Agent skill

Smart Email Agent

by LeoYeAI in LeoYeAI/openclaw-master-skills

All-in-one Gmail agent for OpenClaw. An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passedProductivity & Automation

Install Smart Email Agent

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill smart-email-agent -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills smart-email-agent --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/emailagy .claude/skills/smart-email-agent && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
smart-email-agent
GitHub stars
2.2k
Token cost
~4.7k tokens
SKILL.md length
619 words
Files
8 (incl. scripts, references, assets)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

All-in-one Gmail agent for OpenClaw. An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 5 steps: Verificar gog en PATH y GOG_ACCOUNT… → Construir query desde la intención del… → Ejecutar con --format minimal --json… → …
  • ANYTHING email-related: checking inbox
  • SKILL.md covers PARTE 1 — LEER CORREOS…, PARTE 2 — ORGANIZAR…, PARTE 3 — ANALIZAR Y… and PARTE 4 — RESPONDER Y REDACTAR…, plus 1 more section
  • Runs JavaScript and Python scripts from its folder; calls python3, npm and brew

What it does

Smart Email Agent is an agent skill from LeoYeAI/openclaw-master-skills. All-in-one Gmail agent for OpenClaw. Fuses email-reader, email-organizer, email-analyzer, email-responder, email-scheduler, and email-reporter into a single skill with token-optimizer integration and a self-improvement engine. Use this skill for ANYTHING email-related: checking inbox, searching messages, organizing labels, classifying/prioritizing, drafting replies, scheduling automation, generating reports, or reviewing costs. Triggers on: correo, email, inbox, bandeja, spam, draft, borrador, responder…

Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `_meta.json`, `assets/HEARTBEAT.email.md` and `hooks/HOOK.md`).

It sits in Productivity & Automation, covering Email management. It works with Gmail and Python. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • ANYTHING email-related: checking inbox
  • Searching messages
  • Organizing labels
  • Classifying/prioritizing

Example prompts

  • “/smart-email-agent”

Requirements

  • Python 3
  • Node.js
  • A credential in ANTHROPIC_API_KEY
  • A credential in SAFE_BROWSING_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Verificar gog en PATH y GOG_ACCOUNT configurado
  2. Construir query desde la intención del usuario (preguntar si es ambiguo)
  3. Ejecutar con --format minimal --json --max N
  4. Parsear JSON → presentar lista formateada
  5. Ofrecer: leer completo / buscar más / actuar sobre el mensaje

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (JavaScript and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • npm
    • brew

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Smart Email Agent loads about 4.7k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 187 tokens; SKILL.md has 619 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~187
When it runs · the whole SKILL.md, loaded when a task matches
~4.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 619 words, ~4,675 tokens.

Download SKILL.mdSave it as .claude/skills/smart-email-agent/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
smart-email-agent
description
All-in-one Gmail agent for OpenClaw. Fuses email-reader, email-organizer, email-analyzer, email-responder, email-scheduler, and email-reporter into a single skill with token-optimizer integration and a self-improvement engine. Use this skill for ANYTHING email-related: checking inbox, searching messages, organizing labels, classifying/prioritizing, drafting replies, scheduling automation, generating reports, or reviewing costs. Triggers on: correo, email, inbox, bandeja, spam, draft, borrador, responder, organizar, etiquetar, archivar, informe, estadísticas, notificame, automatiza, revisar mensajes, prioriza, cuanto cuesta, presupuesto, mejora el agente. Requires: gog CLI (primary) or Gmail API Python scripts (fallback).
version
3.0.0
tags
email, gmail, gog, inbox, spam, organizer, analyzer, responder, scheduler, reporter, cost, optimization, self-improvement, openclaw

Smart Email Agent v3 — Gmail All-in-One

Un solo skill que reemplaza los 6 skills del pack original. Punto de entrada único para toda la gestión de correo.

Lazy loading activo: este skill se carga completo (~500 tokens). NO cargues los 6 skills individuales — sería 4.800 tokens desperdiciados.


PARTE 1 — LEER CORREOS (email-reader)

Herramienta principal: gog CLI
bash
# Verificar prerequisitos
which gog || echo "Instalar: npm i -g gogcli  OR  brew install gogcli"
echo $GOG_ACCOUNT  # debe estar configurado

# Autenticar si es la primera vez
gog auth add $GOG_ACCOUNT
Comandos esenciales
bash
# No leídos en inbox (acción por defecto cuando el usuario dice "revisa mi correo")
gog gmail search 'in:inbox is:unread' --max 5 --format minimal --json

# Buscar por criterio — usar sintaxis Gmail
gog gmail search 'from:juan@empresa.com newer_than:3d' --max 10 --format minimal --json
gog gmail search 'subject:factura has:attachment' --max 10 --format minimal --json
gog gmail search 'in:spam is:unread' --max 20 --format minimal --json

# Leer correo completo
gog gmail get <message_id> --format full --json

# Leer hilo completo
gog gmail thread <thread_id> --format minimal --json
Operadores de búsqueda Gmail

from: to: subject: label: is:unread is:starred has:attachment newer_than:Nd older_than:Nd in:inbox in:sent in:spam in:trash filename:ext

Flujo estándar de lectura
  1. Verificar gog en PATH y GOG_ACCOUNT configurado
  2. Construir query desde la intención del usuario (preguntar si es ambiguo)
  3. Ejecutar con --format minimal --json --max N
  4. Parsear JSON → presentar lista formateada:
📬 5 correos no leídos:
1. De: Juan García <juan@empresa.com> | Asunto: Propuesta Q2 | Hace 2h
   Vista previa: Hola, te mando el resumen de... | ID: msg_abc123
  1. Ofrecer: leer completo / buscar más / actuar sobre el mensaje
Reglas de lectura
  • SIEMPRE usar --format minimal --json --max N (N=5 por defecto)
  • Nunca mostrar JSON crudo; nunca leer contenido completo sin pedirlo
  • Preservar IDs para acciones de seguimiento
  • Sin resultados → confirmar criterios, sugerir términos más amplios
  • Solo lectura — enviar/responder requiere la sección RESPONDER (Parte 4)
  • No guardar contenido de correos en MEMORY.md salvo que el usuario lo pida
Errores comunes
ErrorCausaSolución
gog: command not foundgog no instaladonpm i -g gogcli o brew install gogcli
GOG_ACCOUNT not setVariable no configuradaPedir al usuario su email Gmail
Token expiradoOAuth vencidogog auth add <email>
API error 429Rate limitEsperar 60s, reintentar con backoff

PARTE 2 — ORGANIZAR (email-organizer)

Jerarquía corporativa Corp/ (azul)
Corp/
├── Interno/
│   ├── Management      ← Gerencia, Directores
│   ├── Tech & Ops      ← Desarrollo, Soporte, Operaciones
│   ├── Commercial      ← Ventas, Marketing
│   ├── Admin & HR      ← Jurídica, RRHH, Contabilidad
│   └── Team            ← Resto del equipo @empresa.com
├── Partners & Clientes/
│   └── [empresa]       ← Wolkvox, Masiv, Unisanitas, Nuva, etc.
├── Proveedores/
│   └── [proveedor]     ← Google, Microsoft, AWS, etc.
└── Sistema/
    ├── DMARC
    ├── Notificaciones
    ├── Alertas
    └── No-Reply
Comandos de organización con gog
bash
# Crear etiqueta
gog gmail label create "Corp/Interno/Tech & Ops"

# Aplicar etiqueta a mensaje
gog gmail label apply <message_id> "Corp/Interno/Management"

# Mover correo (quitar INBOX + aplicar etiqueta)
gog gmail modify <message_id> --add-label "Corp/Partners & Clientes/Wolkvox" --remove-label INBOX

# Archivar (quitar INBOX sin borrar)
gog gmail modify <message_id> --remove-label INBOX

# Mover a spam
gog gmail modify <message_id> --add-label SPAM --remove-label INBOX

# Mover a papelera
gog gmail trash <message_id>

# Operación batch (múltiples IDs)
gog gmail batch-modify --ids id1,id2,id3 --add-label "Corp/Sistema/No-Reply" --remove-label INBOX
Reglas de routing automático

Antes de llamar a la IA, aplicar estas reglas sin costo:

from_domain @empresa.com + from_name contiene [Linda, Rafael, Director] → Corp/Interno/Management
from_domain @empresa.com + from_name contiene [Tech, Dev, Soporte]      → Corp/Interno/Tech & Ops
from_domain @empresa.com                                                  → Corp/Interno/Team
from contiene noreply / no-reply / donotreply                            → Corp/Sistema/No-Reply
subject contiene DMARC / SPF / DKIM                                      → Corp/Sistema/DMARC
subject contiene alerta / alert / warning                                → Corp/Sistema/Alertas

Guardar y mantener estas reglas en corp_routing_rules.json.

Protocolo de confirmación

NUNCA ejecutar acciones destructivas sin confirmación explícita:

⚠️ Pendiente de confirmación:
   → Mover 22 correos a Corp/Sistema/No-Reply
   → Eliminar etiqueta "noreply" (ya vacía)
   Esto NO borra correos, solo reorganiza etiquetas.
   ¿Confirmas? (sí/no)

PARTE 3 — ANALIZAR Y CLASIFICAR (email-analyzer)

Decisión de modelo ANTES de analizar
¿Tarea es clasificar / detectar spam / routing?
  → claude-haiku-4-5-20251001   (batch de 10-20 correos, ~$0.00009/correo)

¿Tarea es extraer tareas y fechas de correos importantes?
  → claude-haiku-4-5-20251001   (body[:800], ~$0.00015/correo)

¿El presupuesto está > 80% gastado?
  → forzar haiku para TODO, sin borradores automáticos

¿El presupuesto está > 95% gastado?
  → cero llamadas IA, solo reglas locales

Opus: PROHIBIDO para tareas de email. Sonnet: solo para borradores (ver Parte 4).

Pipeline de reducción de tokens (aplicar siempre)
python
# 1. Pre-filtro sin IA (resolver antes de gastar tokens)
#    - Dominio en corp_routing_rules.json → etiquetar directo
#    - from en known_spam_domains.txt → spam directo
#    - message_id ya en analysis_cache → reutilizar resultado
#    Objetivo: resolver 60-70% a costo $0.00

# 2. Recortar campos al mínimo necesario
CAMPOS = {
    'clasificacion': ['from', 'subject', 'snippet[:100]'],   # ~30 tokens
    'prioridad':     ['from', 'subject', 'body[:400]'],      # ~150 tokens
    'tareas_fechas': ['from', 'subject', 'body[:800]'],      # ~250 tokens
}

# 3. Limpiar texto
def limpiar(texto, limite):
    texto = re.sub(r'<[^>]+>', '', texto)             # quitar HTML
    texto = re.sub(r'https?://\S+', '[URL]', texto)   # comprimir URLs
    texto = re.sub(r'\s+', ' ', texto).strip()
    return texto[:limite]

# 4. Batch: NUNCA menos de 10 correos por llamada
#    Esperar hasta tener 10-20 correos pendientes
BATCH_MIN = 10
BATCH_MAX = 20
Prompt de análisis en batch (Haiku)
SYSTEM (idéntico siempre — para prompt caching):
Eres un clasificador de correos corporativos.
Analiza cada correo y devuelve SOLO JSON array. Sin texto extra.
Para cada ítem: {"idx":N,"corp_label":"...","categoria":"spam|importante|informativo|sistema|otro",
"prioridad":0-10,"es_spam":bool,"necesita_respuesta":bool,
"tiene_phishing":bool,"tareas":[],"fecha_limite":"ISO o null","razon":"máx 10 palabras"}

USER: Analiza: [JSON array de hasta 20 correos con from+subject+snippet[:100]]
Presentación de resultados
🤖 Análisis — 47 correos procesados
⚡ Sin IA (pre-filtro): 31  (66%) → $0.000
🧠 Con Haiku (2 batches): 16     → $0.006

📊 Resultado:
  🔵 Corp/Interno/Management:   2  (prioridad alta)
  🔵 Corp/Partners & Clientes:  8
  🔵 Corp/Sistema/No-Reply:    14
  🗑️  Spam:                    12
  ⚠️  Phishing detectado:        1  → ALERTA
  📋 Con tareas pendientes:      4

Críticos:
  [10/10] linda@empresa.com — "Aprobación contrato urgente"
          Tarea: confirmar antes del viernes

PARTE 4 — RESPONDER Y REDACTAR (email-responder)

Cuándo usar Sonnet vs Haiku para borradores
Prioridad >= 8 → claude-sonnet-4-6       (calidad importa)
Prioridad 5-7  → claude-haiku-4-5-20251001  (suficiente, más barato)
Prioridad < 5  → NO generar borrador automático

Máximo 3 borradores por sesión cuando presupuesto < 60%. Máximo 1 borrador por sesión cuando presupuesto 60-80%. Cero borradores automáticos cuando presupuesto > 80%.

Flujo de respuesta
bash
# 1. Leer el hilo completo
gog gmail thread <thread_id> --format minimal --json

# 2. Preparar contexto recortado para la IA
#    Solo: from + subject + body[:600] del último mensaje + resumen del hilo anterior
Prompt de generación de borrador
Redacta una respuesta profesional y concisa (máx 150 palabras).
Solo el cuerpo del mensaje, sin asunto ni encabezados.
Tono: profesional pero cercano.
Firma: [NOMBRE_USUARIO]

Hilo: [RESUMEN + ÚLTIMO MENSAJE RECORTADO]
Presentar borrador al usuario
✍️ Borrador para: juan@empresa.com
   Re: Propuesta Q2 2026
────────────────────────
Hola Juan,

Gracias por el resumen. Me parece viable la dirección propuesta.
¿Podemos agendar una llamada esta semana?

Saludos,
[Tu nombre]
────────────────────────
[1] Guardar borrador   [2] Editar   [3] Enviar ahora   [4] Descartar
Enviar con gog
bash
# Guardar como borrador
gog gmail draft create --to "juan@empresa.com" \
  --subject "Re: Propuesta Q2 2026" \
  --body "Hola Juan,..." \
  --reply-to <message_id>

# Enviar borrador guardado
gog gmail draft send <draft_id>

# Enviar directamente (SIEMPRE pedir confirmación antes)
gog gmail send --to "juan@empresa.com" --subject "..." --body "..."
Templates de respuesta rápida
acuse_recibo:      "Recibido, te respondo a la brevedad."
confirmar_reunion: "Confirmado para [fecha/hora]. Hasta entonces."
solicitar_info:    "Necesito más información sobre X para proceder."
ausencia:          "Estoy fuera hasta [fecha]. Respondo a mi regreso."
Show full SKILL.md (246 more words)Show less
Follow-ups automáticos
bash
# Buscar correos enviados sin respuesta en últimos 5 días
gog gmail search 'in:sent newer_than:5d' --max 20 --format minimal --json
# Cruzar con INBOX para detectar cuáles no tienen respuesta

PARTE 5 — AUTOMATIZAR (email-scheduler)

Heartbeat optimizado: 55 minutos

Por qué 55 min: el caché de Anthropic expira a los 60 minutos. Con heartbeat de 55 min, el agente mantiene el caché caliente → cada mensaje paga cache-read en lugar de cache-write (3.75x más barato).

json
// ~/.openclaw/openclaw.json
{
  "agents": {
    "email-assistant": {
      "heartbeat": { "every": "55m" },
      "model": "anthropic/claude-haiku-4-5-20251001"
    }
  }
}
Cron jobs recomendados
json
{
  "cron": {
    "jobs": [
      {
        "id": "email-priority-check",
        "schedule": "*/55 * * * *",
        "description": "Revisar correos importantes — modelo Haiku",
        "message": "Revisa inbox no leídos. Si hay prioridad >= 8, notifícame.",
        "model": "anthropic/claude-haiku-4-5-20251001",
        "enabled": true
      },
      {
        "id": "email-spam-cleanup",
        "schedule": "0 8 * * *",
        "description": "Limpieza diaria de spam — solo reglas locales, costo $0",
        "message": "Aplica reglas locales de spam. Sin llamadas IA.",
        "model": "anthropic/claude-haiku-4-5-20251001",
        "enabled": true
      },
      {
        "id": "email-weekly-report",
        "schedule": "0 9 * * MON",
        "description": "Informe semanal",
        "message": "Genera informe semanal de correos con email-reporter.",
        "model": "anthropic/claude-haiku-4-5-20251001",
        "enabled": true
      }
    ]
  }
}

Regla de oro para crons: SIEMPRE especificar claude-haiku-4-5-20251001. Usar Opus para un cron de 10 tareas/día = $17.70/mes extra innecesario.

HEARTBEAT.md de correo
markdown
## Email Heartbeat — Modelo: claude-haiku-4-5-20251001

### Check de correos (cada 55 min)
1. gog gmail search 'in:inbox is:unread' --max 10 --format minimal --json
2. Aplicar reglas locales de corp_routing_rules.json
3. Si hay correo con keywords urgente/crítico/emergencia → notificar
4. Si no hay urgentes → HEARTBEAT_OK (silencio)

### Check de spam (cada 2h, sin IA)
1. gog gmail search 'in:spam is:unread' --max 50 --format minimal --json
2. Aplicar known_spam_domains.txt → mover a trash directo
3. Sin llamadas a IA

Quiet hours: 23:00–07:00 → HEARTBEAT_OK automático
Gmail Push Notifications (tiempo real)
bash
# Configurar webhook Pub/Sub
python3 scripts/setup_pubsub.py --topic "email-agent-notifications"

# El webhook dispara cuando llega un correo nuevo:
openclaw message "Nuevo correo. Revisa con gog y notifícame si es importante."
Activar automatización completa
Usuario: "Activa el agente de correo en modo automático"
Agente:
  1. Verificar: gog auth status
  2. Crear cron jobs recomendados (ver arriba)
  3. Copiar HEARTBEAT.md al workspace
  4. Preguntar: ¿activar Gmail Push para tiempo real?
  5. Confirmar canal de notificaciones (NOTIFY_CHANNEL)
  6. "✅ Agente activado. Reviso cada 55 min. Te aviso si hay algo importante."

PARTE 6 — INFORMES Y ESTADÍSTICAS (email-reporter)

Tipos de informe
bash
# Resumen del día (al final de sesión — SIEMPRE mostrar)
# Ver sección "Resumen de costos" más abajo

# Estadísticas de spam
gog gmail search 'in:trash newer_than:30d' --max 100 --format minimal --json
# Parsear y agrupar por dominio remitente

# Tareas pendientes en correos
# Consultar analysis_cache donde tareas[] no está vacío y sin respuesta

# Log de prompts IA detectados
cat .learnings/PROMPTS_DETECTADOS.md

# Historial de acciones del agente
cat email_audit.log | tail -50

# Deshacer última acción
gog gmail modify <ids_from_audit_log> --remove-label TRASH --add-label INBOX
Resumen de costos — mostrar al cerrar cada sesión
💰 Sesión de hoy
  Correos procesados:    47
  ├─ Sin IA (reglas):    31  (66%) → $0.000
  ├─ Haiku (2 batches):  14       → $0.005
  └─ Sonnet (borradores): 2       → $0.005

  Tokens consumidos: 5.090
  Costo sesión:     $0.010
  Ahorro estimado:  $0.040 (80% vs. sin optimizar)

  Presupuesto mes:  $X.XX gastado / $Y.YY total (N%)
  Proyección mes:   $Z.ZZ

PARTE 7 — MOTOR DE AUTO-MEJORA

Cuándo capturar un aprendizaje
EventoArchivoID
Usuario corrige clasificación.learnings/LEARNINGS.mdLRN-YYYYMMDD-NNN
Costo sesión > 2x el promedio.learnings/LEARNINGS.mdLRN-YYYYMMDD-NNN
Error de API (rate limit, auth).learnings/ERRORS.mdERR-YYYYMMDD-XXX
Optimización reduce costos >10%.learnings/LEARNINGS.mdLRN-YYYYMMDD-NNN
Remitente recurrente sin regla.learnings/LEARNINGS.mdLRN-YYYYMMDD-NNN
Formato
markdown
## [LRN-YYYYMMDD-NNN] <tipo>
**Logged**: ISO timestamp
**Priority**: low | medium | high
**Status**: pending | applied | promoted
**Area**: cost | classification | routing | api | spam | drafts

### Summary
Una línea con el aprendizaje y su impacto.

### Details
Qué pasó. Qué se asumía vs. qué era verdad.

### Action
Cambio concreto: qué archivo editar, qué valor cambiar.

### Impact
Ahorro estimado: $X/mes | Tokens -N%
---
Ciclo al cerrar sesión
1. Revisar .learnings/ con Status=pending
2. ¿Learnings con Impact > $0.01/mes?
   → Proponer: "Aprendí que X. ¿Lo aplico a las reglas?"
   → Si acepta → editar corp_routing_rules.json → Status=applied
3. ¿3+ learnings del mismo dominio/patrón?
   → Promover a regla permanente sin preguntar
   → Status=promoted
4. Reportar: "Apliqué N mejoras. Ahorro estimado: $X/mes"
Efecto compuesto del aprendizaje
Mes 1: ~$0.50/mes  (0 reglas)
Mes 2: ~$0.30/mes  (10 reglas aprendidas)
Mes 3: ~$0.18/mes  (25 reglas)
Mes 6: ~$0.10/mes  (60+ reglas)

PARTE 8 — CONTROL DE PRESUPUESTO

Cuatro modos operativos
% gastadoModoRestricciones
0–59%Normal ✅Todo habilitado
60–79%Ahorro leve 🟡Avisar. Máx 2 borradores/sesión
80–94%Ahorro fuerte 🟠Solo Haiku. Sin borradores auto. Batch obligatorio ≥20
95–100%Emergencia 🔴Cero IA. Solo gog + reglas locales
Diagnósticos con token-optimizer (si está instalado)
bash
/context list    # qué archivos consumen tokens ahora
/usage full      # tokens + costo por respuesta
/usage cost      # resumen acumulado de sesión
/status          # modelo activo, % contexto

python3 scripts/token_tracker.py check   # estado del budget diario
python3 scripts/model_router.py "analizar correos nuevos"  # qué modelo usar

Referencias — leer cuando necesites más detalle

  • references/cost-optimization.md — Técnicas avanzadas: prompt caching, deduplicación semántica, modo emergencia
  • references/learning-patterns.md — Patrones de auto-mejora y ciclo de vida de learnings
  • hooks/openclaw-handler.js — Inyecta estado de presupuesto + modo activo al inicio de sesión
  • scripts/init_orchestrator.py — Setup inicial: verifica gog, crea budget_tracker, SKILLS_INDEX
  • assets/HEARTBEAT.email.md — Plantilla lista para copiar al workspace

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/emailagy of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • assets/HEARTBEAT.email.md
  • hooks/HOOK.md
  • hooks/openclaw-handler.js
  • references/cost-optimization.md
  • references/learning-patterns.md
  • scripts/init_orchestrator.py

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Smart Email Agent next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Smart Email Agent compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Smart Email Agent this skillLeoYeAI/openclaw-master-skills2.2k—~4.7kAutomated safety check: PassMIT
Performance Patternss-morgan-jeffries/apple-mail-fast-mcp104—~1.5kAutomated safety check: PassMIT
Prismer Google WorkspacePrismer-AI/PrismerCloud1.6k3 repos~4.2kAutomated safety check: PassMIT
Google WorkspaceRedWoodOG/Hermes-Desktop177—~2.1kAutomated safety check: PassMIT
Community Google WorkspaceArgentAIOS/argentos-core126—~2.8kAutomated safety check: PassMIT
Google WorkspaceTommy-yw/RunbookHermes5461 repos~2.7kAutomated safety check: PassMIT

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Works with

Questions about Smart Email Agent

What does Smart Email Agent do?

All-in-one Gmail agent for OpenClaw. An agent skill from LeoYeAI/openclaw-master-skills. Smart Email Agent is an agent skill from LeoYeAI/openclaw-master-skills. All-in-one Gmail agent for OpenClaw.

When should I use Smart Email Agent?

Smart Email Agent fits situations like: ANYTHING email-related: checking inbox; searching messages; organizing labels; classifying/prioritizing.

How do I install Smart Email Agent in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill smart-email-agent -a claude-code`. Or copy the skill folder (skills/emailagy in LeoYeAI/openclaw-master-skills) into .claude/skills/smart-email-agent in your project. Claude Code loads it when a task matches its description.

How do I install Smart Email Agent in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill smart-email-agent -a codex`. Or copy the skill folder (skills/emailagy in LeoYeAI/openclaw-master-skills) into .agents/skills/smart-email-agent in your project. Codex loads it when a task matches its description.

Can I use Smart Email Agent in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill smart-email-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/smart-email-agent, .gemini/skills/smart-email-agent, .github/skills/smart-email-agent and .opencode/skills/smart-email-agent in your project.

What does Smart Email Agent need to run?

Going by SKILL.md and its folder, Smart Email Agent needs JavaScript and Python for the scripts in its folder and the command-line tools its instructions call (python3, npm and brew). Our summary lists: Python 3; Node.js; A credential in ANTHROPIC_API_KEY; A credential in SAFE_BROWSING_API_KEY.

Does Smart Email Agent access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Smart Email Agent safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Smart Email Agent use?

Smart Email Agent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Smart Email Agent use?

About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 779 tokens, read only when the agent opens those files.

What are the alternatives to Smart Email Agent?

Skills that share tags, products or a category with Smart Email Agent: Performance Patterns (s-morgan-jeffries/apple-mail-fast-mcp, 104 stars), Prismer Google Workspace (Prismer-AI/PrismerCloud, 1.6k stars), Google Workspace (RedWoodOG/Hermes-Desktop, 177 stars) and Community Google Workspace (ArgentAIOS/argentos-core, 126 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Smart Email Agent?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,160 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.