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gmail-labelGmail label 搜索

Agent Skill

gmail-label 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

353

周安装

15

GitHub Stars

9

下载量

124
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:gmail-label(Gmail label 搜索)
来源仓库:https://github.com/aiagentwithdhruv/skills
仓库路径:skills/gmail-label
安装命令:
npx skills add https://github.com/aiagentwithdhruv/skills --skill gmail-label
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aiagentwithdhruv/skills --skill gmail-label

简介

gmail-label 用于查找、检索和筛选相关信息,支持关键词匹配。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中的信息定位需求。
  • 结合来源仓库和原始 README 可进一步核验具体用法。
  • 安装命令:npx skills add https://github.com/aiagentwithdhruv/skills --skill gmail-label。
  • 建议确认权限范围和维护状态,避免触发联网或文件操作。

SKILL.md

Gmail Auto-Label

Goal

Fetch inbox emails, classify them via parallel subagents into Action Required / Waiting On / Reference, and apply labels in bulk via Gmail API.

Scripts

  • ./scripts/gmail_label_fetch.py - Fetch email summaries as compact JSON
  • ./scripts/gmail_label_split.py - Split emails into N chunks for parallel classification
  • ./scripts/gmail_label_merge.py - Merge classified chunks into single labels.json
  • ./scripts/gmail_label_apply.py - Apply label classifications in bulk

Subagent

  • email-classifier — defined in .claude/agents/email-classifier.md
  • Model: Sonnet 4.5 (fast, cost-efficient classification)
  • Each subagent reads one chunk, writes one classified output file

Flow (Parallel — default)

Step 1: Fetch emails

python3 .claude/skills/gmail-label/scripts/gmail_label_fetch.py \
  --account ACCOUNT --query "in:inbox" --limit 100 --output .tmp/emails.json

Step 2: Split into chunks

python3 .claude/skills/gmail-label/scripts/gmail_label_split.py \
  --input .tmp/emails.json --chunks 10 --output-dir .tmp/chunks

Step 3: Classify in parallel (spawn 10 subagents)

Spawn 10 email-classifier subagents in background, one per chunk. Each subagent:

  • Reads .tmp/chunks/chunk_N.json
  • Classifies each email
  • Writes .tmp/chunks/classified_N.json

Use the Task tool with run_in_background: true and model: "sonnet". Launch ALL 10 in a single message for true parallelism:

For each chunk 0-9, spawn a Task with:
  subagent_type: "email-classifier"
  model: "sonnet"
  run_in_background: true
  prompt: "Read /absolute/path/.tmp/chunks/chunk_N.json, classify each email, write results to /absolute/path/.tmp/chunks/classified_N.json"

CRITICAL: Do NOT use TaskOutput to read subagent results. The subagents write their results to files — the main agent never needs to see the classification data. Reading TaskOutput will flood the context window and cause "prompt too long" errors with large batches (500+ emails).

Instead, poll for file existence:

# Wait until all classified files exist (timeout after 120s)
for i in $(seq 0 9); do
  while [ ! -f ".tmp/chunks/classified_$i.json" ]; do sleep 2; done
done

Then proceed directly to Step 4 (merge).

Step 4: Merge classifications

python3 .claude/skills/gmail-label/scripts/gmail_label_merge.py \
  --input-dir .tmp/chunks --output .tmp/labels.json

Step 5: Apply labels

python3 .claude/skills/gmail-label/scripts/gmail_label_apply.py \
  --account ACCOUNT --input .tmp/labels.json

Classification Guidelines

Action Required:

  • Security alerts that need verification
  • Expiring credit cards / domain renewals with deadlines
  • Slack @mentions asking questions
  • New team members to greet (Slack join notifications)
  • Client emails needing response
  • Business listing updates (Google Business Profile, Bing Places)
  • Stripe action-required notices

Waiting On:

  • Outbound sales emails awaiting reply
  • Support tickets awaiting resolution
  • Proposals sent, pending response

Reference:

  • Marketing newsletters (DigitalMarketer, etc.)
  • Charity/nonprofit newsletters (RAPS, etc.)
  • Google Business Profile performance reports
  • Promotional offers (Blinkist, sales, etc.)
  • Platform update notifications (Google Play, Apify, etc.)
  • Confirmation codes (already used)
  • Real estate newsletters (Westbank, etc.)
  • Gaming account emails (Riot Games, etc.)
  • Informational security alerts (2FA turned on, etc.)
  • Health advisories
  • Legal/policy update notices

Account Registry

Accounts are stored in gmail_accounts.json at workspace root. Each account needs:

  • email - Gmail address
  • token_file - Path to OAuth token

Adding New Accounts

python3 .claude/skills/gmail-inbox/scripts/gmail_multi_auth.py --account ACCOUNT_NAME --email EMAIL

Performance

  • Serial flow: ~36s for 100 emails (fetch 1s + classify 34s + apply 1s)
  • Parallel flow: ~30s for 100 emails (classify 19s + merge <1s + apply ~10s)
  • Classification is the bottleneck; 10 parallel subagents cut it from 34s to 19s (~1.8x speedup)
  • Agent startup overhead (~4s stagger) limits theoretical gains
  • Apply step may vary due to Gmail API throttling

Schema

Inputs

NameTypeRequiredDescription
accountstringYesGmail account name from registry
querystringNoGmail search query (default: 'in:inbox')
limitintegerNoMax emails to classify (default: 100)
chunksintegerNoParallel classification chunks (default: 10)

Outputs

NameTypeDescription
labels_appliedobjectCount per category: Action Required, Waiting On, Reference

Credentials

NameSource
credentials.jsonfile
token_*.jsonfile (auto-generated)

Composable With

Skills that chain well with this one: gmail-inbox

Cost

Claude Sonnet API for classification

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.64%
按下载量换算43

Claude

31.31%
按下载量换算39

Cursor

19.97%
按下载量换算25

Gemini CLI

10.41%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

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通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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