email-summarizer

Email summary and contact profiling skill. Fetch emails from an IMAP mailbox or parse local exports (.pst / .mbox / .msg), build a contact profile report (HTML + Excel), and optionally send it via email. Trigger when user says "summarize my emails", "check recent emails", "analyze my contacts", "profile email contacts", "parse pst file", "analyze outlook export", "send contact report", etc.

Email Summarizer + Contact Profiler

Three independent stages — run any subset depending on what you need:

text

[Data Source]          [Analysis]          [Delivery]
fetch_imap.py    ──→                ──→
parse_file.py    ──→  build_report.py ──→  send_report.py
                       → .html + .xlsx

Each stage reads from / writes to files (or stdin/stdout), so they compose freely.

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Installation

bash

# Python dependencies
pip install -r requirements.txt

# Node.js dependency (only for native .pst parsing)
cd scripts && npm install && cd ..

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Script Reference

Stage 1 — Data Source

fetch_imap.py — Fetch emails from a live IMAP mailbox

bash

export EMAIL_USER=[e-mail]
export EMAIL_PASS=your-app-password

# Fetch last 7 days (inbox only) → emails.json
python3 scripts/fetch_imap.py --days 7 --output emails.json

# Fetch a date range, inbox + sent
python3 scripts/fetch_imap.py --since 2026-03-01 --until 2026-03-28 \
    --with-sent --preset 163 --output emails.json

# Pipe directly to build_report.py (no intermediate file)
python3 scripts/fetch_imap.py --days 30 | python3 scripts/build_report.py --output report

Flag	Default	Description
--days N	7	Fetch last N days. Ignored if --since is set.
--since DATE	—	Start date inclusive (YYYY-MM-DD)
--until DATE	—	End date exclusive (YYYY-MM-DD, default: today)
--max N	200	Max emails per folder
--folder NAME	INBOX	Inbox folder name
--preset NAME	163	Mailbox preset: 163 | qq | exmail | gmail | outlook
--with-sent	off	Also fetch Sent folder (recommended for relationship analysis)
--output FILE	stdout	Write JSON to file; prints to stdout if omitted

Supported presets:

Preset	IMAP Server	Sent folder
163	imap.163.com:993	已发送
qq	imap.qq.com:993	Sent Messages
exmail	imap.exmail.qq.com:993	Sent Messages
gmail	imap.gmail.com:993	[Gmail]/Sent Mail
outlook	outlook.office365.com:993	Sent Items

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parse_file.py — Parse a local email export

bash

# Parse a .pst file (native engine, no system install needed)
python3 scripts/parse_file.py --pst ~/Downloads/archive.pst --output emails.json

# Parse a .mbox file (inbox + sent)
python3 scripts/parse_file.py --mbox Inbox.mbox --sent-mbox Sent.mbox --output emails.json

# Parse a folder of .msg files, filter by date
python3 scripts/parse_file.py --msg-dir ./exported/ --since 2026-01-01 --output emails.json

# Pipe to build_report.py
python3 scripts/parse_file.py --pst archive.pst | python3 scripts/build_report.py --output report

Flag	Default	Description
--pst FILE	—	Outlook .pst archive
--mbox FILE	—	Unix mbox file
--msg-dir DIR	—	Folder of .msg files
--sent-mbox FILE	—	Additional sent-items mbox (with --mbox)
--pst-engine	auto	auto | native | readpst
--days N	all	Only include last N days
--since DATE	—	Start date inclusive (YYYY-MM-DD)
--until DATE	—	End date exclusive (YYYY-MM-DD)
--max N	500	Max emails to load
--output FILE	stdout	Write JSON to file; prints to stdout if omitted

PST engines:

Engine	Flag	Requires
Native (default)	--pst-engine native	cd scripts && npm install
Readpst	--pst-engine readpst	apt install pst-utils or brew install libpst
Auto	--pst-engine auto	Tries native first, falls back to readpst

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Stage 2 — Analysis

build_report.py — Analyse emails → HTML + Excel report

bash

# From a file
python3 scripts/build_report.py --input emails.json --output report

# Specify owner explicitly (when analysing someone else's PST)
python3 scripts/build_report.py --input emails.json --output report \
    --owner [e-mail]

# From stdin (piped from Stage 1)
python3 scripts/fetch_imap.py --days 30 | python3 scripts/build_report.py --output report

Flag	Default	Description
--input FILE	stdin	Path to emails JSON from Stage 1
--output PREFIX	contact_report	Output path prefix. Writes <prefix>.html and <prefix>.xlsx
--owner EMAIL	auto-inferred	Mailbox owner address. Auto-detected if omitted.

Output files:

• <prefix>.html — self-contained HTML report (open in browser or attach to email)
• <prefix>.xlsx — Excel spreadsheet with the same data

Report columns: # / Preferred Name / Email / Company / Position / Subject Summary / Source / Emails (Recv/Sent)

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Stage 3 — Delivery

send_report.py — Send report files via SMTP

bash

export EMAIL_USER=[e-mail]
export EMAIL_PASS=your-app-password

# Send HTML + Excel to a recipient
python3 scripts/send_report.py \
    --html report.html --xlsx report.xlsx --to [e-mail]

# Send to yourself (EMAIL_USER)
python3 scripts/send_report.py --html report.html --xlsx report.xlsx

# HTML only (no attachment)
python3 scripts/send_report.py --html report.html --to [e-mail]

# Custom subject
python3 scripts/send_report.py --html report.html --subject "March Contact Report"

Flag	Default	Description
--html FILE	required	HTML file to use as email body
--xlsx FILE	—	Excel file to attach (optional)
--to ADDR	EMAIL_USER	Recipient address
--subject STR	auto	Email subject (auto-generated if omitted)
--preset NAME	163	SMTP preset: 163 | qq | exmail | gmail | outlook

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Private library modules (not standalone scripts)

File	Provides
_core.py	decode_header, parse_addr, get_domain, strip_html, html_esc, get_body
_analyze.py	infer_owner, build_contacts, domain/company/position/name inference
_render.py	build_report_html, build_excel, SMTP_MAP

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Complete workflow examples

A — IMAP mailbox → report → send to self

bash

export EMAIL_USER=[e-mail]
export EMAIL_PASS=your-app-password

python3 scripts/fetch_imap.py --days 30 --with-sent --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
python3 scripts/send_report.py --html /tmp/report.html --xlsx /tmp/report.xlsx --preset 163

B — PST file → report → send to someone

bash

python3 scripts/parse_file.py --pst ~/Downloads/archive.pst --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
EMAIL_USER=[e-mail] EMAIL_PASS=xxx \
  python3 scripts/send_report.py --html /tmp/report.html --xlsx /tmp/report.xlsx \
    --to [e-mail]

C — One-liner (pipe, no intermediate files)

bash

python3 scripts/parse_file.py --pst archive.pst \
  | python3 scripts/build_report.py --output /tmp/report

D — Report only, no email (open locally)

bash

python3 scripts/parse_file.py --mbox Inbox.mbox --output /tmp/emails.json
python3 scripts/build_report.py --input /tmp/emails.json --output /tmp/report
# Open /tmp/report.html in a browser

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AI analysis templates

After loading the email JSON into the AI context, use the following templates:

Part A: 4-Dimension Email Summary

text

🔥 Part 1 — Important & Action Items
  🚨 [URGENT]    Subject — Sender — Date | Summary | Action | Deadline
  ⚡ [IMPORTANT] Subject — Sender — Date | Summary | Action
  📌 [NOTE]      Subject — Sender — Date | Summary

📊 Part 2 — Grouped by Sender / Topic

✅ Part 3 — To-Do List

📅 Part 4 — Timeline (YYYY-MM-DD  Sender → Subject: summary)

Part B: Contact Profile Analysis

Sort by total interactions. For each contact:

text

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
👤 Rank #N | Name <email>   Total: N (Recv: N / Sent: N)
🧑 Gender       M/F/Unknown  Confidence: H/M/L  Basis: …
💼 Role         …            Basis: domain / signature / keywords
🔗 Relationship Colleague / Client / Institution / Stranger
   Direction    Mutual / Owner-initiated / Contact-initiated
📝 Topics       • subject 1  • subject 2
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Heat scale: 🔥 Heavy (≥10) · ⚡ Active (5–9) · 💬 Moderate (2–4) · 🌙 Light (1)

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Notes

• Credentials are passed via environment variables — never hardcoded
• IMAP connections use readonly mode — emails are never modified or deleted
• Body text is truncated to 2000 characters per email
• fetch_imap.py sends an RFC 2971 ID command required by 163/188 servers; harmless on others