tcga-gene-expression
Retrieve gene expression data from TCGA (The Cancer Genome Atlas) to analyze cancer-specific expression patterns.
Best use case
tcga-gene-expression is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Retrieve gene expression data from TCGA (The Cancer Genome Atlas) to analyze cancer-specific expression patterns.
Teams using tcga-gene-expression should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tcga-gene-expression/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tcga-gene-expression Compares
| Feature / Agent | tcga-gene-expression | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Retrieve gene expression data from TCGA (The Cancer Genome Atlas) to analyze cancer-specific expression patterns.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# TCGA Gene Expression Analysis
## Usage
```python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class OrigeneClient:
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
try:
self.transport = streamablehttp_client(url=self.server_url, headers={"SCP-HUB-API-KEY": self.api_key})
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
return True
except Exception as e:
return False
async def disconnect(self):
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
def parse_result(self, result):
if isinstance(result, dict):
content_list = result.get("content") or []
else:
content_list = getattr(result, "content", []) or []
texts = []
for item in content_list:
if isinstance(item, dict):
if item.get("type") == "text":
texts.append(item.get("text") or "")
else:
if getattr(item, "type", None) == "text":
texts.append(getattr(item, "text", "") or "")
return "".join(texts)
## Initialize and use
client = OrigeneClient("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "<your-api-key>")
await client.connect()
result = await client.session.call_tool("get_gene_specific_expression_in_cancer_type", arguments={"gene": "TP53"})
print(client.parse_result(result))
await client.disconnect()
```
### Tool: `get_gene_specific_expression_in_cancer_type`
- Args: `gene` (str) - Gene symbol (e.g., "TP53")
- Returns: High/low expression cancers with mean expression and sample counts
### Use Cases
- Cancer genomics, biomarker discovery, tumor characterization, precision oncologyRelated Skills
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