 3rd-gen, wild-type sparing |
| Nazartinib | Novartis | III | 3rd-gen, CNS active |

## 6. Safety Considerations
- **On-target toxicity**: Skin rash, diarrhea (class effect)
- **Off-target concerns**: Cardiac toxicity (rare)
- **Safety Tier**: 2 (Manageable risk)

## 7. Key Opportunities
1. Resistance mechanisms (C797S, MET amplification)
2. Combination therapies (EGFR + MET)
3. CNS-penetrant candidates
4. Biomarker-driven patient selection

## 8. Key Risks
1. Crowded competitive space
2. Generic competition (1st gen)
3. Resistance development

Examples

Basic Profile

/target EGFR

With Specific Focus

/target KRAS --focus safety
Analyze safety profile of BTK
/target HER2 --focus competition

Compare Multiple Targets

Compare targets EGFR, HER2, HER3 for NSCLC treatment
Rank KRAS, NRAS, HRAS by druggability

Specific Analysis

/target BRAF V600C
Assess druggability of mutant BRAF
What is the tractability of KRAS G12C?

Running Scripts

The scripts/ directory contains data fetching utilities:

# Fetch basic target data
python scripts/fetch_target_data.py EGFR --output data.json

# Include all sources
python scripts/fetch_target_data.py EGFR --uniprot --chembl --pathways -o full.json

# Specific data only
python scripts/fetch_target_data.py KRAS --diseases-only

Requirements

None for basic use (uses public APIs).

For advanced features and scripts:

pip install requests pandas

Additional Resources

Best Practices

Use official gene symbols (HGNC nomenclature) for best results
Include mutation if relevant (e.g., "KRAS G12C")
Specify focus when you need deeper analysis on one area
Compare targets to support decision-making

Common Pitfalls

Pitfall	Solution
Ambiguous gene names	Use official HGNC symbols
Multiple isoforms	Specify isoform number if needed
Species confusion	Assume human unless specified
Outdated info	Data is current as of last API fetch

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/majiayu000-claude-skill-registry-target-profile/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

majiayu000-claude-skill-registry-target-profile.ocm.jsonjson

{
  "ocm": "1",
  "id": "majiayu000-claude-skill-registry-target-profile",
  "kind": "skill",
  "name": "target-profile",
  "description": "Generate comprehensive target profiles including druggability assessment, disease associations, pathway context, and competitive landscape. Use this skill when analyzing drug targets for validation, dossier creation, or competitive intelligence. Supports gene symbols (EGFR), protein names, or UniProt IDs. Keywords: target, dossier, validation, druggability, tractability, target analysis",
  "publisher": "majiayu000",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "medical"
    ],
    "tags": [
      "skill-md",
      "target",
      "drug-discovery",
      "dossier",
      "validation",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Generate comprehensive target profiles including druggability assessment, disease associations, pathway context, and competitive landscape. Use this skill when analyzing drug targets for validation, dossier creation, or competitive intelligence. Supports gene symbols (EGFR), protein names, or UniProt IDs. Keywords: target, dossier, validation, druggability, tractability, target analysis"
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/majiayu000/claude-skill-registry",
      "path": "skills/documents/target-profile/SKILL.md",
      "ref": "a16e8e7bdddc9a7406a56f51e520cef7a8646208",
      "url": "https://github.com/majiayu000/claude-skill-registry/blob/a16e8e7bdddc9a7406a56f51e520cef7a8646208/skills/documents/target-profile/SKILL.md",
      "key": "majiayu000/claude-skill-registry/skills/documents/target-profile/SKILL.md"
    }
  },
  "instructions": "# Target Profile Skill\n\nGenerate comprehensive target dossiers for drug discovery decision-making.\n\n## Quick Start\n\n```\n/target EGFR\n/target-profile KRAS G12C\nCreate a target dossier for HER2 including clinical trials\nAnalyze druggability of BRAF V600E\n```\n\n## What's Included\n\n| Section | Description | Data Source |\n|---------|-------------|-------------|\n| **Executive Summary** | Key insights in one page | Aggregated |\n| **Target Overview** | Gene/protein name, class, location | UniProt |\n| **Druggability** | Tractability scores, target class | Open Targets |\n| **Disease Associations** | Asso",
  "cost": {
    "context_tokens": 1348
  }
}

Fetch it by URL: GET /api/v1/registry/majiayu000-claude-skill-registry-target-profile/manifest?version=1.0.0

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