Communitygithub.com

Linearl/reasonix_skill_repo

Practical agent skills for Reasonix & Claude Code: patent search, document-to-skill, web scraping, local OCR, project migration, Obsidian/OneNote notes, remote deployment & multi-machine sync — 12 self-contained skills.

What is reasonix_skill_repo?

reasonix_skill_repo is a Claude Code agent skill that practical agent skills for Reasonix & Claude Code: patent search, document-to-skill, web scraping, local OCR, project migration, Obsidian/OneNote notes, remote deployment & multi-machine sync — 12 self-contained skills.

Works with✓Claude Code✓Codex CLI~Cursor
npx skills add Linearl/reasonix_skill_repo
View original→Browse all skills

Ask in your favorite AI

Open a new chat with this agent skill pre-loaded.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

Documentation

Mail Skill

A powerful email management skill that acts as your personal email assistant.

When to Activate

  • User asks to check, fetch, or read emails
  • User wants to search emails (keyword or natural language)
  • User needs to send, reply to, or forward emails
  • User requests email summaries or reports
  • User mentions email threads or conversations
  • User asks about attachments in emails
  • User wants to organize or classify emails

Quick Start

# Fetch latest emails
python scripts/mail_cli.py fetch --days 7

# Search emails
python scripts/mail_cli.py search --query "project update"

# Send an email
python scripts/mail_cli.py send --to [email protected] --subject "Hello" --body "Message content"

Core Commands

Fetch Emails

# Fetch recent emails (default: last 7 days, max 50)
python scripts/mail_cli.py fetch

# Fetch from specific folder
python scripts/mail_cli.py fetch --folder INBOX

# Fetch from all folders
python scripts/mail_cli.py fetch --folder ALL

# Fetch more emails (requires confirmation)
python scripts/mail_cli.py fetch --limit 200 --confirm

# Fetch only unread
python scripts/mail_cli.py fetch --unread

# Check fetch task status (async)
python scripts/mail_cli.py fetch-status <task_id>

Output: JSON with task_id for async tracking. Emails are stored in ./mail_data/<account>/.

Search Emails

# Full-text search
python scripts/mail_cli.py search --query "budget report"

# Semantic search (vector embeddings)
python scripts/mail_cli.py search --query "project timeline" --vector

# Hybrid search (FTS + Vector with reranking)
python scripts/mail_cli.py search --query "meeting notes" --hybrid

# Filter by attributes
python scripts/mail_cli.py search --sender "[email protected]" --folder INBOX --is-read 0

# Filter by classification
python scripts/mail_cli.py search --importance high --category work

# Filter by tag
python scripts/mail_cli.py search --tag "follow-up"

Output: JSON with count and results array containing message_id, subject, sender, date, snippet.

Natural Language Search

# Smart search understands natural language
python scripts/mail_cli.py smart-search "emails from John last week about budget"
python scripts/mail_cli.py smart-search "unread emails from boss yesterday"
python scripts/mail_cli.py smart-search "emails about project deadline this month"

Output: JSON with parsed_query (extracted date range, sender, keywords) and results.

Read Email

# Read full email with enhanced Markdown formatting
python scripts/mail_cli.py read <message_id>

# Brief table view
python scripts/mail_cli.py read <message_id> --brief

Output: Markdown-formatted email with sender, recipients, date, subject, body, attachments, and thread context.

Send Email

# Basic send
python scripts/mail_cli.py send --to [email protected] --subject "Subject" --body "Body text"

# With CC/BCC
python scripts/mail_cli.py send --to [email protected] --cc [email protected] --subject "Subject" --body "Body"

# With attachments
python scripts/mail_cli.py send --to [email protected] --subject "Report" --body "See attached" --attach ./report.pdf

# Zip folders as attachment
python scripts/mail_cli.py send --to [email protected] --subject "Files" --body "Here" --attach ./folder --zip-as "files.zip"

Note: Body text supports Markdown and is automatically converted to styled HTML.

Reply to Email

# Reply to sender
python scripts/mail_cli.py reply <message_id> --body "Reply content"

# Reply to all (sender + CC)
python scripts/mail_cli.py reply <message_id> --body "Reply to all" --all

# With attachments
python scripts/mail_cli.py reply <message_id> --body "See attached" --attach ./file.pdf

Note: Original email history is appended automatically. Signature is added if signature.md exists.

Thread View

# Show email thread timeline
python scripts/mail_cli.py thread <message_id>

# With LLM-generated summary
python scripts/mail_cli.py thread <message_id> --summary

Output: Timeline of related emails with sender/recipient matching.

Email Summarization

# Summarize recent emails (categorized)
python scripts/mail_cli.py summarize --limit 10

# Summarize emails from a fetch task
python scripts/mail_cli.py summarize --task-id <task_id>

Output: Markdown report with categories:

  • Verification codes (extracted codes highlighted)
  • Important emails (priority keywords detected)
  • Action required (reply/follow-up needed)
  • Other regular emails

Summary Report by Sender

# Generate report grouped by sender (last 7 days)
python scripts/mail_cli.py summary-report

# Custom date range
python scripts/mail_cli.py summary-report --date-from 2024-01-01 --date-to 2024-01-31

# Save to file
python scripts/mail_cli.py summary-report --output report.md

Output: Markdown report with sender-grouped emails and LLM-generated summaries.

Email Management

Mark as Read/Starred

# Mark as read
python scripts/mail_cli.py mark <message_id> --read 1

# Mark as unread
python scripts/mail_cli.py mark <message_id> --read 0

# Star/unstar
python scripts/mail_cli.py mark <message_id> --starred 1

# Batch mark
python scripts/mail_cli.py batch-mark --from-search "newsletter" --read 1

Tags (Labels)

# Add tag
python scripts/mail_cli.py tag add <message_id> "follow-up"

# Remove tag
python scripts/mail_cli.py tag remove <message_id> "follow-up"

# List tags
python scripts/mail_cli.py tag list <message_id>

# Batch add tags
python scripts/mail_cli.py tag batch-add "important" --from-search "from:boss"

Classification

# Classify single email
python scripts/mail_cli.py classify <message_id>

# Auto-classify all unclassified
python scripts/mail_cli.py classify --limit 100

# Manual reclassify
python scripts/mail_cli.py reclassify <message_id> --importance high --category work

Categories: work, personal, notification, promo, uncategorized Importance: critical, high, normal, low

Move/Delete

# Move to folder
python scripts/mail_cli.py move <message_id> Archive

# Delete email
python scripts/mail_cli.py delete <message_id>

Attachments

List Attachments

# List attachments with preview URLs
python scripts/mail_cli.py attachments --limit 50

Output: JSON with preview_url for each attachment (local HTTP server URL).

Parse Attachment Content

# Parse attachments for specific email
python scripts/mail_cli.py parse-attachments --message-id <message_id>

# Parse all unprocessed attachments
python scripts/mail_cli.py parse-attachments --all

Supported formats: PDF, Excel (.xlsx/.xls), PowerPoint (.pptx), images (OCR via vision model), text files.

AI Features

AI-Generated Reply

# Generate and preview reply
python scripts/mail_cli.py ai-reply <message_id> --dry-run

# Generate with intent guidance
python scripts/mail_cli.py ai-reply <message_id> --intent "polite decline"

# Include thread context
python scripts/mail_cli.py ai-reply <message_id> --with-thread

# Send directly (with confirmation)
python scripts/mail_cli.py ai-reply <message_id>

Flow: Generates reply → Shows preview → Asks confirmation (y/n/e=edit) → Sends or cancels.

Email Templates

# List templates
python scripts/mail_cli.py templates list

# Show template
python scripts/mail_cli.py templates show welcome

# Create template
python scripts/mail_cli.py templates create welcome --content "Hello {{name}}, ..." --required-vars name

Configuration

Copy example.config.txt to config.txt and fill in your details:

# Email Account
[email protected]
MAIL_ACCOUNT_1_PASSWORD=your-app-password
MAIL_ACCOUNT_1_PROTOCOL=imap
MAIL_ACCOUNT_1_IMAP_SERVER=imap.gmail.com
MAIL_ACCOUNT_1_IMAP_PORT=993
MAIL_ACCOUNT_1_POP3_SERVER=pop.gmail.com
MAIL_ACCOUNT_1_POP3_PORT=995
MAIL_ACCOUNT_1_SMTP_SERVER=smtp.gmail.com
MAIL_ACCOUNT_1_SMTP_PORT=465
MAIL_ACCOUNT_1_USE_SSL=true

# AI Configuration (Optional - LLM and Embedding can use different providers)
# LLM_API_KEY=your_api_key
# LLM_API_BASE=https://api.deepseek.com/v1
# LLM_MODEL_NAME=deepseek-chat
# EMBEDDING_API_KEY=your_api_key
# EMBEDDING_API_BASE=https://api.siliconflow.cn/v1
# EMBEDDING_MODEL_NAME=BAAI/bge-large-zh-v1.5
# RERANKER_MODEL_NAME=BAAI/bge-reranker-base

Data Storage

Directory Structure

mail_data/
├── <account_sanitized>/         # Per-account storage
│   ├── mail_index.db           # Email index (SQLite + FTS5 + ChromaDB)
│   ├── eml/                    # Raw email files
│   ├── json/                   # Parsed email JSON
│   ├── attachments/            # Downloaded attachments
│   ├── signature.md            # Account signature (optional)
│   └── templates/              # Email templates (optional)

Account Path Sanitization

Email addresses are sanitized for directory names:

  • [email protected] → user_at_example_com
  • Special characters removed, only alphanumeric, -, _ kept

Output Formats

All commands return JSON with consistent structure:

Success Response

{
  "status": "success",
  "message": "Operation completed",
  "data": { ... }
}

Error Response

{
  "status": "error",
  "error_code": "USER_EMAIL_NOT_FOUND",
  "message": "Email not found locally"
}

Error Codes

CodeDescription
USER_EMAIL_NOT_FOUNDEmail/account not found
USER_INVALID_PARAMETERInvalid input parameter
USER_MISSING_PARAMETERRequired parameter missing
BIZ_ACCOUNT_NOT_CONFIGUREDNo email account configured
SERVER_IMAP_CONNECTION_FAILEDIMAP connection error
SERVER_SMTP_SEND_FAILEDSMTP send error
SERVER_DATABASE_ERRORDatabase error
INTERNAL_ERRORInternal server error

Search Capabilities

Three Search Modes

  1. FTS (Full-Text Search): Fast keyword search using SQLite FTS5
  2. Vector Search: Semantic similarity using OpenAI embeddings + ChromaDB
  3. Hybrid Search: Combines FTS + Vector with cross-encoder reranking

Rebuild Search Index

# Rebuild FTS5 and vector indices
python scripts/mail_cli.py rebuild-index

Requirements

  • Python 3.8+
  • OpenAI API key (for AI features)
  • Email account with IMAP/SMTP access

Installation

pip install -r requirements.txt

Troubleshooting

  • Config not found: Copy example.config.txt to config.txt and fill in your email details
  • IMAP connection failed: Check server settings and app passwords
  • Search returns empty: Run rebuild-index to rebuild search indices
  • Attachments not previewing: Check if attachment server is running (auto-starts on demand)

Updates

/mail-update

Clone or update mail-skill from GitHub, with automatic backup:

REPO_URL="https://github.com/lgwanai/mail-skill.git"
SKILL_DIR="mail-skill"

if [ -d "$SKILL_DIR/.git" ]; then
    # Already cloned — backup then pull
    cd "$SKILL_DIR"
    BACKUP_DIR="backup/$(date +%Y%m%d_%H%M%S)"
    mkdir -p "$BACKUP_DIR"
    cp -r scripts requirements.txt example.config.txt SKILL.md README.md "$BACKUP_DIR/" 2>/dev/null
    git pull origin main
else
    # First time — clone
    rm -rf "$SKILL_DIR"
    git clone "$REPO_URL" "$SKILL_DIR"
    cd "$SKILL_DIR"
fi

# Reinstall dependencies
pip install -r requirements.txt

echo "Updated to $(git log -1 --format='%h %s')"
[ -n "${BACKUP_DIR:-}" ] && echo "Backup saved to $BACKUP_DIR"

What it does:

  1. If already cloned: backs up source files to backup/YYYYMMDD_HHMMSS/, then git pull
  2. If first time: git clone from GitHub
  3. Reinstalls dependencies
  4. Shows the latest commit info and backup path

Related Skills

steipete/sag

ElevenLabs text-to-speech with mac-style say UX.

community

steipete/oracle

Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.

community

steipete/peekaboo

Capture and automate macOS UI with the Peekaboo CLI.

community

obra/brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

community

affaan-m/prisma-patterns

Prisma ORM patterns for TypeScript backends — schema design, query optimization, transactions, pagination, and critical traps like updateMany returning count not records, $transaction timeouts, migrate dev resetting the DB, @updatedAt skipped on bulk writes, and serverless connection exhaustion.

community

affaan-m/django-celery

Django + Celery async task patterns — configuration, task design, beat scheduling, retries, canvas workflows, monitoring, and testing. Use when adding background jobs, scheduled tasks, or async processing to a Django app.

community