agentsclimarketplace

Gmail analyze

Skill CorentinLumineau/gmail-assistant/skills/gmail-analyze

Analyze Gmail inbox patterns to identify high-volume senders and suggest filter rules. Use when organizing email, creating automation rules, or understanding inbox composition. Works with personal Gmail accounts via gog CLI.From its SKILL.md

Install
npx -y skills add CorentinLumineau/gmail-assistant --skill gmail-analyze

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.1 KB, 684 tokens by cl100k_base, as published. Nobody here has run it

Gmail Inbox Analyzer

Analyze inbox patterns and suggest intelligent filter rules using Pareto analysis.

Prerequisites Check

First, verify gog CLI is authenticated:

gog auth status

If not authenticated, guide the user to run:

gog auth add [email protected]

Analysis Workflow

Step 1: Fetch Recent Emails

Fetch emails from the specified time period (default: 30 days):

gog gmail search 'newer_than:${DAYS}d' --json

Parse the JSON output to extract:

  • from - Sender email
  • subject - Email subject
  • date - Received date
  • labelIds - Applied labels

Step 2: Pattern Analysis

Analyze the fetched emails to identify:

  1. Top Sender Domains (Pareto: focus on top 20%)

    • Group emails by sender domain
    • Count frequency per domain
    • Identify domains with 5+ emails
  2. Sender Categories

    • Newsletters: substack, mailchimp, newsletter, digest
    • Notifications: github, gitlab, jira, slack, notion
    • Shopping: amazon, ebay, order, shipping
    • Social: facebook, twitter, linkedin
  3. Volume Distribution

    • Total emails analyzed
    • Emails per day average
    • Peak sending times

Step 3: Generate Suggestions

For each high-volume pattern, suggest:

DomainCountSuggested LabelFilter CriteriaConfidence
substack.com45Newslettersfrom:@substack.comHigh
github.com32Notificationsfrom:@github.comHigh

Step 4: Present Results

Output a formatted report:

## Inbox Analysis Report

**Period**: Last {days} days
**Total Emails**: {count}
**Unique Senders**: {unique}

### Top 10 Sender Domains

1. example.com - 142 emails (28%)
2. github.com - 89 emails (18%)
...

### Suggested Filters

| Priority | Pattern | Label | Est. Impact |
|----------|---------|-------|-------------|
| 1 | from:@newsletter.com | Newsletters | 45 emails/month |
| 2 | from:@github.com | Notifications | 32 emails/month |

### Recommended Actions

1. Create "Newsletters" label and filter
2. Create "Notifications" label with auto-archive
3. ...

Would you like me to create these filters? Use `/gmail-filter` to proceed.

Arguments

  • $ARGUMENTS - Number of days to analyze (default: 30)

Example Usage

User: /gmail-analyze 60

Analyzes the last 60 days of email.

Output Format

Always provide:

  1. Summary statistics
  2. Top senders table
  3. Suggested filters with confidence levels
  4. Clear next steps

Error Handling

  • If gog not installed: Provide installation instructions
  • If not authenticated: Guide through OAuth setup
  • If no emails found: Suggest widening the search period

What ships with it: 1 file

1.6 KB alongside SKILL.md

references/

Keep looking

Skills are one crate of 326,422. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.