style-analyzer

par microsoft

Analysez le style de communication de l'utilisateur dans ses conversations Teams et ses e-mails afin de créer un profil de mimétisme réutilisable — salutations, ton, longueur, ponctuation,…

npx skills add https://github.com/microsoft/cat-agent-skills --skill style-analyzer

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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.