Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/axisrow/claude_code_gmail_plugin/auto-tagnpx skills add axisrow/claude_code_gmail_plugin --skill auto-taggit clone --depth 1 https://github.com/axisrow/claude_code_gmail_pluginWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00041 | $0.00530 |
| Opus 5 | $0.00020 | $0.00265 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
auto-tag scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Auto-Tag Gmail Emails
Analyze emails and apply label recommendations with user confirmation.
Arguments
$ARGUMENTS — optional Gmail search query. If empty, use recent INBOX messages.
Step 1: Fetch emails and labels
- Use Gmail MCP
gmail_search_messagesto fetch emails (use query from$ARGUMENTSorin:inbox, limit 10-20) - Use Gmail MCP
gmail_list_labelsto get available user labels - For each email, use
gmail_read_messageto get full content and current labels
Step 2: Classify with tagging context
For each email, classify into one of 5 categories:
- 🟢 PERSONAL / ЛИЧНОЕ — addressed personally, requires response
- 🔵 USEFUL NEWSLETTER / ПОЛЕЗНАЯ РАССЫЛКА — consciously subscribed content
- 🟡 IMPORTANT NOTIFICATION / ВАЖНОЕ УВЕДОМЛЕНИЕ — transactional, worth reading
- 🟠 NOISE / ШУМ — repetitive alerts, noise
- 🔴 SPAM / МУСОР — marketing, promotions, spam
For each email provide:
- Category (with emoji)
- Brief summary
- Recommendation
- Suggested labels from the available user label list
Step 3: Generate tagging proposal
Create a summary table of proposed changes:
- For each email: abbreviated subject, current labels, proposed new labels (by name)
- Only propose labels that are NOT already on the email
Step 4: Confirm with user
Ask the user to confirm: "Apply these tags? (y/n)"
Do NOT proceed without explicit user confirmation.
Step 5: Apply tags (only after confirmation)
After user confirms, construct JSON and pipe to the apply script:
echo '{"actions": [{"message_id": "MSG_ID", "add": ["Label_ID"]}]}' | python3 ~/.claude/scripts/gmail-analyzer/modify_labels.py
Report results: how many emails were tagged successfully.
If user declines, report "Cancelled." and do not apply.
Respond in the user's language.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 63 lines · 41 tokens per session scan A 168f88f57baf
auto-tag is a skill published in the GitHub repository axisrow/claude_code_gmail_plugin (3 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 530 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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