CC cazador-lead-magnets-f100k
Diseña LEAD MAGNETS DE ALTA TANGIBILIDAD (bases de datos con investigación real, miniapps, bots, custom GPTs, skills, audits) que generan comentarios masivos y deseo inmediato. Aplica el MÉTODO LMT — 3 capas (cazar el dolor latente, validar con reel-anzuelo, empaquetar tangible) + 1 filtro (se usa en menos de 1h). Activar SIEMPRE que se pida 'diseña un lead magnet', 'qué lead magnet hago', 'convierte esto en lead magnet', 'lead magnet que genere comentarios', 'lead magnet tangible', 'lead magnet con miniapp/bot/agente/base de datos', 'qué regalo previo a la venta', 'qué doy gratis para captar leads', 'lead magnet del dolor X', 'lead magnet para [nicho]'. Principio: mientras más tangible y usable al momento, más comentarios, deseo y leads de alta intención. Toma data real (comentarios, DMs, competencia) y devuelve briefing de producción. NO inventa dolores ni propone PDFs primero. NO usar para la OFERTA principal (constructor-ofertas-f100k) ni el VSL/landing (vsl-expert-f100k).
Diseña LEAD MAGNETS DE ALTA TANGIBILIDAD (bases de datos con investigación real, miniapps, bots, custom GPTs, skills, audits) que generan comentarios masivos…
As a process C 62/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 62/100
- 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
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 55 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3820 tokens
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
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)
- +3Description length 991: 120–800 characters recommended
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +4Structure: 27 headings
- +3Step-by-step instructions: 55 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.