Auto-Priming desde Documentos del Cliente

Every unprocessed document is untapped knowledge. Auto-priming transforms raw documents into structured, queryable knowledge that agents can use.

Purpose

Read all unprocessed documents in the working directory, extract structured knowledge, and generate RAG priming files that agents can reference during content generation.

Procedure

1. List all unprocessed documents (compare against calibration-digest.md to skip already-processed files).

2. For each document, determine type and extraction strategy:

   • PDF textbook: Extract unit structure, section patterns, activity types, vocabulary level, assessment formats
   • PDF curriculum: Extract destrezas, indicators, competencies, grade mapping
   • PDF inserts: Extract cross-cutting themes, integration patterns, frequency rules
   • HTML deliverable: Extract architecture decisions, agent specs, process flows, metrics
   • Image (taxonomy): Describe levels, verbs, structure visually observed
   • Word/text draft: Extract content, identify sections, note style patterns

3. For each document, generate a RAG priming file:

   ---
   source: {filename}
   type: {curriculum|textbook|deliverable|insert|taxonomy|draft}
   domain: {curriculo|editorial|pedagogia|neurociencia|proceso}
   processed_date: {ISO timestamp}
   confidence: {alta|media|baja}
   ---
   ## TL;DR
   {3-5 bullet summary}
   
   ## Key Entities
   {Names, systems, processes extracted}
   
   ## Constraints and Requirements
   {Explicit constraints found}
   
   ## Evidence
   {Key quotes or data points with [METODOLOGIA] tags}
   

4. Save priming files to references/priming-rag/rag-{domain}-{source-slug}.md

5. Update calibration-digest.md with new priming files and recalculated level.

6. Report: "N documentos procesados. N priming files generados. Nivel: {NUEVO}."

Inputs

Name	Source	Required
Unprocessed documents	Working directory	Yes
calibration-digest.md	Previous calibration	No

Outputs

Artifact	Format	Destination
RAG priming files	Markdown	references/priming-rag/
Updated calibration-digest.md	Markdown	Working directory

Acceptance Criteria

• Every readable document processed (PDFs via Read tool, limited to first 20 pages for large files)
• Each priming file has TL;DR, entities, constraints, evidence with tags
• Calibration level updated
• Zero content fabrication — only extract what's in the document

Output Format

# Auto-Prime Report — {TIMESTAMP}

## Documentos Procesados: {N}

| Archivo | Tipo | Dominio | Confianza | Priming File |
|---------|------|---------|-----------|--------------|
| {filename} | curriculum | curriculo | alta | rag-curriculo-{slug}.md |

## Nivel Actualizado: {COLD|WARM|HOT|DEEP}

## Nuevos Priming Files
- references/priming-rag/rag-{domain}-{slug}.md

## Archivos Omitidos
- {filename}: {razon}

Error Handling

• PDF too large to read: Process first 20 pages, note "PARTIAL" in priming file
• Unreadable format: Skip, log in calibration-digest as "SKIPPED: {reason}"
• Duplicate source: Update existing priming file instead of creating new one
• Binary files (images): Describe visually if image tool available, otherwise skip with note

Anti-Patterns

• Don't invent content not in the source document — use [SUPUESTO] for any inference
• Don't generate priming files for documents already processed (check digest first)
• Don't try to read binary files (images are described visually, not OCR'd)
• Don't generate a single monolithic priming file — one file per source document
• Don't skip the TL;DR section — it's the most-referenced part by downstream agents

References

• references/priming-extraction-patterns.md — Extraction strategies per document type