---
title: Project Ideas
description: Ideas for applying the patterns from this course to your own content processing projects.
---

# Project Ideas

The patterns in this course apply far beyond email newsletters. Here are ideas for applying them to your own work.

## The Patterns, Generalised

| Pattern                    | How to Apply                                                                                                      |
| -------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| **Context-Aware Research** | Ask your AI agent for recommendations within your project context. It sees your stack and gives tailored answers. |
| **Agents Writing Agents**  | Describe a specialized subagent in plain language; let AI write the definition.                                   |
| **Compounding Knowledge**  | Every research report and plan becomes context for the next layer.                                                |
| **Parallel Execution**     | Design agents to be stateless and idempotent — then spawn N of them.                                              |
| **Semantic Enrichment**    | Use LLMs to extract structured metadata from unstructured content.                                                |

## Content Processing Projects

### Migrate a Documentation Site

- **Source**: Old docs in various formats (MediaWiki, Confluence, Word docs)
- **Process**: Extract → convert to markdown → classify by topic/audience → cross-link
- **Patterns**: Extraction, taxonomy design, semantic enrichment

### Organize a Personal Knowledge Base

- **Source**: Years of notes, bookmarks, saved articles
- **Process**: Categorize by domain, extract key concepts, link related items
- **Patterns**: Classification, parallel processing, validation

### Build a Company Knowledge Graph

- **Source**: Internal wikis, meeting notes, project docs, emails
- **Process**: Extract entities (people, projects, technologies), link by relationships
- **Patterns**: Semantic enrichment, taxonomy design, cross-linking

### Curate a Research Paper Library

- **Source**: Downloaded PDFs, arXiv references, citation lists
- **Process**: Extract paper metadata, classify by field, identify citation networks
- **Patterns**: Extraction, classification, entity extraction

### Process Customer Feedback

- **Source**: Survey responses, support tickets, reviews
- **Process**: Categorize by topic/sentiment/urgency, extract mentioned features
- **Patterns**: Classification, parallel processing, semantic enrichment

### Archive and Index a Mailing List

- **Source**: Years of mailing list archives (mbox, EML)
- **Process**: Extract threads, identify key contributors, categorize discussions
- **Patterns**: Extraction (same EML pipeline!), classification, people extraction

## Agent Patterns to Try

### Hierarchical Classification

Instead of one pass, use a multi-stage classification:

```
Stage 1: Rough category (technical vs non-technical)
    ↓
Stage 2: Subcategory (language models, infrastructure, ethics)
    ↓
Stage 3: Detailed tags and entities
```

Each stage uses a different agent optimized for that level of granularity.

### Ensemble Classification

Run multiple classification agents on the same content and compare results. Disagreements highlight ambiguous cases that need human review.

### Review-and-Refine Loop

```
Classify → Review agent checks output → Flag issues → Re-classify flagged items
```

The review agent catches errors that the classification agent misses, creating a self-correcting loop.

### Incremental Classification

Process new content as it arrives rather than in batches. The classification agent runs on each new item, using existing data for context:

```
New file → Classify → Compare with existing → Flag outliers → Add to collection
```

### Multi-Model Enrichment

Use different models for different enrichment tasks:

| Task                       | Model  | Why                               |
| -------------------------- | ------ | --------------------------------- |
| Entity extraction          | Haiku  | Fast, good at pattern recognition |
| Topic classification       | Sonnet | Balanced speed and accuracy       |
| Content quality assessment | Opus   | Deep understanding needed         |
| Tag generation             | Haiku  | Quick keyword extraction          |

## Build Your Own Pipeline

A generic content pipeline skeleton:

```
source-files/
    ↓
1. Research phase
    - Agent analyzes sample → recommends approach
    - Plan written to plans/
    ↓
2. Extraction phase
    - Converter script (built by agent)
    - Output: content/raw/
    ↓
3. Taxonomy phase
    - Agent reads all content → proposes schema
    - Schema saved to reports/
    ↓
4. Classification phase
    - Classification agent (built by agent)
    - Parallel processing: content/enriched/
    ↓
5. Validation phase
    - Validation scripts (built by agent)
    - Progress tracker (built by agent)
    ↓
6. Application phase
    - Generate JSON data
    - Build website/search/dashboard
```

## Pro Tips

1. **Start with a small sample** (5-10 files) to validate your approach before scaling to hundreds
2. **Write CLAUDE.md early** — it compounds across sessions
3. **Save all research to reports/** — don't let findings live only in chat history
4. **Use the plain text version** of emails when available — often already markdown
5. **Make agents stateless** — design for parallelism from the start
6. **Build validation scripts incrementally** — extract them when you see redundant work
7. **Re-run the full pipeline** after schema changes — don't patch individually

---

**Back to [Course Overview](/content-repurpose/)** | **Start with [Phase 1: Setting Up](/content-repurpose/01-research/)**