reduce

# /reduce

> Extract insights from source material into atomic, reusable claims.

## Purpose

Transform raw input (articles, transcripts, notes, documents) into structured atomic claims — each with provenance, confidence, and topic tags. This is the core extraction step: it turns noise into signal. Every claim stands alone and can be recombined later.

## Usage

```bash
# Reduce a file
/reduce path/to/transcript.md

# Reduce inline text
/reduce --text "Ed called about pricing. He wants $2K per seat for enterprise..."

# Reduce with explicit source metadata
/reduce path/to/article.md --source "HBR" --date 2026-03-15

# Reduce with depth control
/reduce path/to/paper.pdf --depth deep

# Reduce multiple files
/reduce path/to/*.md --batch
```

## Arguments

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `<input>` | positional | required | File path, glob pattern, or `--text` for inline |
| `--text` | string | — | Inline text to reduce (alternative to file input) |
| `--source` | string | auto-detect | Source attribution (author, publication, URL) |
| `--date` | date | today | Date of the source material |
| `--depth` | enum | `standard` | `quick` (key points only), `standard` (claims + context), `deep` (claims + evidence + counterpoints) |
| `--format` | enum | `atomic` | `atomic` (one claim per block), `outline` (hierarchical), `table` (comparison grid) |
| `--max-claims` | int | 50 | Maximum number of claims to extract |
| `--tags` | string[] | auto | Topic tags to apply (auto-detected if omitted) |
| `--batch` | flag | false | Process multiple files, one output per input |
| `--output` | path | stdout | Write to file instead of stdout |

## Workflow

1. **Ingest** — Read source material. Detect format (markdown, PDF, plain text, transcript). Extract metadata (title, author, date) if present.
2. **Segment** — Break source into logical sections. Identify speakers in transcripts. Detect topic shifts.
3. **Extract** — Pull atomic claims from each segment. Each claim is a single, falsifiable statement or actionable insight. No compound claims.
4. **Classify** — Tag each claim: `fact`, `opinion`, `decision`, `action-item`, `question`, `insight`. Assign confidence (0.0–1.0).
5. **Deduplicate** — Merge claims that say the same thing in different words. Keep the clearest phrasing.
6. **Enrich** — Add topic tags, link to related entities (people, projects, orgs). Note provenance (source + location in source).
7. **Structure** — Emit claims in the requested format with YAML frontmatter.

## Output

Each reduced file produces structured output:

```yaml
---
type: reduction
source: "path/to/original.md"
source_title: "Q1 Strategy Call with Ed"
source_date: 2026-03-15
reduced_at: 2026-03-20T14:30:00Z
claim_count: 12
topics: [pricing, enterprise, ai-masters]
---

## Claims

### 1. Enterprise pricing target is $2K/seat
- **type:** decision
- **confidence:** 0.9
- **speaker:** Ed Honour
- **context:** Discussed during pricing review segment
- **tags:** [pricing, enterprise, ai-masters]

### 2. Current conversion rate from free tier is 3.2%
- **type:** fact
- **confidence:** 0.7
- **speaker:** Roberto
- **context:** Referenced but not sourced — verify against analytics
- **tags:** [metrics, conversion, funnel]
```

## Dependencies

- `/seed` — Often receives input from seed (but can run standalone)
- `/reflect` — Output feeds into reflect for connection discovery
- File system read access to source material
- PDF parsing capability for `.pdf` inputs