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conversation-json-to-md

conversation-json-to-md

Use when converting chat export JSON into per-conversation Markdown files with preserved user-assistant Q/A and normalized headings.

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name: conversation-json-to-md description: Use when converting chat export JSON into per-conversation Markdown files with preserved user-assistant Q/A and normalized headings.

Conversation JSON To MD

Overview

Convert a user-provided chat-export JSON into multiple Markdown files with consistent Q/A formatting. The workflow keeps only user-assistant exchanges, writes one conversation per Markdown file, preserves answer Markdown, and runs a second pass to normalize filenames and heading structure.

When to Use

Use this skill when the user asks for:

  • Splitting one JSON chat export into many .md files
  • One conversation per markdown file
  • Keeping only question/answer content from user and assistant
  • Renaming response sections to Answer
  • Normalizing exported files with a second formatting pass

Do not use

Do not use this skill for:

  • Plain text transformation that does not involve JSON chat exports
  • Non-conversation JSON processing tasks
  • Requests requiring semantic summarization instead of structural conversion

Instructions

  1. Read the input file path provided by the user. Do not assume default file names.
  2. Detect conversation/message structure automatically.
  3. Export one markdown file per conversation.
  4. Keep only user/assistant Q&A content.
  5. Format each Q/A block as:
    • ## <question text>
    • ### Answer
  6. Preserve answer markdown and demote answer-internal heading levels by one level.
  7. Run an independent second-pass formatting check and fix naming/title structure before final delivery.

Supported Input Structures

The bundled script supports common export formats including:

  • DeepSeek/ChatGPT-like mapping tree (mapping/root/children/fragments)
  • Qwen-like exports (data[].chat.messages[], content_list with phase=answer)
  • Claude web export style (list[{ name, chat_messages: [...] }])
  • Generic message arrays (messages, history, conversations, dialog, turns)
  • Pair fields (question-answer, prompt-response, input-output)

If format detection fails, stop and ask the user for a sample snippet, then extend parsing rules.

Run Script

python3 scripts/convert_conversations.py \
  --input /path/to/<user-provided>.json \
  --output-dir /path/to/output_md \
  --clean

Output Format

Each output file uses this structure:

# <conversation title>

## <user question 1>
### Answer
<assistant answer markdown>

## <user question 2>
### Answer
<assistant answer markdown>

Second-Pass Formatting (Required)

After export, run a second-pass check/fix on output files:

  1. Filename normalization:
  • Keep title-only naming
  • Remove illegal filename characters
  • Resolve duplicates with (2), (3)...
  1. Heading normalization:
  • Keep only one H1: # <conversation title>
  • Ensure questions are H2
  • Ensure responses are exactly ### Answer
  1. Body normalization:
  • Keep answer body markdown
  • Keep answer-internal heading demotion
  1. Final verification:
  • Confirm no files still violate naming or heading rules

Validation Checklist

  • File count equals detected conversation count
  • No random suffixes in filenames
  • No ## REQUEST or ## RESPONSE headers in output
  • Response blocks are present as ### Answer
  • Output preserves markdown rendering correctly
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