CD caso-estudio-contenido-f100k
Reto final del módulo de Creación de Contenido de FÓRMULA 100K: corre el pipeline completo sobre el nicho de una alumna y entrega una PÁGINA DE CASO DE ESTUDIO (HTML de una sola página) lista para subir a Netlify/Vercel como portafolio, más el calendario en CSV. Activar SIEMPRE que se pida 'quiero hacer el reto final', 'crea mi caso de estudio de contenido', 'arma mi calendario + investigación del reto', 'el reto del módulo de contenido', 'haz mi caso de estudio', o cuando se invoque /caso-estudio. Investiga referencias virales reales, arma guiones, ganchos y calendario de 30 días con un checkpoint humano en medio, suma el plan de mes 2 y maqueta el HTML final. NO confundir con calendarizador-contenido (solo el calendario) ni con MAESTRO (semana completa con colocación en Yapper/Bento).
Reto final del módulo de Creación de Contenido de FÓRMULA 100K: corre el pipeline completo sobre el nicho de una alumna y entrega una PÁGINA DE CASO DE…
As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 2. 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 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 (python) that frontmatter does not declare
- 85Steps. 35 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2661 tokens
- low The response is described with custom markup (7 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)
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 797: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.