Gmail

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/gmail-automation

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Gmail Lightweight Gmail integration with standalone OAuth authentication. No MCP server required. > ⚠️ Requires Google Workspace account. Personal Gmail accounts are not supported.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Gmail Query Syntax, Common Label IDs, Token Management, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• You need to search, read, or send Gmail messages from the command line without an MCP server.
• You are automating inbox workflows for a Google Workspace account.
• You want a lightweight Gmail integration backed by standalone OAuth scripts.
• Use when the request clearly matches the imported source intent: Lightweight Gmail integration with standalone OAuth authentication. No MCP server required.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Authenticate with Google (opens browser): bash python scripts/auth.py login Check authentication status: bash python scripts/auth.py status Logout when needed: bash python scripts/auth.py logout
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: First-Time Setup

Authenticate with Google (opens browser):

python scripts/auth.py login

Check authentication status:

python scripts/auth.py status

Logout when needed:

python scripts/auth.py logout

Imported: Gmail Query Syntax

Gmail supports powerful search operators:

from:user@example.com

to:user@example.com

subject:meeting

is:unread

is:starred

is:important

has:attachment

after:2024/01/01

before:2024/12/31

newer_than:7d

older_than:1m

label:work

in:inbox

in:sent

in:trash

Combine with AND (space), OR, or - (NOT):

python scripts/gmail.py search "from:boss@company.com is:unread newer_than:1d"
python scripts/gmail.py search "subject:urgent OR subject:important"
python scripts/gmail.py search "from:newsletter@example.com -is:starred"

Examples

Example 1: Ask for the upstream workflow directly

Use @gmail-automation-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @gmail-automation-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @gmail-automation-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @gmail-automation-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Commands

All operations via

scripts/gmail.py

Search Emails

# Search with Gmail query syntax
python scripts/gmail.py search "from:someone@example.com is:unread"

# Search recent emails (no query returns all)
python scripts/gmail.py search --limit 20

# Filter by label
python scripts/gmail.py search --label INBOX --limit 10

# Include spam and trash
python scripts/gmail.py search "subject:important" --include-spam-trash

Read Email Content

# Get full message content
python scripts/gmail.py get MESSAGE_ID

# Get just metadata (headers)
python scripts/gmail.py get MESSAGE_ID --format metadata

# Get minimal response (IDs only)
python scripts/gmail.py get MESSAGE_ID --format minimal

Send Emails

# Send a simple email
python scripts/gmail.py send --to "user@example.com" --subject "Hello" --body "Message body"

# Send with CC and BCC
python scripts/gmail.py send --to "user@example.com" --cc "cc@example.com" --bcc "bcc@example.com" \
  --subject "Team Update" --body "Update message"

# Send from an alias (must be configured in Gmail settings)
python scripts/gmail.py send --to "user@example.com" --subject "Hello" --body "Message" \
  --from "Mile9 Accounts <accounts@mile9.io>"

# Send HTML email
python scripts/gmail.py send --to "user@example.com" --subject "HTML Email" \
  --body "<h1>Hello</h1><p>HTML content</p>" --html

Draft Management

# Create a draft
python scripts/gmail.py create-draft --to "user@example.com" --subject "Draft Subject" \
  --body "Draft content"

# Send an existing draft
python scripts/gmail.py send-draft DRAFT_ID

Modify Messages (Labels)

# Mark as read (remove UNREAD label)
python scripts/gmail.py modify MESSAGE_ID --remove-label UNREAD

# Mark as unread
python scripts/gmail.py modify MESSAGE_ID --add-label UNREAD

# Archive (remove from INBOX)
python scripts/gmail.py modify MESSAGE_ID --remove-label INBOX

# Star a message
python scripts/gmail.py modify MESSAGE_ID --add-label STARRED

# Unstar a message
python scripts/gmail.py modify MESSAGE_ID --remove-label STARRED

# Mark as important
python scripts/gmail.py modify MESSAGE_ID --add-label IMPORTANT

# Multiple label changes at once
python scripts/gmail.py modify MESSAGE_ID --remove-label UNREAD --add-label STARRED

List Labels

# List all Gmail labels (system and user-created)
python scripts/gmail.py list-labels

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/gmail-automation

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @game-design-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gdb-cli-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gdpr-data-handling-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @gemini-api-dev-v2

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Common Label IDs

INBOX

SENT

DRAFT

SPAM

TRASH

STARRED

IMPORTANT

UNREAD

Imported: Token Management

Tokens stored securely using the system keyring:

• macOS: Keychain
• Windows: Windows Credential Locker
• Linux: Secret Service API (GNOME Keyring, KDE Wallet, etc.)

Service name:

gmail-skill-oauth

Tokens automatically refresh when expired using Google's cloud function.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.