CC investigacion-ads-formula100k
Investigación de ANUNCIOS PAGOS (paid ads) con la metodología FÓRMULA 100K + framework GOAT de Hormozi (Hook + Meat + CTA). Activar SIEMPRE que se pida 'investiga ads', 'busca ads de referencia', 'espía a la competencia en ads', 'biblioteca de anuncios de Meta', 'meta/facebook ads library', 'tiktok creative center', 'top ads', 'ads de [competidor]', 'qué anuncios corre X', 'dame 30 ideas de ads', '30-50 variantes de anuncios', 'guionízame este ad', 'variantes de un ad ganador', 'H x M x C', 'modulariza este ad'. NO confundir con investigacion-contenido-formula100k (contenido orgánico). Revisa la Biblioteca de Anuncios de Meta y el TikTok Creative Center en modo público con el navegador del agente (sin login) y Apify para lotes grandes, caza 30-50 ads, los desglosa con GOAT (Hook 80% / Meat 15% / CTA 5%), saca ángulos y ganchos ganadores, genera ideas propias y opcionalmente guioniza con H x M x C para variantes masivas.
Investigación de ANUNCIOS PAGOS (paid ads) con la metodología FÓRMULA 100K + framework GOAT de Hormozi (Hook + Meat + CTA).
As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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") - warning
body-longSKILL.md body ≈ 5645 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 58/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5645 tokens
- 100Steps. 67 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 12 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)
- +3Description length 933: 120–800 characters recommended
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +4Structure: 26 headings
- +3Step-by-step instructions: 67 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.