| name | style-analyzer |
| description | Analyze the user's communication style across their Teams chats and emails to build a reusable mimicry profile — greetings, tone, length, punctuation, sign-offs, common phrases, and quirks. Use this skill when the user asks to capture or analyze their writing style, or before configuring an assistant (e.g. an OOO auto-responder) that should write in their voice. |
Style Analyzer
Analyze the user's communication patterns across Teams and Outlook and build a
style profile that other skills or automations (e.g. an out-of-office
auto-responder) can use to write in the user's voice. Save the profile to memory
so it's available across sessions.
Tool names. This skill refers to Microsoft 365 tools as m365_* and to
memory as remember/recall tools. If your host exposes these under different
names, map them to the equivalent capability.
Data collection
1. Gather sent emails (20–30 samples)
- List the last ~30 emails in the Sent folder.
- For emails with real body content (not just meeting accepts/declines), fetch
the full text body.
2. Gather Teams chat messages
- List recent chats (~50).
- For each relevant chat (prioritize active 1:1 and group chats), fetch the last
~30 messages.
- Filter to messages from the current user (match the
from field to the
user's display name).
3. Sample diversity
Aim for:
- 10+ sent emails with body content
- 50+ Teams messages across 20–25 different chats
- A mix of 1:1, group, and meeting chats
- Both internal and external conversations where available
Analysis framework
Analyze the collected messages across these dimensions:
- A. Greetings — how they address people (first name, "Hi [Name]", "Hey",
formal titles); patterns by relationship type (internal vs external).
- B. Tone & formality — professional/casual/mixed; direct vs hedging; warmth
indicators.
- C. Message length — average sentence count; frequency of one-word replies;
when they write longer messages.
- D. Punctuation & grammar — consistency; common typos (e.g. lowercase "i");
emoji usage (none / occasional / frequent).
- E. Sign-offs — email signature style; Teams message endings; closing
phrases ("Thanks", "Regards", etc.).
- F. Common phrases — frequently used expressions for agreement ("sounds
good", "makes sense"), requests ("can you", "would you mind"), availability,
and FYI/context-setting.
- G. Technical communication — how they explain technical concepts; level of
detail; hedging vs confidence.
- H. Action patterns — how they delegate, loop others in, and schedule
meetings.
Output
1. Display a summary
Present findings as a formatted table:
## Communication Style Profile for [Name]
| Dimension | Pattern |
|-----------|---------|
| Greetings | ... |
| Tone | ... |
| Length | ... |
| Emojis | ... |
| Sign-offs | ... |
| Technical | ... |
### Common phrases
- "..."
- "..."
### Quirks & notes
- ...
2. Save to memory
Store the style guide in memory. Use two entries to stay within any per-fact
length limits:
- Entry 1 — greetings, tone, brevity, punctuation, emojis.
- Entry 2 — common phrases, delegation style, technical communication,
quirks.
Tag both as a preference so they persist and can be recalled later.
3. Confirm storage
Tell the user:
- The style profile has been saved to memory.
- It can be recalled with a query like "writing style".
- It's available to other assistants and automations that write in their voice.
Usage notes
- Re-run periodically (e.g. quarterly) to keep the profile current.
- Pairs well with an OOO / auto-responder skill that should mimic the user's
voice.
Privacy
All analysis happens inside the user's own agent environment against their own
Microsoft 365 data. No communication content is sent to any third party. The
saved profile describes how the user writes, not what they wrote — do not
store verbatim private message content in the profile.
