normalize-format

name

normalize-format

description

Normalizes a single inbox file of any supported format (plain markdown, Claude.ai JSON export, Claude Code JSONL session, Readwise markdown/CSV highlight, transcript with timestamps or speaker labels, link capture) into a clean markdown body plus partial frontmatter (id, title, source block, word_count). Handles format-specific failure modes — JSON content-block arrays, timestamp stripping, per-highlight chunking, URL-vs-commentary separation. Use when ingesting any inbox item for the substacker Librarian. Trigger keywords — normalize, convert, parse, transcript, export, JSON, JSONL, highlight, CSV.

Normalize Format

Table of Contents

• Supported formats
• Workflow
• Detection heuristics
• Worked example
• Guardrails

Related skills: Called by ingest-inbox-item as step 1. Upstream of tag-by-topic, score-intuition-density, dedupe-against-corpus.

Supported formats

Extension	Format	Notes
.md, .txt	plain markdown	Default; passes through
.json	Claude.ai export	Conversation with messages array
.jsonl	Claude Code session	Content-block array per response
.md (Readwise-shaped)	Readwise export	Highlights + user notes
.csv	Readwise CSV	Per-row highlight
.vtt, .srt, .md (diarized)	Transcript	May include timestamps + speaker labels
.md with URL + commentary	Link capture	User's framing is the signal

Workflow

Normalize one file:
- [ ] Step 1: Detect format by extension + first-line sniff
- [ ] Step 2: Apply format-specific parse
- [ ] Step 3: Split long transcripts at topic boundaries (>3000 words)
- [ ] Step 4: Emit [{body, partial_frontmatter}, ...] list (usually one item)

Step 1: Detection

• .jsonl with "type":"assistant" → Claude Code session.
• .json with "conversation" / "messages" top-level key → Claude.ai export.
• .md starting with # and Readwise boilerplate (**Highlights first synced by Readwise...**) → Readwise.
• .vtt / .srt, or .md with [HH:MM:SS] timestamp pattern, or lines prefixed with speaker labels like Me: → transcript.
• .md with ≤50 words and a prominent URL → link capture.
• Else: plain markdown.

Step 2: Format-specific parse

Plain markdown: pass body through unchanged. Title = first H1 or filename-derived.

Claude.ai JSON: flatten content blocks to markdown. Preserve user/assistant turn labels (**Me:** / **Claude:**). Strip system-reminder blocks. provenance.author: claude, confidence: paraphrased.

Claude Code JSONL: flatten content-block array. Drop tool_use blocks unless the adjacent user message references the tool output. Strip system reminders.

Readwise: split per-book file into one seed per highlight. Body = highlight + user note. Boilerplate stripped. For bare highlights (no user note), set provenance.confidence: quoted, density capped at 3 downstream. For user-annotated highlights, confidence: owned.

Transcript: strip timestamps. Preserve speaker labels as **Speaker:** prefixes. If >3000 words, split at topic shifts — emit multiple outputs sharing parent_source. Target ~1500 words per chunk.

Link capture: separate URL from commentary. Body = user's commentary. Frontmatter adds source.linked_url. If <50 words of commentary, flag low_commentary: true so the scorer caps density.

Step 3: Split long transcripts

Split heuristic: paragraph break + topic-vocabulary shift (measured by tag overlap drop across adjacent paragraphs). Each chunk ~1500 words. Preserve parent_source across chunks.

Detection heuristics

• A file that looks like a transcript but is actually an email thread (first-line sniff: From: ..., Date: ..., Subject: ...) — reclassify as plain markdown or link capture.
• A Readwise file missing boilerplate — treat as plain markdown.
• A .json file that isn't a Claude export — treat as plain markdown and wrap in code fences.

Worked example

Input (inbox/2026-04-21-claude-bnn.json):

{"conversation":{"name":"BNN variational","messages":[
  {"role":"user","content":[{"type":"text","text":"help me intuit why variational inference..."}]},
  {"role":"assistant","content":[{"type":"text","text":"Think of it as fitting a simple distribution..."}]}
]}}

Output:

# BNN variational

**Me:** help me intuit why variational inference...

**Claude:** Think of it as fitting a simple distribution...

With partial_frontmatter = {id: 2026-04-21-bnn-variational, title: "BNN variational", source: {type: claude-conversation, ...}, provenance: {author: claude, confidence: paraphrased}}.

Guardrails

1. Readwise CSV with malformed rows: skip the row, log WARN | malformed CSV row in <file> line N to changelog.
2. Claude.ai JSON schema drift: if messages key missing, fall back to recursive text extraction; mark confidence: paraphrased regardless.
3. Transcript that is actually an email thread: reclassify rather than keep as transcript.
4. Never OCR images. Image-only inbox items get a seed body of [image: awaiting user annotation] and status: dead with reason image-only.
5. Files >50k words: refuse and log. User splits manually.
6. Never lose the original. Even if parsing fails, the inbox file stays intact (caller moves to .processed/ only on success).

Quick reference

• Seven supported formats, each with a specific parser.
• Returns [{body, partial_frontmatter}, ...] — always a list, usually of length 1.
• Long transcripts split into multiple outputs sharing parent_source.