2. Your first continuation
What gets written
Section titled “What gets written”A continuation archives ranges of the session log as numbered Markdown chapters — verbatim text, generated
by the plugin copying the log. Where the ranges come from is the only thing that differs by route: a click
or a command (Continue with chapters, /chapters-continue, and their branch twins) derives them in the
plugin with zero model involvement, while the model’s own path proposes them from
chapters_segment. Either way the model never authors the body and 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.mdChapter 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
Section titled “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 chapterby its path with the read tool when the detail matters. The previous session stays intact.
## In flight<State of play — your handoff note>
## Chapters1. [Deeply analyze this project…](.dsh-chapters/<root>/chapters/001-….md) — <summary> (33 msgs)2. [Earlier history](.dsh-chapters/<root>/chapters/002-….md) — <summary> (18 msgs)
## Finding things- Chapter detail: open its path above with the read tool.- A deferred tool result: chapters_artifact (the path, or the bare sha a chapter cites) — action 'toc' | 'search' | 'read'.- Anything else on this project — other sessions' work, archived plans (`kind: plan`), curated rules: chapters_search.
Ancestry: root <root-id>; parent <parent-id>; archive under .dsh-chapters/.Two blocks above the fold may or may not appear: the CORE RULES this machine approved, and the operative plan — which only a continuation gets, because a branch is handed zero plan bytes. The three Finding things verbs are there because a tool the child was never told about does not exist to it. §18 annotates the real shape line by line.
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
Section titled “Retrieval is the model’s normal behavior”The chapter list plus “reload with read” is all the instruction needed in practice — and the notice now
names the other two verbs too (chapters_artifact for deferred blobs, chapters_search for everything on
the project), because a tool an agent was never told about does not exist for it. 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
Section titled “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
Section titled “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.