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 commands/alanjmr21/whatsapp-agent-kit/build-agentgit clone --depth 1 https://github.com/alanjmr21/whatsapp-agent-kitWhat 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 | $0.00000 | $0.00508 |
| Opus 5 | $0.00000 | $0.00254 |
| Sonnet 5 | $0.00000 | $0.00102 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
build-agent 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 3d 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.
What it actually says
Lee el archivo CLAUDE.md completo. Contiene todas las instrucciones detalladas.
Ejecuta el flujo de onboarding de AgentKit siguiendo las 5 fases EN ORDEN:
FASE 1 — Bienvenida y verificación del entorno
- Muestra el mensaje de bienvenida
- Verifica Python >= 3.11
- Crea las carpetas necesarias (agent/, agent/providers/, config/, knowledge/, tests/)
- Genera requirements.txt e instala dependencias
- Crea .env base
FASE 2 — Entrevista del negocio
- Haz las 10 preguntas UNA POR UNA
- Espera respuesta antes de continuar a la siguiente
- PREGUNTA 9: el usuario elige su proveedor de WhatsApp (Whapi/Meta/Twilio)
- PREGUNTA 10: pide las credenciales específicas del proveedor elegido
- Guarda todas las respuestas para la Fase 3
FASE 3 — Generación del agente
- Genera config/business.yaml con datos del negocio
- Genera config/prompts.yaml con system prompt poderoso y específico
- Si hay archivos en /knowledge, léelos e incorpóralos al prompt
- Genera agent/providers/ con el proveedor elegido (base.py + init.py + adaptador)
- Genera agent/main.py (FastAPI + webhook provider-agnostic)
- Genera agent/brain.py (Claude API)
- Genera agent/memory.py (SQLite + historial)
- Genera agent/tools.py (herramientas según caso de uso)
- Genera tests/test_local.py (simulador de chat)
- Genera Dockerfile y docker-compose.yml
- Configura .env con WHATSAPP_PROVIDER y las API keys del usuario
FASE 4 — Testing local
- Ejecuta python tests/test_local.py
- El usuario chatea con su agente en la terminal
- Si hay ajustes, modifica prompts.yaml y repite
- No avanza sin aprobación del usuario
FASE 5 — Deploy a Railway
- Solo si el usuario quiere
- Build Docker + instrucciones de Railway
- Configuración de webhook específica para el proveedor elegido
REGLAS:
- Habla siempre en español
- Una pregunta a la vez
- Nunca hardcodees API keys
- No avances de fase sin confirmación
- El agente debe funcionar antes de hablar de deploy
- Genera SOLO el adaptador del proveedor elegido (no los 3)
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.
- 3d ago First seen · 50 lines · 0 tokens per session scan A 3ec56b1cb9ec
build-agent is a command published in the GitHub repository alanjmr21/whatsapp-agent-kit (11 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 508 tokens. 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.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.