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 log-activity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。
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
BTC 5分钟K线实时方向预测。v5.9对抗式审查重构: 13因子收敛到3个有证据信号(half_body延续+volume放量+meanrev回归, 11个47-49%硬币因子清零) + 三层独立信息过滤(多周期4h/1h/15m趋势 + 跨资产ETH/SOL广度 + 真订单流OFI) + 移除bull×0.92惩罚/Platt置信度门控。半K线策略第2分钟执行。黑天鹅防护: ATR spike+FNG<25。Binance端点双向故障切换。
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
正在显示 SKILL.md
| name | log-activity |
| description | Log activity to activities.csv |
Logging outreach activities (calls, messages, emails)
| What | Path |
|---|---|
| Activities | $CRM_PATH/activities.csv |
| People | $CRM_PATH/contacts/people.csv |
activity_id,linkedin_url,date,channel,activity_type,message_preview,result,response_quality,next_followup_date,audience_segment,hook_type,notes
import pandas as pd
from datetime import date, timedelta
import uuid
activities = pd.read_csv('$CRM_PATH/activities.csv')
new_activity = {
'activity_id': f'act-{uuid.uuid4().hex[:8]}',
'linkedin_url': 'https://linkedin.com/in/example', # or email
'date': str(date.today()),
'channel': 'telegram', # linkedin/email/twitter/telegram/whatsapp
'activity_type': 'dm', # dm/connect_request/followup/email/call/research
'message_preview': 'Hi! This is Ivan from WeLabelData...', # first 100 characters
'result': 'sent', # sent/replied/no_response/accepted/rejected
'response_quality': '', # positive/neutral/negative/meeting
'next_followup_date': str(date.today() + timedelta(days=7)),
'audience_segment': 'training_course',
'hook_type': 'course_invitation',
'notes': ''
}
activities = pd.concat([activities, pd.DataFrame([new_activity])], ignore_index=True)
activities.to_csv('$CRM_PATH/activities.csv', index=False)
people = pd.read_csv('$CRM_PATH/contacts/people.csv')
mask = people['linkedin_url'] == 'https://linkedin.com/in/example'
people.loc[mask, 'status'] = 'contacted'
people.loc[mask, 'last_contact_date'] = str(date.today())
people.loc[mask, 'next_followup_date'] = str(date.today() + timedelta(days=7))
people.loc[mask, 'last_updated'] = str(date.today())
people.to_csv('$CRM_PATH/contacts/people.csv', index=False)
linkedin -- LinkedIn DMemail -- Emailtelegram -- Telegramwhatsapp -- WhatsApptwitter -- Twitter/Xdm -- Direct messageconnect_request -- Connection requestfollowup -- Follow-up contactemail -- Email messagecall -- Phone callresearch -- Research (not outreach)sent -- Sent, waiting for responsereplied -- Received a responseno_response -- No response (after N days)accepted -- Accepted request/invitationrejected -- Declinedpositive -- Interestedneutral -- Neither yes nor nonegative -- Not interestedmeeting -- Meeting scheduledFor mass outreach -- log multiple at once:
sent_to = ['email1@test.com', 'email2@test.com', 'email3@test.com']
for email in sent_to:
new_activity = {
'activity_id': f'act-{uuid.uuid4().hex[:8]}',
'linkedin_url': email, # using email as ID
'date': str(date.today()),
'channel': 'telegram',
'activity_type': 'dm',
'result': 'sent',
# ...
}
activities = pd.concat([activities, pd.DataFrame([new_activity])], ignore_index=True)
telegram-send -- before loggingupdate-lead -- update person status