用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/aAAaqwq/AGI-Super-Team --skill create-project命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
币安广场合约投机雷达 v5:以最近24小时专业交易帖为主要证据,回源核验帖子, 联合币安公共合约行情、4周期K线、布林带、ATR、量能和RR,生成可审计的本地影子报告。 触发词:币安广场、扫描币安、binance square、合约机会、交易信号雷达、4小时雷达
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BB 双向套利策略:加密合约 10x 杠杆布林带均值回归。布林带收窄=横盘→在下轨买、上轨卖;三重过滤器(1h趋势/RSI/BB甜区)确认碗平放,轨对轨止盈(RR 2:1~4:1)。含实时WebSocket模拟盘(paper)、历史回测(simulate/backtest_daily)、币安永续实盘CLI(trade_exec)。触发:'bb套利'、'布林带'、'bollinger'、'横盘策略'、'NEAR'、'回测'、'模拟盘'、'paper trading'。
基于 SOC 职业分类
| name | create-project |
| description | Create new project with breakdown |
Creating a new project with task breakdown
| What | Path |
|---|---|
| Projects | $PM_PATH/pm_projects_master.csv |
| Tasks | $PM_PATH/pm_tasks_master.csv |
project_id,project_name,description,goal,status,priority,priority_score,owner,created_date,last_updated,deadline,estimated_hours,actual_hours,actual_tokens,crm_link_type,crm_link_id,tags,notes
import pandas as pd
from datetime import date
import uuid
projects = pd.read_csv('$PM_PATH/pm_projects_master.csv')
new_project = {
'project_id': f'proj-{uuid.uuid4().hex[:4]}',
'project_name': 'Project name',
'description': 'Detailed description',
'goal': 'What does success look like?',
'status': 'planning', # idea/planning/in_progress/on_hold/completed/cancelled
'priority': 'hot', # hot/medium/low
'priority_score': 0.9,
'owner': 'Ivan',
'created_date': str(date.today()),
'last_updated': str(date.today()),
'deadline': '2025-02-10', # if applicable
'estimated_hours': 10,
'actual_hours': 0,
'actual_tokens': 0,
'crm_link_type': '', # company/person/activity
'crm_link_id': '',
'tags': 'tag1;tag2',
'notes': ''
}
projects = pd.concat([projects, pd.DataFrame([new_project])], ignore_index=True)
projects.to_csv('$PM_PATH/pm_projects_master.csv', index=False)
tasks = pd.read_csv('$PM_PATH/pm_tasks_master.csv')
project_id = 'proj-xxxx' # ID of the created project
task_list = [
('Research', 'Gather information', 2),
('Planning', 'Define approach', 1),
('Implementation', 'Do the work', 5),
('Testing', 'Verify results', 1),
]
for i, (name, desc, hours) in enumerate(task_list):
new_task = {
'task_id': f'task-{uuid.uuid4().hex[:4]}',
'project_id': project_id,
'parent_task_id': '',
'task_name': name,
'description': desc,
'status': 'todo',
'priority': 'medium',
'priority_score': 0.5,
'assignee': 'Ivan',
'created_date': str(date.today()),
'last_updated': str(date.today()),
'deadline': '',
'estimated_hours': hours,
'actual_hours': 0,
'actual_tokens': 0,
'blocked_by': '',
: ,
: i +
}
tasks = pd.concat([tasks, pd.DataFrame([new_task])], ignore_index=)
tasks.to_csv(, index=)
idea → planning → in_progress → on_hold → completed/cancelled
If the project is linked to CRM:
'crm_link_type': 'person', # or 'company', 'activity'
'crm_link_id': 'https://linkedin.com/in/example', # or website, activity_id
query-leads -- if linked to CRM