Best use case
log-activity is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Log activity to activities.csv
Teams using log-activity 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/log-activity/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How log-activity Compares
| Feature / Agent | log-activity | 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?
Log activity to activities.csv
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
# CRM Log Activity
> Logging outreach activities (calls, messages, emails)
## When to use
- After any outreach
- "log what I wrote"
- "record a call"
- For effectiveness analytics
## Paths
| What | Path |
|------|------|
| Activities | `$CRM_PATH/activities.csv` |
| People | `$CRM_PATH/contacts/people.csv` |
## Schema
```csv
activity_id,linkedin_url,date,channel,activity_type,message_preview,result,response_quality,next_followup_date,audience_segment,hook_type,notes
```
## How to log
```python
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)
```
## After logging -- update the person
```python
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)
```
## Channels (channel)
- `linkedin` -- LinkedIn DM
- `email` -- Email
- `telegram` -- Telegram
- `whatsapp` -- WhatsApp
- `twitter` -- Twitter/X
## Activity types (activity_type)
- `dm` -- Direct message
- `connect_request` -- Connection request
- `followup` -- Follow-up contact
- `email` -- Email message
- `call` -- Phone call
- `research` -- Research (not outreach)
## Results (result)
- `sent` -- Sent, waiting for response
- `replied` -- Received a response
- `no_response` -- No response (after N days)
- `accepted` -- Accepted request/invitation
- `rejected` -- Declined
## Response quality (response_quality)
- `positive` -- Interested
- `neutral` -- Neither yes nor no
- `negative` -- Not interested
- `meeting` -- Meeting scheduled
## Batch logging
For mass outreach -- log multiple at once:
```python
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)
```
## Related skills
- `telegram-send` -- before logging
- `update-lead` -- update person statusRelated Skills
monitoring-whale-activity
Track large cryptocurrency transactions and whale wallet movements across multiple blockchains. Monitor exchange inflows/outflows, manage watchlists, Track large cryptocurrency transactions and whale wallet movements in real-time. Use when tracking large holder movements, exchange flows, or wallet activity. Trigger with phrases like "track whales", "monitor large transfers", "check whale activity", "exchange inflows", or "watch wallet".
wemp-operator
> 微信公众号全功能运营——草稿/发布/评论/用户/素材/群发/统计/菜单/二维码 API 封装
zsxq-smart-publish
Publish and manage content on 知识星球 (zsxq.com). Supports talk posts, Q&A, long articles, file sharing, digest/bookmark, homework tasks, and tag management. Use when publishing content to 知识星球, creating/editing posts, uploading files/images/audio, managing digests, batch publishing, or formatting content for 知识星球.
zoom-automation
Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.
zoho-crm-automation
Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.
ziliu-publisher
字流(Ziliu) - AI驱动的多平台内容分发工具。用于一次创作、智能适配排版、一键分发到16+平台(公众号/知乎/小红书/B站/抖音/微博/X等)。当用户需要多平台发布、内容排版、格式适配时使用。触发词:字流、ziliu、多平台发布、一键分发、内容分发、排版发布。
zhihu-post-skill
> 知乎文章发布——知乎平台内容创作与发布自动化
zendesk-automation
Automate Zendesk tasks via Rube MCP (Composio): tickets, users, organizations, replies. Always search tools first for current schemas.
youtube-knowledge-extractor
This skill performs deep analysis of YouTube videos through **both information channels** Multimodal YouTube video analysis through both audio (transcript) and visual (frame extraction + image analysis) channels. Especially powerful for HowTo videos, tutorials, demos, and explainer videos where what is SHOWN (screenshots, UI demos, diagrams, code, physical actions) is just as important as what is SAID. Use this skill whenever a user wants to analyze, summarize, or create step-by-step guides from YouTube videos, or when they share a YouTube URL and want to understand what happens in the video. Triggers on requests like "Analyze this YouTube video", "Create a step-by-step guide from this video", "What does this video show?", "Summarize this tutorial", or any YouTube URL shared with analysis intent.
youtube-factory
Generate complete YouTube videos from a single prompt - script, voiceover, stock footage, captions, thumbnail. Self-contained, no external modules. 100% free tools.
youtube-automation
Automate YouTube tasks via Rube MCP (Composio): upload videos, manage playlists, search content, get analytics, and handle comments. Always search tools first for current schemas.
xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas