InternScience
InternScience/DrClaw
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/InternScience/DrClaw --skill drugsda-p2rank명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Use the ACPX CLI through DrClaw's existing exec/long_exec tools to run Codex in the current project workspace.
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Convert a user style request into concrete rewrite constraints and apply that style during de-flavoring. Use when the user specifies a target tone, audience, or writing persona.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | drugsda-p2rank |
| description | null |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
| i18n | {"zh":{"description":"使用P2Rank预测蛋白口袋。"}} |
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
)
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()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
return False
async def disconnect(self):
try:
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}
The description of tool pred_pocket_prank.
Use P2Rank to predict ligand binding pockets in the input protein.
Args:
pdb_file_path (str): Path to the protein structure file (PDB format)
Return:
status (str): success/error
msg (str): message
pred_pockets (List[dict]): List of dict, each containing pocket confidence and center position information. The first pocket (pred_pockets[0]) has the highest score and is usually used for molecular docking.
--site_id (str): Pocket id
--probability (float): Predicted confidence score (0~1) of the pocket
--center_x (float): Center X of the pocket
--center_y (float): Center Y of the pocket
--center_z (float): Center Z of the pocket
How to use tool pred_pocket_prank :
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
response = await client.session.call_tool(
"pred_pocket_prank",
arguments={
"pdb_file_path": pdb_file_path
}
)
result = client.parse_result(response)
pred_pockets = result["pred_pockets"]
await client.disconnect()