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CD WhatsApp Business Suite — AI Leads, Channels, Campaigns & 32 MCP Tools

Automate WhatsApp at scale — mine leads from groups with AI, broadcast to channel followers, bulk message with ban-safe delays, schedule campaigns, auto-reply in your voice, collect reviews, and track delivery. 90+ REST endpoints, 32 MCP tools for Claude & GPT, Python SDK. No Meta Business API required. Free tier available.

LeoYeAI/openclaw-master-skills Claude Code author: LeoYeAI MIT 4 files body ≈ 4 824 tokens Open the sourcegithub.com↗ analyzed 29 h ago

Automate WhatsApp at scale — mine leads from groups with AI, broadcast to channel followers, bulk message with ban-safe delays, schedule campaigns, auto-reply…

As a process D 40/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationWhatsAppZapierGitHubAI and agentsMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
63/100
safety, quality, tests
Safety 60%
73
Quality 40%
49
Run on models
none yet
Process rating
D
40/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: openclaw-master-skills, openclaw-master-skills

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 7

✓ No critical or high findings

Medium and low: 7
  • medium Exfiltration net-credential-use SKILL.md:103
    Credential used in a network call (verify the destination is the intended service)
    curl -H "X-API-Key: $MOLTFLOW_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:113
    Credential used in a network call (verify the destination is the intended service)
    curl -H "X-API-Key: $MOLTFLOW_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:123
    Credential used in a network call (verify the destination is the intended service)
    curl -H "X-API-Key: $MOLTFLOW_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:133
    Credential used in a network call (verify the destination is the intended service)
    curl -X POST -H "X-API-Key: $MOLTFLOW_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:146
    Credential used in a network call (verify the destination is the intended service)
    curl -X POST -H "X-API-Key: $MOLTFLOW_API_KEY" \
  • low Secrets in code secret-high-entropy-token integrations.md:156
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - **On-chain**: Query ERC-8004 Identity Registry at `0x80…432`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:322
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Registry | `0x80…432` |
    table

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-long name is longer than 64 chars
  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "requiredEnv"
  • note frontmatter-key unknown frontmatter key "primaryEnv"

Process rating: all ten parameters 40/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 20 mutating operations with no state check
  • 40Consistency. Frontmatter name (WhatsApp Business Suite — AI Leads, Channels, Campaigns & 32 MCP Tools) differs from the folder (whatsapp-automation-suite)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4824 tokens
  • 100Steps. 67 steps
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 325: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 67 items
  • +4Has examples (19 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 49.