2. Your first continuation

What gets written

Every continuation archives the chosen ranges of the session log as numbered Markdown chapters — verbatim text, generated by the plugin copying the log (the model chooses ranges and titles; it never authors the body, and it is never asked to summarize). Plus a durable registry entry, and for oversized tool results, artifact files. On disk in your project:

.dsh-chapters/
  artifacts/8f/8f3a1c….txt      # content-addressed, deduplicated, shared across sessions
  <root-session-id>/
    chapters/001-deeply-analyze-this-project-and-describe-it-for-.md
    chapters/002-earlier-history.md

Chapter files are plain Markdown with YAML frontmatter — open them, grep them, commit them. They are not a proprietary format; the archive outlives the plugin.

What the new session opens with

The continuation is a brand-new session whose first message is a Table of Contents notice — annotated anatomy in §18; the condensed shape:

# Continuation: <title you/agent gave>

[core rules, if any are approved on this machine]

This session continues an archived conversation. Chapters are verbatim Markdown in the workspace;
every byte remains retrievable — inline, or at artifact paths cited inside a chapter. Read a chapter
by its path with the read tool when the detail matters. The previous session stays intact.

## In flight
<State of play — your handoff note>

## Chapters
1. [Deeply analyze this project…](.dsh-chapters/<root>/chapters/001-….md) — 33 user / 21 assistant messages (16,055 est tokens)
2. [Earlier history](.dsh-chapters/<root>/chapters/002-….md) — …

Ancestry: root <root-id>; parent <parent-id>; archive under .dsh-chapters/.

Measured size: ~102 tokens of index where the replaced transcript ran ~2,476 — and unlike a summary, every line is a path to verbatim text, not a paraphrase to trust.

Retrieval is the model’s normal behavior

The chapter list plus “reload with read” is all the instruction needed in practice: in a real 10-session production run, agents executed 49 archive retrievals — 49/49 successful, ~497 KB restored — including a child session re-consulting a 24K-token artifact three times after compaction had removed it from its context. Tool output deferral works the same way: huge results arrive as a stub with a chapters_artifact handle (toc → search → read windows) instead of eating the context.

The parent survives

Nothing about the continuation modifies the old session: its log is append-only, its prompt cache is intact, and you can keep working in it, delete it, or fork from it again (branching is legal — history is a DAG; the TOC flattens it for the model, chronologically, with explicit back-links).

The numbers version of this page

The same mechanics ran under fire in §20, the field study: 21 compactions across 10 sessions, zero inference tokens spent compacting, zero lost spans, every session closed clean.

Next: 3. Link a knowledge pool — turning one session’s memory into a team’s.