CC investigacion-contenido-formula100k
Investigación de tendencias e ideas de contenido viral con la metodología FORMULA 100K. Usar SIEMPRE que se pida investigar tendencias, encontrar ideas de contenido, cazar temas virales, qué funciona en mi nicho, investigar a la competencia, encontrar ganchos visuales, validar ideas, preguntas de la audiencia, outliers de YouTube para convertir en reels, buscar referencias virales por keyword, o 'no sé de qué hablar', 'quiero ideas para mis videos', 'búscame referencias con [keyword]'. En un agente OpenClaw usa búsqueda web + Apify (datos públicos de Instagram y TikTok con vistas reales) + vidIQ si está conectado, nunca un navegador logueado. Solo reporta lo verificado.
Investigación de tendencias e ideas de contenido viral con la metodología FORMULA 100K.
As a process C 52/100 · 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.
- 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 ≈ 8017 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 52/100
- 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
- 40Execution cost. Instruction body is 8017 tokens: crowds the task out of the window
- 60Steps. 173 steps, 4 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 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)
- -5TODO / placeholder text left in the skill
- -223 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 678: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 173 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.