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BB whatsapp-cloud-api

Integracao com WhatsApp Business Cloud API (Meta). Mensagens, templates, webhooks HMAC-SHA256, automacao de atendimento. Boilerplates Node.js e Python.

Not recommendedcritical or high security findings
sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 31 files body ≈ 4 421 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Integracao com WhatsApp Business Cloud API (Meta).

As a process B 77/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

IntegrationWhatsAppSoftware developmentAI and agentsCustomer supporttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
75/100
safety, quality, tests
Safety 60%
67
Quality 40%
88
Run on models
none yet
Process rating
B
77/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Exfiltration
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. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 8

  • high Exfiltration exfil-webhook-url references/webhook-setup.md:458
    Webhook / callback URL commonly used for exfiltration (verify the destination)
    Forwarding       https://a1b2…app -> http://loca…000
Medium and low: 7
  • medium Exfiltration exfil-webhook-url references/webhook-setup.md:463
    Webhook / callback URL commonly used for exfiltration (verify the destination) (quoted — discussed, not commanded)
    1. Copie a URL HTTPS do ngrok (ex: `https://a1b2…app`)
    quoted
  • medium Exfiltration exfil-webhook-url references/webhook-setup.md:464
    Webhook / callback URL commonly used for exfiltration (verify the destination) (quoted — discussed, not commanded)
    2. No painel da Meta, atualize o Callback URL para: `https://a1b2…app/webhook`
    quoted
  • low Secrets in code secret-high-entropy-token assets/boilerplate/nodejs/package-lock.json:73
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…xCA+ORZv…wO5/ywWF…Tag==",
    detector
  • low Secrets in code secret-high-entropy-token assets/boilerplate/nodejs/package-lock.json:199
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…Hra/JILC…zl8+mJRc…9PI+6Xbq…zjQ==",
    detector
  • low Secrets in code secret-high-entropy-token assets/boilerplate/nodejs/package-lock.json:257
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…GLw+xYSd…cqA==",
    detector
  • low Secrets in code secret-high-entropy-token assets/boilerplate/nodejs/package-lock.json:280
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…B4t+D+OBkj…bcE+mWTyvVV7D/zsdE…1Rw==",
    detector
  • low Secrets in code secret-high-entropy-token assets/boilerplate/nodejs/package-lock.json:294
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…MfZ+71RA…HvA==",
    detector

Files scanned: 29. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 77/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4421 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 45 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • low 35 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 151: enough signal without eating the budget
  • +4Structure: 37 headings
  • +3Step-by-step instructions: 45 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.