session

name	session
version	1.0
description	Extract conversation turns from AI session history files (.jsonl)
entry	{"script":"scripts/main.py","class":"SessionSkill"}
triggers	{"extensions":[".jsonl"],"intents":["claude session","codex session","cursor session","ai session","session history"]}
requires	[]
author	axoviq.com
license	AGPL-3.0-or-later

Session Skill

Extracts human-readable conversation turns from AI coding session history files (.jsonl). Supports two formats:

• Claude Code — the JSONL format written by Anthropic's Claude Code CLI (~/.claude/projects/<hash>/<session-id>.jsonl)
• Codex / Cursor — the simpler {"role": ..., "content": ...} per-line format used by OpenAI Codex and Cursor IDE sessions

Format is detected automatically from the first parseable line.

What gets extracted

Only substantive conversation turns are kept:

Content type	Action
User text messages	Kept if ≥ 3 words
Assistant text responses	Kept if ≥ 20 words
Assistant thinking blocks	Skipped (internal reasoning, not final output)
Tool use / tool result blocks	Skipped (avoids leaking file contents or credentials)
Image / attachment blocks	Skipped
Sub-agent scaffolding (isSidechain: true)	Skipped (internal sub-agent turns)
Session metadata lines	Skipped (permission-mode, file-history-snapshot, system, last-prompt)

The extracted text is then passed through Synthadoc's standard pre-LLM source sanitizer (zero-width characters, bidi overrides, HTML comments, hidden CSS spans, base64 blobs, instruction-override phrases), exactly like PDF, DOCX, URL, and every other source type.

Output format

Each turn is labelled [USER] or [ASSISTANT] and separated by ---:

[USER]
How do I implement a sliding window algorithm?

---

[ASSISTANT]
A sliding window algorithm maintains a contiguous subarray (the "window") …

suggested_slug

The skill returns a suggested_slug in metadata derived from the session file's modification time and the first substantive user message:

session-2026-07-15-how-do-i-implement-a-sliding

Large sessions — chunking

Sessions longer than 30 substantive turns are split into 30-turn chunks. Each chunk is labelled with a ## Part N of M header so the downstream LLM can process sections independently. The metadata dict includes chunk_total when chunking occurs; single-chunk sessions (≤ 30 turns) are unchanged.

Limitations

• Tool output excluded — tool result blocks (shell output, file reads, etc.) are stripped. This is intentional: it avoids leaking file contents and credentials into the wiki.
• Format auto-detection — detection inspects the first 30 parseable lines. Corrupt or empty files produce an empty ExtractedContent.
• No deduplication across ingest runs — re-ingesting the same session file creates or updates the same wiki page (standard ingest dedup applies via source hash).

When this skill is used

• Source path ends with .jsonl
• Intent phrases: "claude session", "codex session", "cursor session", "ai session", "session history"

Standalone usage

import asyncio
from synthadoc.skills.session.scripts.main import SessionSkill

skill = SessionSkill()

async def main():
    result = await skill.extract("/path/to/session.jsonl")
    print(result.text)       # [USER]\n...\n\n---\n\n[ASSISTANT]\n...
    print(result.metadata)   # {"format": "claude_code", "turn_count": 42, "suggested_slug": "..."}

asyncio.run(main())