Productividad Skill Beta

Revela

Convierte capturas de Instagram, TikTok, X y LinkedIn en una biblioteca markdown estructurada. Detecta carruseles, agrupa diapositivas del mismo post, normaliza prompts a un formato XML portable, y etiqueta cada pieza por sectores declarados por el usuario, utilidad concreta, prioridad y esfuerzo. Los sectores los configura el usuario en runtime — nada está hardcodeado.

Instalación

Próximamente disponible. El comando de instalación se activa cuando publiquemos el repositorio público (objetivo: 30 utilidades curadas). ¿Acceso anticipado? Escribe a [email protected].

Extracts, translates and catalogs information from screenshots (typically social media posts) into a structured, queryable markdown library.

What it does

Processes folders of screenshots and produces a markdown catalogue with rich YAML metadata, ready to query with Obsidian + Dataview, filter by user-declared sectors, and feed editorial or knowledge-base pipelines.

When to use it

You collect Instagram/TikTok/X/LinkedIn captures of prompts, post ideas, tools, courses or flyers.
You want to stop losing them in a chaotic folder.
You want a queryable library tagged by your own sectors and priority.

How it works

Drop screenshots into revela/inbox/.
Trigger the utility (e.g. /revela in Claude Code, or natural language).
The agent groups carousels (multi-image posts), extracts text via vision, classifies, normalizes prompts and writes one .md per post.
Originals move to revela/ready/, generated markdown lives in revela/output/.
Indexes and a dashboard regenerate on every run.

Configuration

On first run, the agent asks which sectors apply to your context (e.g. personal, marketing, research, my-org). The answer goes into revela/.config.yaml and tags every catalogued piece. Sectors are never hardcoded — they’re always yours.

If you skip configuration, defaults are [work, personal, marketing, research].

Output schema

Every .md carries a YAML frontmatter with: identity, source, author + metrics, classification (type, sectors, actionable_as), decision (priority, effort, copy_risk, expires), connections (variables, tags, related, derived_idea). See SKILL.md for the full schema.

Compatibility

Tested with: Claude Code (primary), and any agent that supports vision and follows skill-style instruction loading. Mark compatible_with in utility.json per agent verified.

Limitations

Optimized for the user’s language being any common Latin-alphabet language. Right-to-left or CJK scripts are best-effort.
Carousel grouping relies on filename timestamps. If your capture tool strips timestamps, grouping falls back to author + visible pagination indicator.
Vision extraction quality scales with model capability. Use a model with strong OCR-on-screenshots performance.

License

MIT — see repo root.