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 skills add PsychQuant/che-apple-mail-mcp --skill bulk-operation-previewgit clone --depth 1 https://github.com/PsychQuant/che-apple-mail-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/psychquant/che-apple-mail-mcp/bulk-operation-preview)<a href="https://agentmods.dev/skills/psychquant/che-apple-mail-mcp/bulk-operation-preview"><img src="https://agentmods.dev/badge/skills/psychquant/che-apple-mail-mcp/bulk-operation-preview/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/psychquant/che-apple-mail-mcp/bulk-operation-preview"><img src="https://agentmods.dev/badge/skills/psychquant/che-apple-mail-mcp/bulk-operation-preview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00064 | $0.01517 |
| Opus 5 | $0.00032 | $0.00758 |
| Sonnet 5 | $0.00013 | $0.00303 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
bulk-operation-preview 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 11d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk Operation Preview
當 search 結果 ≥ 5 emails 或操作會 touch 多個 emails 時,先 show 結構化 preview 讓 user 過目。
Preview Format
基本結構
搜尋結果:{N} 封 emails (by {filter_summary})
Threads 分布(按時間排序):
✓ [{date}] {thread_key} ({M} msgs)
✓ [{date}] {thread_key} ({M} msgs)
⚠ [{date}] {thread_key} ({M} msgs) ← potential false positive,reason: {reason}
...
附件:{K} 個檔案 ({total_size} 預估)
- {file_1.pdf} ({size_1})
- ...
⚠ {flag_count} 個 threads 標示為 potential false positive
要排除嗎?確認操作範圍?
Pipeline-style filter summary
借 nsql pipeline format:
Mail
-> filter(sender = '[email protected]'
OR sender = '[email protected]'
OR subject contains '陳君厚')
-> group(thread_key)
-> sort(first_message asc)
讓 user 一眼看出 search 邏輯。
False-positive flagging(整合 false-positive-detection.md)
每 thread 跑 false-positive detection:
| Flag | 條件 |
|---|---|
| ✓ | sender/recipient 直接匹配 filter 中的 email 地址 |
| ⚠ | sender 不在 filter 但 subject 含 filter 關鍵字(可能 false positive) |
| ⚠⚠ | sender 完全不在 filter 且 subject 也只含通用詞(高機率 false positive) |
| ❓ | metadata 不足以判斷(少於 1 封 email 在 thread) |
詳見 rules/false-positive-detection.md。
Side-effect scope counting
在 Phase 3 (Operation Confirmation) 之前,count operation 影響的 scope:
我將執行:
- 寫入 {N} 個 markdown 檔到 {output_dir}/
- 下載 {K} 個 attachments ({total_size}) 分流到:
→ {data_dir}/ ({data_count} 個資料檔)
→ {documents_dir}/ ({doc_count} 個文件附件)
- 建立/更新 {index_count} 個 index files
- {modify_mailapp ? "修改 Mail.app 標記/移動" : "不修改 Mail.app 內容(read-only)"}
預估磁碟用量:{disk_estimate}
預估執行時間:{time_estimate}
⚠ 此操作{reversible ? "可以 undo(刪除生成的檔案)" : "無法 undo"}
確認執行嗎?
Thread breakdown 細節
按 first message date 排序,format:
[YYYY-MM-DD] {thread_key} ({msg_count} msgs)
Participants: {top 3 participants}
First: {first_sender}, Last: {last_sender}
對 chchen 案例:
[2026-04-29] 新聘博士後研究學者繳交資料 (2 msgs)
Participants: yijulee, che830621, cchen, coco891017
First: yijulee, Last: che830621
[2026-04-19] 後續面試報告時間請教 (5 msgs)
Participants: che830621, cchen, coco891017, yijulee, tsoping
First: che830621, Last: che830621
⚠ [2026-03-15] 應徵資料科學統計合作社... (1 msg)
Participants: che830621, scchen
First/Last: che830621
⚠ Reason: sender (scchen) 不在 filter pattern,subject 含「陳」但實際對象是另一個 unit
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.
- 11d ago First seen · 155 lines · 64 tokens per session scan A 1dd6d74d2ee1
bulk-operation-preview is a skill published in the GitHub repository PsychQuant/che-apple-mail-mcp (8 stars, last pushed 2d ago), licensed MIT. It adds 64 tokens to every session and 1,517 once invoked, about $0.0003 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 2026-08-30.
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