agentmods.dev

whatsapp-send

whatsapp-send is a skill for Claude Code from razvangirgiz/wazap. It costs 64 tokens per session (776 once invoked), scanned A, original, MIT.

A controlled WhatsApp sending tool for messages, replies, forwards, files, reactions, and polls. It drafts the action first and sends it from the user's own account only after approval.

In plain words
What is it for?
Use it to send or reply to WhatsApp messages, forward content, share files, react to messages, and create polls after resolving the intended chat.
Why use it?
It reduces the risk of sending the wrong content or sending to the wrong person by showing the recipient and exact action before anything leaves the account.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the wazap plugin — 5 skills, 1 MCP server shipped together

not rated 0repo changed 5d ago A scan 64 tokens original MIT

Good fit Use it to send or reply to WhatsApp messages, forward content, share files, react to messages, and create polls after resolving the intended chat.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/razvangirgiz/wazap/whatsapp-send
View source ↗ razvangirgiz/wazap
Install

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.

Any agent
npx skills add razvangirgiz/wazap --skill whatsapp-send
Clone the repo
git clone --depth 1 https://github.com/razvangirgiz/wazap

Made for: Claude Code.

Or install wazap, the plugin that ships this one along with the rest of its 5 skills, 1 MCP server.

Wrote 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.

agentmods badge for whatsapp-send

README.md
[![agentmods](https://agentmods.dev/badge/skills/razvangirgiz/wazap/whatsapp-send/github.svg)](https://agentmods.dev/skills/razvangirgiz/wazap/whatsapp-send)
Your own site
<a href="https://agentmods.dev/skills/razvangirgiz/wazap/whatsapp-send"><img src="https://agentmods.dev/badge/skills/razvangirgiz/wazap/whatsapp-send/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.

agentmods 80×15 button for whatsapp-send

Your own site · 80×15
<a href="https://agentmods.dev/skills/razvangirgiz/wazap/whatsapp-send"><img src="https://agentmods.dev/badge/skills/razvangirgiz/wazap/whatsapp-send.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 776 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.00776
Opus 5 $0.00032 $0.00388
Sonnet 5 $0.00013 $0.00155
Haiku 4.5 $0.00006 $0.00078

Measured 5d ago against content hash c1394d059124, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

whatsapp-send 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 5d 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.

skills/whatsapp-send/SKILL.md · 39 lines

How it starts

The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.

WhatsApp send

A message sent here is indistinguishable from one the user typed. The rail: the user sees recipient and exact text, says yes, then it goes. One approval covers one message to one chat.

Resolve the recipient

  1. search_contacts with the name. Exactly one match: use its chat_id. Several: list them with numbers and ask. None: ask for the number in international format; NOT_ON_WHATSAPP means the number is wrong, not that you should retry.
  2. Groups come from list_chats with filter: "groups". Before posting, get_group_info; if announcement_only is true and the user is not admin, say so instead of trying.
  3. A reply to a specific message needs its message_id from read_messages; pass it as reply_to so the quote shows.

Draft

  1. read_messages on the chat, limit: 20, and match the register already in use: language (Romanian or English), formality, emoji, length. A two-line chat gets a two-line reply.
  2. Write the message as the user, first person, without a signature or "sent by an assistant".
  3. Files: send_media needs a local file_path that exists on the machine running wazap, or a public URL. Check the path before drafting; pick as_document: true for PDFs and anything the recipient should keep at original quality, as_voice: true only for audio meant as a voice note, as_gif: true for a .gif or an mp4 meant to loop like a GIF (a .gif needs ffmpeg on that machine).
  4. Call the matching send tool (send_message / send_media / send_poll / send_location / forward_message). It does not send. It returns a draft_id and a preview.

Confirm, then send

Show the preview the tool returned, exactly, and wait for a yes:

To: Ana (+40 722 …)
"Joi la 10 e perfect, ne vedem la notar. Aduc eu actele."

On the user's yes, call confirm_send with that draft_id. Do not call the send tool again. Report the result with the message_id so the user can follow up with edit_message (own messages, 15 minutes) or delete_message. DRAFT_EXPIRED (15 minutes) or DRAFT_NOT_FOUND means draft again, show the new preview, and wait for another yes.

Read the full file on GitHub · 39 lines

Changes

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.

  1. 5d ago Changed c1394d059124
  2. 10d ago First seen · 39 lines · 64 tokens per session scan A 439fd0b7be9c

Subscribe to this mod's changes

whatsapp-send is a skill published in the GitHub repository razvangirgiz/wazap (0 stars, last pushed 2d ago), licensed MIT. It adds 64 tokens to every session and 776 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens