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CD conecta-tu-app-f100k

Guía paso a paso para conectar una app a lo que la vuelve un negocio: base de datos (Supabase), login (Google o enlace por correo), el PORTERO (solo entran los correos de quienes pagaron, validado contra Skool, Hotmart, Kajabi, Stripe, Mercado Pago o pagos a mano, sin que estén conectadas entre sí), cobros según el país, centro de control /admin con refresco de la lista, y publicación en Vercel. Claude hace lo técnico y pide solo lo que no puede hacer (crear cuentas, copiar llaves, clics en paneles). Trae plantillas probadas y prompts para copiar y pegar. Usar cuando digan "cómo conecto el login", "conecta Supabase", "base de datos para mi app", "que solo entren mis alumnos", "cómo valido que pagó", "que solo entre quien compró mi curso", "pasarela de pagos", "cómo cobro desde Colombia", "Stripe no está en mi país", "centro de control", "panel admin", "webhook de pagos" o "conecta mi app". NO construye la app desde cero (constructor-miniapps-f100k, crea-tu-app-f100k) ni landings de venta.

ClawHub Claude Code author: Fórmula 100K v1.0.0 MIT-0 26 files · 11 scripts body ≈ 2 745 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

Guía paso a paso para conectar una app a lo que la vuelve un negocio: base de datos (Supabase), login (Google o enlace por correo), el PORTERO (solo entran…

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSupabaseStripeTelegramAI and agentstype 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
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
73
Run on models
none yet
Process rating
D
41/100
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

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Exfiltration net-credential-use plantillas/app/api/webhooks/stripe/route.ts:22
    Credential used in a network call (verify the destination is the intended service)
    evento = stripe.webhooks.constructEvent(await req.text(), firma, process.env.STRIPE_WEBHOOK_SECRET!)

Files scanned: 26. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 41/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 85Steps. 37 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2745 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1003: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 13 example trigger phrases
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (9 of 9)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill is not malware, but it needs review because it can deploy to production and automatically change paid-member access, and its Stripe template is under-scoped.
LLM: suspicious (high) · 17 Sept 2026